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Agent trace

Production two-tower improvement sol-0.153.4-high-05

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

Thinking text is unavailable for this run. The archive contains 86 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · high effort
Recorded conversation
58:54 · 92 steps
Private reward
0.021477 · Pass
Attempt cost
$4.59
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.

Personality

As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend.

You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique.

Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them.

Writing style

Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable.

If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering.

Technical communication

Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice.

You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details.

Working with the user

You have two channels for staying in conversation with the user:

  • You share updates in the commentary channel.
  • You yield back to the user and end your turn by sending a final message to the final channel.

The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task.

When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events.

Intermediate commentary

As you work, you send messages to the commentary channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify.

If the user's request requires calling tools, start with a message in the commentary channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work.

Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users.

Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do <this good thing> rather than <this obviously bad thing>", "I will do <X>, not <Y>".

Final answer

In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary.

Formatting rules

Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:

  • You may format with GitHub-flavored Markdown.
  • When referencing a real local file, prefer a clickable markdown link.
  • Clickable file links should look like app.py: plain label, absolute target, with optional line number inside the target.
  • If a file path has spaces, wrap the target in angle brackets: [My Report.md](</abs/path/My Project/My Report.md:3>).
  • Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.
  • Do not use URIs like file://, vscode://, or https:// for file links.
  • Do not provide ranges of lines.
  • Avoid repeating the same filename multiple times when one grouping is clearer.
Visualizations

Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps.

Good candidates include:

  • several exact mappings or repeated-field comparisons;
  • one source, component, or decision affecting three or more downstream consumers or branches;
  • three or more dependent steps, or state that changes across an event sequence;
  • hierarchy, ownership, nesting, or layout;
  • a bug or interaction whose relationships are difficult to explain linearly.

Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout.

Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations.

Rules for getting work done

  • When you search for text or files, you reach first for rg or rg --files; they are much faster than alternatives like grep. If rg is unavailable, you use the next best tool without fuss.
  • When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.
  • Do not chain shell commands with separators like echo "===="; or printf '---'; the output becomes noisy in a way that makes the user's side of the conversation worse.
  • Exercise caution when escaping text for exec_command calls - backticks and $() passed to the cmd argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.
  • Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.
  • When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.

File editing constraints

Use apply_patch for local file edits. Do not create or edit files with cat or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need apply_patch. Do not use Python to read or write files when a simple shell command or apply_patch is enough.

You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user.

Never use destructive commands like git reset --hard or git checkout -- unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands.

Autonomy and persistence

Adapt accordingly based on the user’s request type. When asked to:

  • Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant.
  • Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation.
  • Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains.
  • Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker.

You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications).

A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives.

You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user.

When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront.

If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission.

Destructive actions

Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.

Before taking a destructive action:

  • Make sure the action is clearly within the user's request.
  • Resolve the exact targets with read-only checks when necessary.
  • Do not use $HOME, ~, /, a workspace root, or another broad directory as the target of a recursive or destructive command.
  • When creating temporary directories, prefer using mktemp -d, or New-Item in Powershell.
  • When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.
  • When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.
  • Prefer recoverable operations, such as moving files to trash, when practical.
  • If the target or scope is unclear, stop and ask the user.

Never run commands such as rm -rf $HOME or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.

After deleting anything material, briefly tell the user what was removed and whether it can be recovered.

Using skills

A skill is a set of instructions provided through a SKILL.md source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.

How to use skills
  • Discovery: When a ## Skills section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its SKILL.md. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as r0 to their filesystem roots. Expand the alias before accessing the skill.
  • Trigger rules: If the user names an available skill (with $SkillName or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
  • Missing/blocked: If a named skill is not available or its SKILL.md cannot be read, say so briefly and continue with the best fallback.
  • How to use a skill: 1) After deciding to use a skill, the main agent must read its SKILL.md completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from ### Skill roots, then open and read its SKILL.md completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call skills.list with {"authority":{"kind":"orchestrator"}}, select the matching package, and pass its main_resource to skills.read. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When SKILL.md references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed SKILL.md. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to skills.read; do not treat skill:// identifiers as filesystem paths. 3) If SKILL.md points to extra folders such as references/, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if scripts/ exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use skills.read and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if assets/ or templates exist).
  • Coordination and sequencing:
  • If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  • Announce which skills you're using and why. If you skip an obvious skill, say why.
  • Context hygiene:
  • Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.
  • Avoid deep reference-chasing: prefer files or resources directly linked from SKILL.md unless blocked.
  • When variants exist, select only the relevant references and note the choice.
  • Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.

When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill.

Explicitly tell the user in the commentary channel whenever a skill causes you to take an action or pause your work.

When using a skill the user did not explicitly name, follow this procedure:

  • First, tell the user in the commentary channel why you are using the skill.
  • Then, use the skill as long as it stays within the scope of the task.
  • Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response).

If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected.

02

Developer instructions

+00:01
Developer instructions

<skills_instructions>

Skills

A skill is a set of local instructions to follow that is stored in a SKILL.md file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.

Skill roots
  • r0 = /tmp/codex-home/skills/.system
Available skills
  • imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)
  • openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)
  • plugin-creator: Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)
  • skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)
  • skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) </skills_instructions>

<permissions instructions> Filesystem sandboxing defines which files can be read or written. sandbox_mode is danger-full-access: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the sandbox_permissions for any reason, commands will be rejected. </permissions instructions>

<apps_instructions>

Apps (Connectors)

Apps (Connectors) can be explicitly triggered in user messages in the format [$app-name](app://{connector_id}). Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the codex_apps MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the tool_search tool. If tool_search is available, the apps that are searchable by tools_search will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. </apps_instructions>

03

Developer instructions

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Developer instructions

You are /root, the primary agent in a team of agents collaborating to fulfill the user's goals.

At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.

You can use spawn_agent to create a new agent, followup_task to give an existing agent a new task and trigger a turn, and send_message to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the fork_turns parameter.

You will receive messages in the analysis channel in the form:

Message Type: MESSAGE | FINAL_ANSWER
Task name: <recipient>
Sender: <author>
Payload:
<payload text>

They may be addressed as to=/root

Note that collaboration tools cannot be called from inside functions.exec. Call spawn_agent, send_message, followup_task, wait_agent, interrupt_agent, and list_agents only as direct tool calls using the recipient shown in their tool definitions, such as to=functions.collaboration.spawn_agent, since they are intentionally absent from the functions.exec tools.* namespace. Available tools in functions.exec are explicitly described with a tools namespace in the developer message.

All agents share the same directory. In detail:

  • All agents have access to the same container and filesystem as you.
  • All agents use the same current working directory.
  • As a result, edits made by one agent are immediately visible to all other agents.

When calling wait_agent, prefer longer waits (minutes) to avoid busy polling.

There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.

Full-history forks (fork_turns omitted or "all") inherit the parent model and reasoning effort and do not accept overrides. Only set model or reasoning_effort when explicitly requested by the user, applicable AGENTS.md instructions, or skill instructions; when doing so, set fork_turns to "none" or a positive integer string.

04

Developer instructions

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Developer instructions

<multi_agent_mode>Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work.</multi_agent_mode>

05

User context

+00:01
User context

<recommended_plugins> Here is a list of plugins that are available but not installed.

  • Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)
  • Box (box@openai-curated-remote)
  • Codex Security (codex-security@openai-curated-remote)
  • Figma (figma@openai-curated-remote)
  • GitHub (github@openai-curated-remote)
  • Gmail (gmail@openai-curated-remote)
  • Google Calendar (google-calendar@openai-curated-remote)
  • Google Drive (google-drive@openai-curated-remote)
  • Linear (linear@openai-curated-remote)
  • Notion (notion@openai-curated-remote)
  • OpenAI Developers (openai-developers@openai-curated-remote)
  • Outlook Calendar (outlook-calendar@openai-curated-remote)
  • Outlook Email (outlook-email@openai-curated-remote)
  • SharePoint (sharepoint@openai-curated-remote)
  • Slack (slack@openai-curated-remote)
  • Teams (teams@openai-curated-remote) </recommended_plugins>

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-23</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state
{
  "type": "world_state",
  "full": true,
  "state": {
    "agents_md": {},
    "apps_instructions": true,
    "collaboration_mode": {
      "mode": "default",
      "model": "gpt-5.6-sol",
      "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d"
    },
    "context_window_guidance": "",
    "environments": {
      "environments": {
        "local": {
          "cwd": "/app",
          "status": "available",
          "shell": "sh"
        }
      },
      "current_date": "2026-09-23",
      "timezone": "Etc/UTC",
      "filesystem": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-23",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "high",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "high",
  "summary": "auto"
}
06

Task instructions

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Task instructions

Improve a production music-discovery two-tower

You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate.

Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis.

The source used to reproduce the production model is included under /task/current_model/. You may copy it into /app/solution/, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact.

Production Context

The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table.

The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above.

What You Have

All public inputs are under /task/data/. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is 24786800, when train_* ends. A 30-minute gap precedes the seven-day val_* window. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window.

The package includes:

  • /task/current_model/train_current_model.py: the production training and ranking script. It can reproduce the public validation ranking and produce a hidden-test submission after refitting.
  • /task/current_model/two_tower_model.py: model primitives used by the production script.
  • /task/evaluate_public.py: the public validation evaluator for development.
  • /task/data/validation_eligible_candidates.npz and /task/data/test_eligible_candidates.npz: platform-owned candidate sets for the validation and hidden-test cutoffs.
  • /task/data/validation_preference_state.parquet and /task/data/test_preference_state.parquet: active explicit preference state at the corresponding cutoffs.
  • /task/requirements.txt: the exact offline Python distribution inventory.

You can reproduce the production public backtest with:

python /task/current_model/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet

Data

For each event family there is a train_*.parquet and val_*.parquet file:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, timestamp, item_id, is_organic

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

  1. For model development and selection, train only from train_* and score the subsequent val_* outcomes.
  2. After selecting an improvement, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

  • candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
  • target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
  • validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
  • test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
  • validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

Validation candidates exclude all train_* encounters. Test candidates exclude all train_* and val_* encounters. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery Objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

  • grade 3: the user organically likes the track;
  • grade 1: the user organically listens to at least 80% of the track;
  • grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this same objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities; treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Deliverable

Submit one final ranking as /app/solution/submission.parquet or /app/solution/submission.csv. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates submission.parquet and ignores submission.csv.

The ranking must:

  • include the columns uid, item_id, and rank; only these columns are evaluated;
  • use finite, integer-valued entries in all three required columns;
  • cover exactly the users in target_users.parquet, with no missing or extra users;
  • contain exactly 100 rows and 100 distinct items for every target user;
  • contain every integer rank from 1 through 100 exactly once per user; and
  • use only items present in that user's test_eligible_candidates.npz candidate set.

Also leave the improved implementation used to build the artifact at /app/solution/train_current_model.py, along with any local modules it imports. A concise /app/solution/README.md with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model-provider connection managed by Harbor.
  • Do not alter cutoff definitions, target users, candidate eligibility, or relevance labels.
  • Improve the production pipeline in place. Do not substitute an unrelated model family or evade the indexed two-tower serving constraint.
  • The environment includes CPU PyTorch 2.8.0. No accelerator is available.
  • You have up to 60 minutes. Prioritize measured validation improvements, a correct final refit, and a valid submission over broad architecture search.
  • Keep the best valid submission saved throughout the attempt so it survives the deadline.
07

exec command

+00:03
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

I’ll reproduce the baseline first, then inspect the objective and data distributions—especially how negatives are sampled—before testing targeted changes on the public backtest. I’ll keep a valid best artifact under /app/solution/ as soon as possible, then refit the selected approach on train+validation.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"mkdir -p /app/solution && cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":6000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"532720","wall_time_seconds":0.046481147,"exit_code":1,"original_token_count":43,"output":"Traceback (most recent call last):\n  File \"/app/solution/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44728425887d2bac28596a5ff77d8",
  "usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 197,
    "reasoning_output_tokens": 20,
    "total_tokens": 15097
  },
  "turn_token_usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 197,
    "reasoning_output_tokens": 20,
    "total_tokens": 15097
  },
  "thread_token_usage": {
    "input_tokens": 14900,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 197,
    "reasoning_output_tokens": 20,
    "total_tokens": 15097
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e456140e-6e73-434c-a3e2-e100b397025c",
    "process_id": "38042",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/app/solution/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/app/solution/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 46445165
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/app/solution/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"
  }
}
08

exec command

+00:11
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The default python on this shell is not the supplied offline environment (it lacks NumPy), so I’m locating the packaged interpreter before changing any model code. The copied source is intact.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,320p' /app/solution/two_tower_model.py && find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -40 && ls -la /task && cat /task/requirements.txt","workdir":"/app","yield_time_ms":1000,"max_output_tokens":30000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:

#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray
    packed: np.ndarray

    @classmethod
    def read(cls, path: Path) -> "CandidateSet":
        with np.load(path, allow_pickle=False) as artifact:
            result = cls(
                uids=artifact["uids"].astype(np.int64, copy=True),
                item_ids=artifact["item_ids"].astype(np.int64, copy=True),
                packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
            )
        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
        if result.packed.shape != expected:
            raise ValueError(
                f"invalid candidate artifact shape {result.packed.shape}; "
                f"expected {expected}"
            )
        return result


@dataclass(frozen=True)
class SequenceData:
    item_ids: np.ndarray
    item_features: np.ndarray
    user_ids: np.ndarray
    offsets: np.ndarray
    sequence_timestamps: np.ndarray
    sequence_items: np.ndarray
    event_features: np.ndarray
    activity_features: np.ndarray
    state_ids: np.ndarray
    liked_states: np.ndarray
    disliked_states: np.ndarray
    state_counts: np.ndarray
    inference_user_rows: np.ndarray
    inference_liked_items: np.ndarray
    inference_disliked_items: np.ndarray
    inference_user_features: np.ndarray

    @property
    def lengths(self) -> np.ndarray:
        return np.diff(self.offsets)

    @property
    def target_count(self) -> int:
        return int(np.maximum(self.lengths - 1, 0).sum())


@dataclass(frozen=True)
class TargetPartition:
    """A contiguous recent target window and its chronological BCE split."""

    window_starts: np.ndarray
    training_counts: np.ndarray
    validation_counts: np.ndarray
    history_training_counts: np.ndarray

    @property
    def selected_count(self) -> int:
        return int(self.training_counts.sum() + self.validation_counts.sum())


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


def model_config(config: Config) -> base.TowerConfig:
    """Map the dense trainer configuration onto the unchanged tower."""
    return base.TowerConfig(
        sequence_length=config.bptt_steps,
        embedding_dim=config.embedding_dim,
        hidden_dim=config.hidden_dim,
        user_hidden_1=config.user_hidden_1,
        user_hidden_2=config.user_hidden_2,
        item_hidden_1=config.item_hidden_1,
        item_hidden_2=config.item_hidden_2,
        dropout=config.dropout,
        epochs=config.epochs,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        gradient_clip=config.gradient_clip,
        uniform_negatives=config.uniform_negatives,
        inbatch_negatives=config.inbatch_negatives,
        inbatch_correction_power=config.inbatch_correction_power,
        inbatch_correction_min=config.inbatch_correction_min,
        inbatch_correction_max=config.inbatch_correction_max,
        seed=config.seed,
    )


def append_state_snapshot(
    active_likes: dict[int, int],
    active_dislikes: dict[int, int],
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> int:
    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
    disliked_states.append(
        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
    )
    state_counts.append((len(active_likes), len(active_dislikes)))
    return len(liked_states) - 1


def causal_activity_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    organic: np.ndarray,
    completion: np.ndarray,
) -> np.ndarray:
    """Compute the first four reviewed user features at every event cutoff."""
    length = len(items)
    result = np.zeros((length, 4), dtype=np.float16)
    if not length:
        return result
    left_rows = np.searchsorted(
        timestamps,
        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
        side="left",
    )
    rows = np.arange(length, dtype=np.int64)
    counts = rows - left_rows + 1
    organic_sum = np.cumsum(organic.astype(np.float64))
    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
    before_organic = np.where(
        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
    )
    before_completion = np.where(
        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
    )

    unique_counts = np.empty(length, dtype=np.int32)
    frequencies: dict[int, int] = {}
    left = 0
    for right, raw_item in enumerate(items):
        item = int(raw_item)
        frequencies[item] = frequencies.get(item, 0) + 1
        required_left = int(left_rows[right])
        while left < required_left:
            old_item = int(items[left])
            remaining = frequencies[old_item] - 1
            if remaining:
                frequencies[old_item] = remaining
            else:
                del frequencies[old_item]
            left += 1
        unique_counts[right] = len(frequencies)

    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
    result[:, 2] = (
        (organic_sum - before_organic) / np.maximum(counts, 1)
    ).astype(np.float16)
    result[:, 3] = (
        (completion_sum - before_completion)
        / np.maximum(counts, 1)
        / 100.0
    ).astype(np.float16)
    return result


def build_sequence_parts(
    listens: base.ListenEvents,
    explicit: base.ExplicitEvents,
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
    items = listens.items[qualifying].astype(np.uint16, copy=False)
    organic = listens.organic[qualifying].astype(np.float32, copy=False)
    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
    if not len(items):
"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations

import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator

import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn


DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100


@dataclass(frozen=True)
class TowerConfig:
    sequence_length: int = 100
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    seed: int = 29


@dataclass(frozen=True)
class ListenEvents:
    timestamps: np.ndarray
    items: np.ndarray
    organic: np.ndarray
    completion_pct: np.ndarray


@dataclass(frozen=True)
class ExplicitEvents:
    timestamps: np.ndarray
    items: np.ndarray
    channel: np.ndarray
    active: np.ndarray
    organic_like: np.ndarray


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
    """Return four standardized features built only from the public catalog."""
    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
    features = np.column_stack(
        [
            np.log1p(positives),
            np.log1p(organic),
            organic / np.maximum(positives, 1.0),
            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
        ]
    )
    means = features.mean(axis=0, keepdims=True)
    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
    standardized = ((features - means) / scales).astype(np.float32)
    if not np.isfinite(standardized).all():
        raise RuntimeError("catalog features contain non-finite values")
    return standardized


def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
    if len(parts) == 1:
        return parts[0]
    timestamps = np.concatenate([part.timestamps for part in parts])
    items = np.concatenate([part.items for part in parts])
    organic = np.concatenate([part.organic for part in parts])
    completion = np.concatenate([part.completion_pct for part in parts])
    order = np.argsort(timestamps, kind="stable")
    return ListenEvents(
        timestamps=timestamps[order],
        items=items[order],
        organic=organic[order],
        completion_pct=completion[order],
    )


def iter_listen_users(
    path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Stream one uid-sorted public listen file one user at a time."""
    parquet = pq.ParquetFile(path)
    pending_uid: int | None = None
    pending: list[ListenEvents] = []
    previous_uid = -1
    for batch in parquet.iter_batches(
        batch_size=750_000,
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
    ):
        frame = batch.to_pandas()
        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
        keep = mapped >= 0
        if not np.any(keep):
            continue
        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
        items = mapped[keep].astype(np.int32, copy=False)
        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
        if np.any(uids[1:] < uids[:-1]):
            raise RuntimeError(f"{path.name} is not sorted by uid")
        boundaries = np.r_[
            0,
            np.flatnonzero(uids[1:] != uids[:-1]) + 1,
            len(uids),
        ]
        for start, end in zip(boundaries[:-1], boundaries[1:]):
            uid = int(uids[start])
            part = ListenEvents(
                timestamps[start:end].copy(),
                items[start:end].copy(),
                organic[start:end].copy(),
                completion[start:end].copy(),
            )
            if pending_uid is None:
                pending_uid = uid
            elif uid != pending_uid:
                if pending_uid < previous_uid:
                    raise RuntimeError(f"{path.name} user order regressed")
                yield pending_uid, _combine_listen_chunks(pending)
                previous_uid = pending_uid
                pending_uid = uid
                pending = []
            pending.append(part)
    if pending_uid is not None:
        yield pending_uid, _combine_listen_chunks(pending)


def merge_listen_streams(
    paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Merge uid-sorted public listen files without materializing them."""
    streams = [iter(iter_listen_users(path, item_index)) for path in paths]
    heads: list[tuple[int, ListenEvents] | None] = [
        next(stream, None) for stream in streams
    ]
    while any(head is not None for head in heads):
        uid = min(head[0] for head in heads if head is not None)
        parts: list[ListenEvents] = []
        for position, head in enumerate(heads):
            if head is not None and head[0] == uid:
                parts.append(head[1])
                heads[position] = next(streams[position], None)
        yield uid, _combine_listen_chunks(parts)


def load_explicit_events(
    data: Path,
    prefixes: tuple[str, ...],
    item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
    """Load public explicit actions as chronological state changes."""
    specifications = (
        ("likes", 0, 1, 0),
        ("unlikes", 0, 0, 1),
        ("dislikes", 1, 1, 0),
        ("undislikes", 1, 0, 1),
    )
    parts: list[pd.DataFrame] = []
    for family, channel, active, tie_priority in specifications:
        for prefix in prefixes:
            frame = pd.read_parquet(
                data / f"{prefix}_{family}.parquet",
                columns=["uid", "timestamp", "item_id", "is_organic"],
            )
            mapped = item_index.get_indexer(
                frame.item_id.to_numpy(dtype=np.int64, copy=False)
            )
            keep = mapped >= 0
            if not np.any(keep):
                continue
            retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
            retained["item"] = mapped[keep].astype(np.int32, copy=False)
            retained["channel"] = np.int8(channel)
            retained["active"] = np.int8(active)
            retained["tie_priority"] = np.int8(tie_priority)
            retained["organic_like"] = np.int8(
                family == "likes"
            ) * retained.is_organic.astype(np.int8)
            parts.append(retained.drop(columns="is_organic"))
    if not parts:
        return {}
    events = pd.concat(parts, ignore_index=True)
    events = events.sort_values(
        ["uid", "timestamp", "tie_priority"], kind="mergesort"
    ).reset_index(drop=True)
    result: dict[int, ExplicitEvents] = {}
    for uid, rows in events.groupby("uid", sort=False):
        result[int(uid)] = ExplicitEvents(
            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
            items=rows.item.to_numpy(dtype=np.int32, copy=True),
            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
            active=rows.active.to_numpy(dtype=np.int8, copy=True),
            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
        )
    log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
    return result


def empty_explicit_events() -> ExplicitEvents:
    return ExplicitEvents(
        timestamps=np.empty(0, dtype=np.int64),
        items=np.empty(0, dtype=np.int32),
        channel=np.empty(0, dtype=np.int8),
        active=np.empty(0, dtype=np.int8),
        organic_like=np.empty(0, dtype=np.int8),
    )


def pack_active_items(
    active: dict[int, int], width: int, padding_item: int
) -> np.ndarray:
    packed = np.full(width, padding_item, dtype=np.uint16)
    if not active:
        return packed
    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
    packed[: len(ordered)] = np.fromiter(
        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
    )
    return packed


def load_inference_state(
    path: Path,
    item_index: pd.Index,
    uids: np.ndarray,
    n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Pack the task-provided active explicit state."""
    state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
    keep = mapped >= 0
    state = state.loc[keep, ["uid", "state"]].copy()
    state["item"] = mapped[keep].astype(np.int32, copy=False)
    user_rows = pd.Index(uids)
    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
    like_counts = np.zeros(len(uids), dtype=np.int32)
    dislike_counts = np.zeros(len(uids), dtype=np.int32)
    for value, width, destination, counts in (
        ("liked", LIKE_ITEMS, liked, like_counts),
        ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
    ):
        selected = state[state.state == value]
        for uid, rows in selected.groupby("uid", sort=False):
            row = int(user_rows.get_indexer([int(uid)])[0])
            if row < 0:
                continue
            items = np.sort(rows.item.unique())
            counts[row] = len(items)
            retained = items[:width]
            destination[row, : len(retained)] = retained.astype(np.uint16)
    return liked, disliked, like_counts, dislike_counts


class SequentialTwoTower(nn.Module):
    """Order-sensitive user tower and independent catalog-aware item tower."""

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
            input_size=config.hidden_dim,
            hidden_size=dim,
            num_layers=1,
            batch_first=True,
        )
        self.user_tower = nn.Sequential(
            nn.Linear(dim * 3 + 6, config.user_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_1, config.user_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_2, dim),
        )
        self.candidate_embedding = nn.Embedding(n_items, dim)
        self.register_buffer(
            "item_features", torch.from_numpy(item_features), persistent=True
        )
        self.item_tower = nn.Sequential(
            nn.Linear(dim + n_item_features, config.item_hidden_1),
            nn.GELU(),
total 40
dr-xr-xr-x 1 root root 4096 Sep 23 21:37 .
drwxr-xr-x 1 root root 4096 Sep 23 21:39 ..
-r--r--r-- 1 root root    0 Sep 23 21:34 .keep
dr-xr-xr-x 1 root root 4096 Sep 23 21:34 current_model
dr-xr-xr-x 1 root root 4096 Sep 23 21:35 data
-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 23 21:34 requirements.txt
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Harness context token usage record
{
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44730bf4487d2a629f97913b67261",
  "usage": {
    "input_tokens": 15200,
    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 197,
    "reasoning_output_tokens": 21,
    "total_tokens": 15397
  },
  "turn_token_usage": {
    "input_tokens": 30100,
    "cached_input_tokens": 22272,
    "cache_write_input_tokens": 0,
    "output_tokens": 394,
    "reasoning_output_tokens": 41,
    "total_tokens": 30494
  },
  "thread_token_usage": {
    "input_tokens": 30100,
    "cached_input_tokens": 22272,
    "cache_write_input_tokens": 0,
    "output_tokens": 394,
    "reasoning_output_tokens": 41,
    "total_tokens": 30494
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fb95d853-3ff6-46e9-82d7-d66f33cbb7f9",
    "process_id": "57126",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,320p' /app/solution/two_tower_model.py && find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -40 && ls -la /task && cat /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,260p' /app/solution/train_current_model.py && sed -n '1,320p' /app/solution/two_tower_model.py && find / -maxdepth 4 -type f \\( -name python -o -name python3 \\) 2>/dev/null | head -40 && ls -la /task && cat /task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 23 21:37 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\n-r--r--r-- 1 root root    0 Sep 23 21:34 .keep\ndr-xr-xr-x 1 root root 4096 Sep 23 21:34 current_model\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 23 21:34 requirements.txt\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 23 21:37 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\n-r--r--r-- 1 root root    0 Sep 23 21:34 .keep\ndr-xr-xr-x 1 root root 4096 Sep 23 21:34 current_model\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 23 21:34 requirements.txt\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1780
    },
    "formatted_output": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 23 21:37 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\n-r--r--r-- 1 root root    0 Sep 23 21:34 .keep\ndr-xr-xr-x 1 root root 4096 Sep 23 21:34 current_model\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 23 21:34 requirements.txt\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
  }
}
09

exec command

+00:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"which -a python python3 uv || true\nls -la /app /opt /usr/local/bin | head -100\nfind / -type f -path '*/bin/python*' 2>/dev/null | head -40\nfind / -type l -path '*/bin/python*' 2>/dev/null | head -40","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000}); text(r.output)

Tool response 

Script completed
Wall time 0.4 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/app:
total 16
drwxr-xr-x 1 agent agent 4096 Sep 23 21:37 .
drwxr-xr-x 1 root  root  4096 Sep 23 21:39 ..
drwxr-xr-x 1 agent agent 4096 Sep 23 21:40 solution

/opt:
total 16
drwxr-xr-x 1 root root 4096 Sep 23 21:36 .
drwxr-xr-x 1 root root 4096 Sep 23 21:39 ..
drwxr-xr-x 4 root root 4096 Sep 23 21:36 codex-home
dr-xr-xr-x 1 root root 4096 Sep 23 21:36 task-venv

/usr/local/bin:
total 48
drwxr-xr-x 1 root root  4096 Sep 23 21:36 .
drwxr-xr-x 1 root root  4096 Aug  3 00:00 ..
lrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12
-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12
lrwxrwxrwx 1 root root    53 Sep 23 21:36 codex -> /opt/codex-home/packages/standalone/current/bin/codex
lrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3
lrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12
-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12
lrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12
lrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3
lrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12
-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12
lrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3
lrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config
lrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12
lrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config
-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12
-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config
/usr/local/bin/python3.12
/usr/local/bin/python3.12-config
/usr/local/bin/python
/usr/local/bin/python-config
/usr/local/bin/python3-config
/usr/local/bin/python3
/opt/task-venv/bin/python3.12
/opt/task-venv/bin/python
/opt/task-venv/bin/python3
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab447380abc87d2b0b703624f78eac5",
  "usage": {
    "input_tokens": 21186,
    "cached_input_tokens": 14720,
    "cache_write_input_tokens": 0,
    "output_tokens": 143,
    "reasoning_output_tokens": 25,
    "total_tokens": 21329
  },
  "turn_token_usage": {
    "input_tokens": 51286,
    "cached_input_tokens": 36992,
    "cache_write_input_tokens": 0,
    "output_tokens": 537,
    "reasoning_output_tokens": 66,
    "total_tokens": 51823
  },
  "thread_token_usage": {
    "input_tokens": 51286,
    "cached_input_tokens": 36992,
    "cache_write_input_tokens": 0,
    "output_tokens": 537,
    "reasoning_output_tokens": 66,
    "total_tokens": 51823
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-643e5578-0390-4bc7-970b-e542b625b06e",
    "process_id": "48602",
    "command": [
      "/bin/sh",
      "-lc",
      "which -a python python3 uv || true\nls -la /app /opt /usr/local/bin | head -100\nfind / -type f -path '*/bin/python*' 2>/dev/null | head -40\nfind / -type l -path '*/bin/python*' 2>/dev/null | head -40"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which -a python python3 uv || true\nls -la /app /opt /usr/local/bin | head -100\nfind / -type f -path '*/bin/python*' 2>/dev/null | head -40\nfind / -type l -path '*/bin/python*' 2>/dev/null | head -40"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3-config\n/usr/local/bin/python3\n/opt/task-venv/bin/python3.12\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3-config\n/usr/local/bin/python3\n/opt/task-venv/bin/python3.12\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 280682649
    },
    "formatted_output": "/usr/local/bin/python3.12\n/usr/local/bin/python3.12-config\n/usr/local/bin/python\n/usr/local/bin/python-config\n/usr/local/bin/python3-config\n/usr/local/bin/python3\n/opt/task-venv/bin/python3.12\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n"
  }
}
10

exec command

+00:25
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"d9be11","wall_time_seconds":1.000904967,"session_id":43565,"original_token_count":0,"output":""}
Harness context token usage record
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11

exec command

+00:30
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"rg -n \"negative|def train|def main|ArgumentParser|sample|loss|rank|partition|cache\" /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":15000}); text(r.output)

Tool response 

Script completed
Wall time 0.0 seconds
Output:

/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24
/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8
/app/solution/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
/app/solution/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
/app/solution/two_tower_model.py:346:def invalid_negative_mask(
/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,
/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])
/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):
/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(
/app/solution/two_tower_model.py:358:            negatives[:, :column]
/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(
/app/solution/two_tower_model.py:374:    """Draw uniform and unique-target in-batch negatives."""
/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)
/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(
/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items
/app/solution/two_tower_model.py:419:            return negatives, correction
/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements
/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(
/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items
/app/solution/two_tower_model.py:432:        raise RuntimeError("mixed negative resampling failed false-negative filter")
/app/solution/two_tower_model.py:433:    return negatives, correction
/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and
/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state
/app/solution/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536
/app/solution/train_current_model.py:56:    uniform_negatives: int = 24
/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8
/app/solution/train_current_model.py:61:    negative_filter_scope: str = "full-training-history"
/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,
/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,
/app/solution/train_current_model.py:346:        "popularity_rank"
/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
/app/solution/train_current_model.py:473:        "cache_version": CACHE_VERSION,
/app/solution/train_current_model.py:477:        "event_time_feature": "exp-negative-gap-over-60-days",
/app/solution/train_current_model.py:481:def save_cache(
/app/solution/train_current_model.py:487:        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
/app/solution/train_current_model.py:490:    log(f"saved public-derived full-sequence cache: {path}")
/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
/app/solution/train_current_model.py:496:        expected = cache_metadata(stage, config)
/app/solution/train_current_model.py:498:            raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
/app/solution/train_current_model.py:505:        f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
/app/solution/train_current_model.py:515:    cache: Path | None,
/app/solution/train_current_model.py:523:    if cache is not None and cache.is_file():
/app/solution/train_current_model.py:524:        prepared = load_cache(cache, stage, config)
/app/solution/train_current_model.py:526:            raise RuntimeError("cached catalog differs from candidate artifact")
/app/solution/train_current_model.py:529:    if cache is not None:
/app/solution/train_current_model.py:530:        save_cache(cache, prepared, stage, config)
/app/solution/train_current_model.py:603:        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
/app/solution/train_current_model.py:609:def invalid_full_history_negatives(
/app/solution/train_current_model.py:610:    negatives: np.ndarray,
/app/solution/train_current_model.py:616:    """Return row-level invalid negatives using the complete training prefix."""
/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3
/app/solution/train_current_model.py:618:    bit_columns = negatives & 7
/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]
/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):
/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]
/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(
/app/solution/train_current_model.py:641:    """Draw mixed negatives and reject all known public-training positives."""
/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),
/app/solution/train_current_model.py:653:    if config.inbatch_negatives:
/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:
/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),
/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)
/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count
/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(
/app/solution/train_current_model.py:689:            negatives,
/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),
/app/solution/train_current_model.py:706:        negatives[invalid] = replacements
/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(
/app/solution/train_current_model.py:710:        negatives,
/app/solution/train_current_model.py:717:        raise RuntimeError("full-history negative resampling failed")
/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),
/app/solution/train_current_model.py:724:def draw_training_negatives(
/app/solution/train_current_model.py:736:    if config.negative_filter_scope == "full-training-history":
/app/solution/train_current_model.py:738:            raise RuntimeError("full-history negative filter was not built")
/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(
/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(
/app/solution/train_current_model.py:755:        config.uniform_negatives,
/app/solution/train_current_model.py:756:        config.inbatch_negatives,
/app/solution/train_current_model.py:816:    negatives: torch.Tensor,
/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])
/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(
/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1
/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors
/app/solution/train_current_model.py:845:def corrected_negative_loss(
/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives
/app/solution/train_current_model.py:853:    if config.uniform_negatives:
/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[
/app/solution/train_current_model.py:855:            :, : config.uniform_negatives
/app/solution/train_current_model.py:857:    if config.inbatch_negatives:
/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)
/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (
/app/solution/train_current_model.py:880:        raise RuntimeError("chronological target partition produced negative counts")
/app/solution/train_current_model.py:888:def chronological_target_partition(
/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,
/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,
/app/solution/train_current_model.py:1121:    sample_weights: np.ndarray,
/app/solution/train_current_model.py:1127:    total_weight = float(sample_weights.sum())
/app/solution/train_current_model.py:1129:        raise RuntimeError("target block has no positive sample weight")
/app/solution/train_current_model.py:1132:        "loss_sum": 0.0,
/app/solution/train_current_model.py:1134:        "negative_sum": 0.0,
/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):
/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)
/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(
/app/solution/train_current_model.py:1165:            negatives[begin:end],
/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(
/app/solution/train_current_model.py:1169:            "bd,bkd->bk", query_vectors, negative_vectors
/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)
/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(
/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config
/app/solution/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)
/app/solution/train_current_model.py:1178:        loss_sum = (
/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0
/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):
/app/solution/train_current_model.py:1182:            raise RuntimeError("dense next-item training produced non-finite loss")
/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()
/app/solution/train_current_model.py:1188:        statistics["loss_sum"] += float(loss_sum.detach().cpu())
/app/solution/train_current_model.py:1190:        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:
/app/solution/train_current_model.py:1200:        torch.mps.empty_cache()
/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives
/app/solution/train_current_model.py:1228:    total_loss = 0.0
/app/solution/train_current_model.py:1232:    total_negative = 0.0
/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(
/app/solution/train_current_model.py:1314:                negatives,
/app/solution/train_current_model.py:1315:                negative_weights,
/app/solution/train_current_model.py:1327:            total_loss += statistics["loss_sum"]
/app/solution/train_current_model.py:1331:            total_negative += statistics["negative_sum"]
/app/solution/train_current_model.py:1335:                release_accelerator_cache(device)
/app/solution/train_current_model.py:1337:                batch_bce = statistics["loss_sum"] / max(
/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)
/app/solution/train_current_model.py:1351:        "loss": total_loss / max(total_weight, 1.0e-12),
/app/solution/train_current_model.py:1353:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),
/app/solution/train_current_model.py:1388:    total_loss = 0.0
/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(
/app/solution/train_current_model.py:1469:                negatives,
/app/solution/train_current_model.py:1470:                negative_weights,
/app/solution/train_current_model.py:1475:            total_loss += statistics["loss_sum"]
/app/solution/train_current_model.py:1479:            release_accelerator_cache(device)
/app/solution/train_current_model.py:1481:        "validation_loss": total_loss / max(total_weight, 1.0e-12),
/app/solution/train_current_model.py:1521:def train_model(
/app/solution/train_current_model.py:1530:    partition = chronological_target_partition(
/app/solution/train_current_model.py:1536:    training_counts = partition.training_counts
/app/solution/train_current_model.py:1537:    validation_counts = partition.validation_counts
/app/solution/train_current_model.py:1541:        f"selected={partition.selected_count:,} "
/app/solution/train_current_model.py:1542:        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
/app/solution/train_current_model.py:1555:        partition.window_starts,
/app/solution/train_current_model.py:1562:        partition.history_training_counts,
/app/solution/train_current_model.py:1566:        build_known_positive_filter(prepared, partition.history_training_counts)
/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == "full-training-history"
/app/solution/train_current_model.py:1571:    best_loss = float("inf")
/app/solution/train_current_model.py:1601:            "negative_filter_scope", "last-100"
/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope
/app/solution/train_current_model.py:1657:            prior.pop("validation_loss", None)
/app/solution/train_current_model.py:1660:                f"{stored_filter_scope}->{config.negative_filter_scope}, "
/app/solution/train_current_model.py:1666:        if "validation_loss" not in prior:
/app/solution/train_current_model.py:1673:                    partition.window_starts,
/app/solution/train_current_model.py:1682:        best_loss = float(prior["validation_loss"])
/app/solution/train_current_model.py:1687:            f"loss={best_loss:.6f}"
/app/solution/train_current_model.py:1697:            partition.window_starts,
/app/solution/train_current_model.py:1710:            partition.window_starts,
/app/solution/train_current_model.py:1731:            diagnostics["validation_loss"]
/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta
/app/solution/train_current_model.py:1735:            best_loss = diagnostics["validation_loss"]
/app/solution/train_current_model.py:1754:            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
/app/solution/train_current_model.py:1755:            f"validation_loss={diagnostics['validation_loss']:.5f} "
/app/solution/train_current_model.py:1762:        release_accelerator_cache(device)
/app/solution/train_current_model.py:1766:                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
/app/solution/train_current_model.py:1776:        f"loss={best_loss:.6f}"
/app/solution/train_current_model.py:1861:def rank_candidates(
/app/solution/train_current_model.py:1867:    ranking_batch_size: int,
/app/solution/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):
/app/solution/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))
/app/solution/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)
/app/solution/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)
/app/solution/train_current_model.py:1900:            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
/app/solution/train_current_model.py:1909:    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
/app/solution/train_current_model.py:1922:def main() -> int:
/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)
/app/solution/train_current_model.py:1929:    parser.add_argument("--prepared-cache", type=Path)
/app/solution/train_current_model.py:1939:        "--loss-microbatch-size",
/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,
/app/solution/train_current_model.py:1944:        "--negative-filter-scope",
/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,
/app/solution/train_current_model.py:2003:    parser.add_argument("--ranking-batch-size", type=int, default=64)
/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1
/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,
/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,
/app/solution/train_current_model.py:2059:        args.prepared_cache.resolve() if args.prepared_cache else None,
/app/solution/train_current_model.py:2061:    target_partition = chronological_target_partition(
/app/solution/train_current_model.py:2076:                        target_partition.training_counts.sum()
/app/solution/train_current_model.py:2079:                        target_partition.validation_counts.sum()
/app/solution/train_current_model.py:2082:                        target_partition.window_starts.sum()
/app/solution/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(
/app/solution/train_current_model.py:2108:        args.ranking_batch_size,
/app/solution/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())
/app/solution/train_current_model.py:2117:                "rows": len(ranking),
/app/solution/train_current_model.py:2118:                "users": int(ranking.uid.nunique()),
/app/solution/train_current_model.py:2126:                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
/app/solution/train_current_model.py:2129:                    "loss_execution": "gradient-accumulated-microbatches",
/app/solution/train_current_model.py:2135:                    "objective": "sample-weighted-BCE-at-every-timestep",
/app/solution/train_current_model.py:2136:                    "negative_sampling": (
/app/solution/train_current_model.py:2139:                    "false_negative_filter": config.negative_filter_scope,
/app/solution/train_current_model.py:2146:                    target_partition.training_counts.sum()
/app/solution/train_current_model.py:2149:                    target_partition.validation_counts.sum()
/app/solution/train_current_model.py:2151:                "discarded_old_targets": int(target_partition.window_starts.sum()),
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-bae8ba3e-d10c-474d-ba70-233948dfc7a8",
    "process_id": "91387",
    "command": [
      "/bin/sh",
      "-lc",
      "rg -n \"negative|def train|def main|ArgumentParser|sample|loss|rank|partition|cache\" /app/solution/train_current_model.py /app/solution/two_tower_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg -n 'negative|def train|def main|ArgumentParser|sample|loss|rank|partition|cache' /app/solution/train_current_model.py /app/solution/two_tower_model.py",
        "query": "negative|def train|def main|ArgumentParser|sample|loss|rank|partition|cache",
        "path": "train_current_model.py"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/app/solution/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:346:        \"popularity_rank\"\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:473:        \"cache_version\": CACHE_VERSION,\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:487:        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n/app/solution/train_current_model.py:490:    log(f\"saved public-derived full-sequence cache: {path}\")\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:496:        expected = cache_metadata(stage, config)\n/app/solution/train_current_model.py:498:            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n/app/solution/train_current_model.py:505:        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n/app/solution/train_current_model.py:515:    cache: Path | None,\n/app/solution/train_current_model.py:523:    if cache is not None and cache.is_file():\n/app/solution/train_current_model.py:524:        prepared = load_cache(cache, stage, config)\n/app/solution/train_current_model.py:526:            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n/app/solution/train_current_model.py:529:    if cache is not None:\n/app/solution/train_current_model.py:530:        save_cache(cache, prepared, stage, config)\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1121:    sample_weights: np.ndarray,\n/app/solution/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/app/solution/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1200:        torch.mps.empty_cache()\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1335:                release_accelerator_cache(device)\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1479:            release_accelerator_cache(device)\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1530:    partition = chronological_target_partition(\n/app/solution/train_current_model.py:1536:    training_counts = partition.training_counts\n/app/solution/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/app/solution/train_current_model.py:1541:        f\"selected={partition.selected_count:,} \"\n/app/solution/train_current_model.py:1542:        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n/app/solution/train_current_model.py:1555:        partition.window_starts,\n/app/solution/train_current_model.py:1562:        partition.history_training_counts,\n/app/solution/train_current_model.py:1566:        build_known_positive_filter(prepared, partition.history_training_counts)\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1673:                    partition.window_starts,\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1697:            partition.window_starts,\n/app/solution/train_current_model.py:1710:            partition.window_starts,\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1762:        release_accelerator_cache(device)\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1867:    ranking_batch_size: int,\n/app/solution/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/app/solution/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/app/solution/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/app/solution/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/app/solution/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/app/solution/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2059:        args.prepared_cache.resolve() if args.prepared_cache else None,\n/app/solution/train_current_model.py:2061:    target_partition = chronological_target_partition(\n/app/solution/train_current_model.py:2076:                        target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2082:                        target_partition.window_starts.sum()\n/app/solution/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/app/solution/train_current_model.py:2108:        args.ranking_batch_size,\n/app/solution/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/app/solution/train_current_model.py:2117:                \"rows\": len(ranking),\n/app/solution/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/app/solution/train_current_model.py:2146:                    target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2151:                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n",
    "stderr": "",
    "aggregated_output": "/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/app/solution/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:346:        \"popularity_rank\"\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:473:        \"cache_version\": CACHE_VERSION,\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:487:        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n/app/solution/train_current_model.py:490:    log(f\"saved public-derived full-sequence cache: {path}\")\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:496:        expected = cache_metadata(stage, config)\n/app/solution/train_current_model.py:498:            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n/app/solution/train_current_model.py:505:        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n/app/solution/train_current_model.py:515:    cache: Path | None,\n/app/solution/train_current_model.py:523:    if cache is not None and cache.is_file():\n/app/solution/train_current_model.py:524:        prepared = load_cache(cache, stage, config)\n/app/solution/train_current_model.py:526:            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n/app/solution/train_current_model.py:529:    if cache is not None:\n/app/solution/train_current_model.py:530:        save_cache(cache, prepared, stage, config)\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1121:    sample_weights: np.ndarray,\n/app/solution/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/app/solution/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1200:        torch.mps.empty_cache()\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1335:                release_accelerator_cache(device)\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1479:            release_accelerator_cache(device)\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1530:    partition = chronological_target_partition(\n/app/solution/train_current_model.py:1536:    training_counts = partition.training_counts\n/app/solution/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/app/solution/train_current_model.py:1541:        f\"selected={partition.selected_count:,} \"\n/app/solution/train_current_model.py:1542:        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n/app/solution/train_current_model.py:1555:        partition.window_starts,\n/app/solution/train_current_model.py:1562:        partition.history_training_counts,\n/app/solution/train_current_model.py:1566:        build_known_positive_filter(prepared, partition.history_training_counts)\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1673:                    partition.window_starts,\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1697:            partition.window_starts,\n/app/solution/train_current_model.py:1710:            partition.window_starts,\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1762:        release_accelerator_cache(device)\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1867:    ranking_batch_size: int,\n/app/solution/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/app/solution/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/app/solution/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/app/solution/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/app/solution/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/app/solution/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2059:        args.prepared_cache.resolve() if args.prepared_cache else None,\n/app/solution/train_current_model.py:2061:    target_partition = chronological_target_partition(\n/app/solution/train_current_model.py:2076:                        target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2082:                        target_partition.window_starts.sum()\n/app/solution/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/app/solution/train_current_model.py:2108:        args.ranking_batch_size,\n/app/solution/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/app/solution/train_current_model.py:2117:                \"rows\": len(ranking),\n/app/solution/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/app/solution/train_current_model.py:2146:                    target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2151:                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3810
    },
    "formatted_output": "/app/solution/two_tower_model.py:40:    uniform_negatives: int = 24\n/app/solution/two_tower_model.py:41:    inbatch_negatives: int = 8\n/app/solution/two_tower_model.py:73:    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n/app/solution/two_tower_model.py:79:            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n/app/solution/two_tower_model.py:346:def invalid_negative_mask(\n/app/solution/two_tower_model.py:347:    negatives: torch.Tensor,\n/app/solution/two_tower_model.py:353:    invalid = negatives.eq(targets[:, None])\n/app/solution/two_tower_model.py:354:    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:355:    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n/app/solution/two_tower_model.py:356:    for column in range(1, negatives.shape[1]):\n/app/solution/two_tower_model.py:357:        invalid[:, column] |= negatives[:, column, None].eq(\n/app/solution/two_tower_model.py:358:            negatives[:, :column]\n/app/solution/two_tower_model.py:363:def draw_filtered_mixed_negatives(\n/app/solution/two_tower_model.py:374:    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:406:    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n/app/solution/two_tower_model.py:407:    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n/app/solution/two_tower_model.py:412:    negatives = negatives_cpu.to(device)\n/app/solution/two_tower_model.py:415:        invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:416:            negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:419:            return negatives, correction\n/app/solution/two_tower_model.py:426:        negatives[invalid] = replacements\n/app/solution/two_tower_model.py:428:    invalid = invalid_negative_mask(\n/app/solution/two_tower_model.py:429:        negatives, targets, context_items, liked_items\n/app/solution/two_tower_model.py:432:        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n/app/solution/two_tower_model.py:433:    return negatives, correction\n/app/solution/train_current_model.py:6:loss.  The fast default retains each user's most recent 128 training targets and\n/app/solution/train_current_model.py:9:computes all of its timestep losses and carries the detached GRU hidden state\n/app/solution/train_current_model.py:13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n/app/solution/train_current_model.py:14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n/app/solution/train_current_model.py:55:    loss_microbatch_size: int = 65_536\n/app/solution/train_current_model.py:56:    uniform_negatives: int = 24\n/app/solution/train_current_model.py:57:    inbatch_negatives: int = 8\n/app/solution/train_current_model.py:61:    negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:165:        uniform_negatives=config.uniform_negatives,\n/app/solution/train_current_model.py:166:        inbatch_negatives=config.inbatch_negatives,\n/app/solution/train_current_model.py:346:        \"popularity_rank\"\n/app/solution/train_current_model.py:471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n/app/solution/train_current_model.py:473:        \"cache_version\": CACHE_VERSION,\n/app/solution/train_current_model.py:477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:481:def save_cache(\n/app/solution/train_current_model.py:487:        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n/app/solution/train_current_model.py:490:    log(f\"saved public-derived full-sequence cache: {path}\")\n/app/solution/train_current_model.py:493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n/app/solution/train_current_model.py:496:        expected = cache_metadata(stage, config)\n/app/solution/train_current_model.py:498:            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n/app/solution/train_current_model.py:505:        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n/app/solution/train_current_model.py:515:    cache: Path | None,\n/app/solution/train_current_model.py:523:    if cache is not None and cache.is_file():\n/app/solution/train_current_model.py:524:        prepared = load_cache(cache, stage, config)\n/app/solution/train_current_model.py:526:            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n/app/solution/train_current_model.py:529:    if cache is not None:\n/app/solution/train_current_model.py:530:        save_cache(cache, prepared, stage, config)\n/app/solution/train_current_model.py:603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n/app/solution/train_current_model.py:609:def invalid_full_history_negatives(\n/app/solution/train_current_model.py:610:    negatives: np.ndarray,\n/app/solution/train_current_model.py:616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n/app/solution/train_current_model.py:617:    byte_columns = negatives >> 3\n/app/solution/train_current_model.py:618:    bit_columns = negatives & 7\n/app/solution/train_current_model.py:621:    invalid |= negatives == targets[:, None]\n/app/solution/train_current_model.py:622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n/app/solution/train_current_model.py:623:    for column in range(1, negatives.shape[1]):\n/app/solution/train_current_model.py:625:            negatives[:, column, None] == negatives[:, :column]\n/app/solution/train_current_model.py:630:def draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n/app/solution/train_current_model.py:647:        (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:653:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n/app/solution/train_current_model.py:672:        if corrected_count < config.inbatch_negatives:\n/app/solution/train_current_model.py:675:                (batch_size, config.inbatch_negatives - corrected_count),\n/app/solution/train_current_model.py:681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n/app/solution/train_current_model.py:682:    correction = np.ones_like(negatives, dtype=np.float32)\n/app/solution/train_current_model.py:685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n/app/solution/train_current_model.py:688:        invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:689:            negatives,\n/app/solution/train_current_model.py:697:                torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:706:        negatives[invalid] = replacements\n/app/solution/train_current_model.py:709:    invalid = invalid_full_history_negatives(\n/app/solution/train_current_model.py:710:        negatives,\n/app/solution/train_current_model.py:717:        raise RuntimeError(\"full-history negative resampling failed\")\n/app/solution/train_current_model.py:719:        torch.from_numpy(negatives).to(device),\n/app/solution/train_current_model.py:724:def draw_training_negatives(\n/app/solution/train_current_model.py:736:    if config.negative_filter_scope == \"full-training-history\":\n/app/solution/train_current_model.py:738:            raise RuntimeError(\"full-history negative filter was not built\")\n/app/solution/train_current_model.py:739:        return draw_full_history_filtered_negatives(\n/app/solution/train_current_model.py:750:    return base.draw_filtered_mixed_negatives(\n/app/solution/train_current_model.py:755:        config.uniform_negatives,\n/app/solution/train_current_model.py:756:        config.inbatch_negatives,\n/app/solution/train_current_model.py:816:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n/app/solution/train_current_model.py:822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n/app/solution/train_current_model.py:823:        len(positives), negatives.shape[1], -1\n/app/solution/train_current_model.py:825:    return positive_vectors, negative_vectors\n/app/solution/train_current_model.py:845:def corrected_negative_loss(\n/app/solution/train_current_model.py:851:    total = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:853:    if config.uniform_negatives:\n/app/solution/train_current_model.py:854:        result += config.uniform_negatives * raw[\n/app/solution/train_current_model.py:855:            :, : config.uniform_negatives\n/app/solution/train_current_model.py:857:    if config.inbatch_negatives:\n/app/solution/train_current_model.py:858:        selected = slice(config.uniform_negatives, total)\n/app/solution/train_current_model.py:860:        result += config.inbatch_negatives * (\n/app/solution/train_current_model.py:880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:888:def chronological_target_partition(\n/app/solution/train_current_model.py:1119:    negatives: torch.Tensor,\n/app/solution/train_current_model.py:1120:    negative_weights: torch.Tensor,\n/app/solution/train_current_model.py:1121:    sample_weights: np.ndarray,\n/app/solution/train_current_model.py:1127:    total_weight = float(sample_weights.sum())\n/app/solution/train_current_model.py:1129:        raise RuntimeError(\"target block has no positive sample weight\")\n/app/solution/train_current_model.py:1132:        \"loss_sum\": 0.0,\n/app/solution/train_current_model.py:1134:        \"negative_sum\": 0.0,\n/app/solution/train_current_model.py:1138:    for begin in range(0, target_count, config.loss_microbatch_size):\n/app/solution/train_current_model.py:1139:        end = min(begin + config.loss_microbatch_size, target_count)\n/app/solution/train_current_model.py:1162:        positive_vectors, negative_vectors = candidate_vectors(\n/app/solution/train_current_model.py:1165:            negatives[begin:end],\n/app/solution/train_current_model.py:1168:        negative_logits = torch.einsum(\n/app/solution/train_current_model.py:1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n/app/solution/train_current_model.py:1171:        positive_loss = F.softplus(-positive_logits)\n/app/solution/train_current_model.py:1172:        negative_loss = corrected_negative_loss(\n/app/solution/train_current_model.py:1173:            negative_logits, negative_weights[begin:end], config\n/app/solution/train_current_model.py:1176:            sample_weights[begin:end].astype(np.float32, copy=False)\n/app/solution/train_current_model.py:1178:        loss_sum = (\n/app/solution/train_current_model.py:1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n/app/solution/train_current_model.py:1181:        if not bool(torch.isfinite(loss_sum)):\n/app/solution/train_current_model.py:1182:            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n/app/solution/train_current_model.py:1184:            (loss_sum / total_weight).backward()\n/app/solution/train_current_model.py:1188:        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n/app/solution/train_current_model.py:1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n/app/solution/train_current_model.py:1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n/app/solution/train_current_model.py:1198:def release_accelerator_cache(device: torch.device) -> None:\n/app/solution/train_current_model.py:1200:        torch.mps.empty_cache()\n/app/solution/train_current_model.py:1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n/app/solution/train_current_model.py:1228:    total_loss = 0.0\n/app/solution/train_current_model.py:1232:    total_negative = 0.0\n/app/solution/train_current_model.py:1293:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1314:                negatives,\n/app/solution/train_current_model.py:1315:                negative_weights,\n/app/solution/train_current_model.py:1327:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1331:            total_negative += statistics[\"negative_sum\"]\n/app/solution/train_current_model.py:1335:                release_accelerator_cache(device)\n/app/solution/train_current_model.py:1337:                batch_bce = statistics[\"loss_sum\"] / max(\n/app/solution/train_current_model.py:1340:                running_bce = total_loss / max(total_weight, 1.0e-12)\n/app/solution/train_current_model.py:1351:        \"loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1355:        / max(total_targets * negative_count, 1),\n/app/solution/train_current_model.py:1388:    total_loss = 0.0\n/app/solution/train_current_model.py:1449:            negatives, negative_weights = draw_training_negatives(\n/app/solution/train_current_model.py:1469:                negatives,\n/app/solution/train_current_model.py:1470:                negative_weights,\n/app/solution/train_current_model.py:1475:            total_loss += statistics[\"loss_sum\"]\n/app/solution/train_current_model.py:1479:            release_accelerator_cache(device)\n/app/solution/train_current_model.py:1481:        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n/app/solution/train_current_model.py:1521:def train_model(\n/app/solution/train_current_model.py:1530:    partition = chronological_target_partition(\n/app/solution/train_current_model.py:1536:    training_counts = partition.training_counts\n/app/solution/train_current_model.py:1537:    validation_counts = partition.validation_counts\n/app/solution/train_current_model.py:1541:        f\"selected={partition.selected_count:,} \"\n/app/solution/train_current_model.py:1542:        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n/app/solution/train_current_model.py:1555:        partition.window_starts,\n/app/solution/train_current_model.py:1562:        partition.history_training_counts,\n/app/solution/train_current_model.py:1566:        build_known_positive_filter(prepared, partition.history_training_counts)\n/app/solution/train_current_model.py:1567:        if config.negative_filter_scope == \"full-training-history\"\n/app/solution/train_current_model.py:1571:    best_loss = float(\"inf\")\n/app/solution/train_current_model.py:1601:            \"negative_filter_scope\", \"last-100\"\n/app/solution/train_current_model.py:1629:            stored_filter_scope != config.negative_filter_scope\n/app/solution/train_current_model.py:1657:            prior.pop(\"validation_loss\", None)\n/app/solution/train_current_model.py:1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n/app/solution/train_current_model.py:1666:        if \"validation_loss\" not in prior:\n/app/solution/train_current_model.py:1673:                    partition.window_starts,\n/app/solution/train_current_model.py:1682:        best_loss = float(prior[\"validation_loss\"])\n/app/solution/train_current_model.py:1687:            f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1697:            partition.window_starts,\n/app/solution/train_current_model.py:1710:            partition.window_starts,\n/app/solution/train_current_model.py:1731:            diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1732:            < best_loss - config.early_stopping_min_delta\n/app/solution/train_current_model.py:1735:            best_loss = diagnostics[\"validation_loss\"]\n/app/solution/train_current_model.py:1754:            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n/app/solution/train_current_model.py:1755:            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n/app/solution/train_current_model.py:1762:        release_accelerator_cache(device)\n/app/solution/train_current_model.py:1766:                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1776:        f\"loss={best_loss:.6f}\"\n/app/solution/train_current_model.py:1861:def rank_candidates(\n/app/solution/train_current_model.py:1867:    ranking_batch_size: int,\n/app/solution/train_current_model.py:1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):\n/app/solution/train_current_model.py:1883:        end = min(start + ranking_batch_size, len(eligibility.uids))\n/app/solution/train_current_model.py:1896:                (uid, int(prepared.item_ids[column]), rank)\n/app/solution/train_current_model.py:1897:                for rank, column in enumerate(columns, start=1)\n/app/solution/train_current_model.py:1900:            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n/app/solution/train_current_model.py:1909:    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n/app/solution/train_current_model.py:1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n/app/solution/train_current_model.py:1922:def main() -> int:\n/app/solution/train_current_model.py:1923:    parser = argparse.ArgumentParser(description=__doc__)\n/app/solution/train_current_model.py:1929:    parser.add_argument(\"--prepared-cache\", type=Path)\n/app/solution/train_current_model.py:1939:        \"--loss-microbatch-size\",\n/app/solution/train_current_model.py:1941:        default=Config.loss_microbatch_size,\n/app/solution/train_current_model.py:1944:        \"--negative-filter-scope\",\n/app/solution/train_current_model.py:1946:        default=Config.negative_filter_scope,\n/app/solution/train_current_model.py:2003:    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n/app/solution/train_current_model.py:2012:        or args.loss_microbatch_size < 1\n/app/solution/train_current_model.py:2033:        loss_microbatch_size=args.loss_microbatch_size,\n/app/solution/train_current_model.py:2034:        negative_filter_scope=args.negative_filter_scope,\n/app/solution/train_current_model.py:2059:        args.prepared_cache.resolve() if args.prepared_cache else None,\n/app/solution/train_current_model.py:2061:    target_partition = chronological_target_partition(\n/app/solution/train_current_model.py:2076:                        target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2079:                        target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2082:                        target_partition.window_starts.sum()\n/app/solution/train_current_model.py:2102:    ranking, embedding_diagnostics = rank_candidates(\n/app/solution/train_current_model.py:2108:        args.ranking_batch_size,\n/app/solution/train_current_model.py:2110:    write_ranking(ranking, args.output.resolve())\n/app/solution/train_current_model.py:2117:                \"rows\": len(ranking),\n/app/solution/train_current_model.py:2118:                \"users\": int(ranking.uid.nunique()),\n/app/solution/train_current_model.py:2126:                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n/app/solution/train_current_model.py:2129:                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n/app/solution/train_current_model.py:2135:                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n/app/solution/train_current_model.py:2136:                    \"negative_sampling\": (\n/app/solution/train_current_model.py:2139:                    \"false_negative_filter\": config.negative_filter_scope,\n/app/solution/train_current_model.py:2146:                    target_partition.training_counts.sum()\n/app/solution/train_current_model.py:2149:                    target_partition.validation_counts.sum()\n/app/solution/train_current_model.py:2151:                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n"
  }
}
Harness context token usage record
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}
12

exec command

+00:34
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"sed -n '320,460p' /app/solution/two_tower_model.py\nsed -n '540,780p' /app/solution/train_current_model.py\nsed -n '780,940p' /app/solution/train_current_model.py\nsed -n '1080,1380p' /app/solution/train_current_model.py\nsed -n '1520,1790p' /app/solution/train_current_model.py\nsed -n '1920,2165p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":40000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 12391)
Total output lines: 1333

            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_1, config.item_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_2, dim),
        )
        nn.init.normal_(self.context_embedding.weight, std=0.02)
        nn.init.normal_(self.candidate_embedding.weight, std=0.02)
        with torch.no_grad():
            self.context_embedding.weight[n_items].zero_()

    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
        mask = items.ne(self.n_items)
        vectors = self.context_embedding(items)
        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
            dim=1, keepdim=True
        ).clamp_min(1)

    def encode_items(self, items: torch.Tensor) -> torch.Tensor:
        inputs = torch.cat(
            [self.candidate_embedding(items), self.item_features[items]], dim=1
        )
        return self.item_tower(inputs)


def invalid_negative_mask(
    negatives: torch.Tensor,
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
) -> torch.Tensor:
    """Identify targets, known positives, active likes, and row duplicates."""
    invalid = negatives.eq(targets[:, None])
    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= negatives[:, column, None].eq(
            negatives[:, :column]
        ).any(dim=1)
    return invalid


def draw_filtered_mixed_negatives(
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
    n_items: int,
    uniform_count: int,
    inbatch_count: int,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw uniform and unique-target in-batch negatives."""
    batch_size = len(targets)
    uniform = torch.randint(
        n_items,
        (batch_size, uniform_count),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
    corrected_count = 0
    if inbatch_count:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
                -1.0,
            )
            columns = torch.topk(priorities, k=corrected_count, dim=1).indices
            inbatch = unique_targets[columns]
        if corrected_count < inbatch_count:
            filler = torch.randint(
                n_items,
                (batch_size, inbatch_count - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
    if corrected_count:
        correction_cpu[
            :, uniform_count : uniform_count + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]]
    negatives = negatives_cpu.to(device)
    correction = correction_cpu.to(device)
    for _ in range(32):
        invalid = invalid_negative_mask(
            negatives, targets, context_items, liked_items
        )
        if not bool(invalid.any()):
            return negatives, correction
        replacements = torch.randint(
            n_items,
            (int(invalid.sum().detach().cpu()),),
            generator=generator,
            device="cpu",
        ).to(device)
        negatives[invalid] = replacements
        correction[invalid] = 1.0
    invalid = invalid_negative_mask(
        negatives, targets, context_items, liked_items
    )
    if bool(invalid.any()):
        raise RuntimeError("mixed negative resampling failed false-negative filter")
    return negatives, correction
    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray
) -> np.ndarray:
    """Pack each user's complete public-training positive-item set."""
    width = (len(prepared.item_ids) + 7) // 8
    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
    retained = 0
    for user_row, target_count in enumerate(training_counts):
        if not target_count:
            continue
        offset = int(prepared.offsets[user_row])
        # T adjacent targets consume sequence positions 0 through T.  The
        # chronological BCE suffix starts at T + 1 and is intentionally absent.
        items = np.unique(
            prepared.sequence_items[
                offset : offset + int(target_count) + 1
            ].astype(np.int64)
        )
        byte_columns = items >> 3
        bit_values = np.left_shift(
            np.uint8(1), (items & 7).astype(np.uint8)
        )
        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
        retained += len(items)
    log(
        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
        f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
    )
    return packed


def invalid_full_history_negatives(
    negatives: np.ndarray,
    targets: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray,
    liked_items: np.ndarray,
) -> np.ndarray:
    """Return row-level invalid negatives using the complete training prefix."""
    byte_columns = negatives >> 3
    bit_columns = negatives & 7
    known = known_positive_packed[user_rows[:, None], byte_columns]
    invalid = ((known >> bit_columns) & 1).astype(bool)
    invalid |= negatives == targets[:, None]
    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= (
            negatives[:, column, None] == negatives[:, :column]
        ).any(axis=1)
    return invalid


def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
    )


def event_sequence(
    model: base.SequentialTwoTower,
    items: torch.Tensor,
    features: torch.Tensor,
    hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    inputs = torch.cat([model.context_embedding(items), features], dim=-1)
    projected = model.event_projection(inputs)
    return model.sequence_encoder(projected, hidden)


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_rows],
        torch.from_numpy(counts.astype(np.float32)).to(device),
    )


def user_queries(
    model: base.SequentialTwoTower,
    sequence_vectors: torch.Tensor,
    liked_vectors: torch.Tensor,
    disliked_vectors: torch.Tensor,
    user_features: torch.Tensor,
) -> torch.Tensor:
    return model.user_tower(
        torch.cat(
            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
            dim=1,
        )
    )


def candidate_vectors(
    model: base.SequentialTwoTower,
    positives: torch.Tensor,
    negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    all_items = torch.cat([positives, negatives.reshape(-1)])
    unique_items, inverse = torch.unique(all_items, return_inverse=True)
    encoded = model.encode_items(unique_items)
    positive_vectors = encoded[inverse[: len(positives)]]
    negative_vectors = encoded[inverse[len(positives) :]].reshape(
        len(positives), negatives.shape[1], -1
    )
    return positive_vectors, negative_vectors


def history_windows(
    sequence: np.ndarray,
    start: int,
    count: int,
    width: int,
    padding_item: int,
) -> np.ndarray:
    base_start = max(0, start - width + 1)
    segment = sequence[base_start : start + count]
    padded = np.concatenate(
        [np.full(width - 1, padding_item, dtype=np.uint16), segment]
    )
    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,
    training_counts: np.ndarray,
) -> np.ndarray:
    """Return the discarded prefix before the retained chronological suffix."""
    training_region_ends = total_targets - validation_counts
    window_starts = training_region_ends - training_counts
    if (
        np.any(window_starts < 0)
        or np.any(training_counts < 0)
        or np.any(validation_counts < 0)
    ):
        raise RuntimeError("chronological target partition produced negative counts")
    if not np.array_equal(
        window_starts + training_counts + validation_counts, total_targets
    ):
        raise RuntimeError("chronological target window is not a final contiguous suffix")
    return window_starts


def chronological_target_partition(
    prepared: SequenceData,
    validation_fraction: float,
    max_train_targets_per_user: int,
    max_validation_targets_per_user: int,
) -> TargetPartition:
    """Select a recent contiguous window with a final out-of-time BCE suffix."""
    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
    validation = np.floor(totals * validation_fraction).astype(np.int64)
    validation[(totals >= 2) & (validation == 0)] = 1
    if max_validation_targets_per_user:
        validation = np.minimum(validation, max_validation_targets_per_user)

    history_training = totals - validation
    training = history_training.copy()
    if max_train_targets_per_user:
        training = np.minimum(training, max_train_targets_per_user)
    window_starts = retained_target_window_starts(totals, validation, training)
    return TargetPartition(
        window_starts=window_starts,
        training_counts=training,
        validation_counts=validation,
        history_training_counts=history_training,
    )


def load_organic_like_outcomes(
    public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
    """Load mapped organic-like timestamps from public fitting history."""
    prefixes = ("train",) if stage == "validation" else ("train", "val")
    frames = [
        pd.read_parquet(
            public / "data" / f"{prefix}_likes.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic"],
        )
        for prefix in prefixes
    ]
    likes = pd.concat(frames, ignore_index=True)
    likes = likes[likes.is_organic == 1].copy()
    mapped = pd.Index(item_ids).get_indexer(
        likes.item_id.to_numpy(dtype=np.int64)
    )
    likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
    likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
    likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
    nested: dict[int, dict[int, list[int]]] = {}
    for raw_uid, raw_timestamp, raw_item in likes.itertuples(
        index=False, name=None
    ):
        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
            int(raw_timestamp)
        )
        event_features[row, :count] = prepared.event_features[positions]
        activity_features[row, :count] = prepared.activity_features[positions]
        state_ids[row, :count] = prepared.state_ids[positions]
        targets[row, :count] = prepared.sequence_items[target_positions]
        weights[row, :count] = target_weights[target_positions]
        score_from = min(count, max(int(score_starts[row]) - start, 0))
        scored[row, score_from:count] = True
        processed[row] = count
        user_sequence = prepared.sequence_items[
            offset : offset + int(lengths[user_row])
        ]
        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
            padding_item,
        )
    return (
        input_items,
        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
        processed,
        weights,
    )


def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    negatives: torch.Tensor,
    negative_weights: torch.Tensor,
    sample_weights: np.ndarray,
    device: torch.device,
    backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compu…2391 tokens truncated…
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[

def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
        )
        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
        improved = (
            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
                torch.save(
                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,
                        "model_state": current_state,
                        "optimizer_state": optimizer.state_dict(),
                        "generator_state": generator.get_state(),
                    },
                    checkpoint_dir / "best.pt",
                )
        else:
            epochs_without_improvement += 1
        log(
            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
            f"validation_loss={diagnostics['validation_loss']:.5f} "
            f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
            f"train_targets={int(diagnostics['targets']):,} "
            f"validation_targets={int(diagnostics['validation_targets']):,} "
            f"train_seconds={diagnostics['seconds']:.1f} "
            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
        )
        release_accelerator_cache(device)
        if epochs_without_improvement >= config.early_stopping_patience:
            log(
                f"early stopping after epoch={epoch}; "
                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
            )
            break

    if best_state is None:
        raise RuntimeError("training did not produce a valid checkpoint")
    model.load_state_dict(best_state)
    model.to(device).eval()
    log(
        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
        f"loss={best_loss:.6f}"
    )
    return model, history


@torch.inference_mode()
def inference_queries(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
    config = Config(
        bptt_steps=args.bptt_steps,
        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
    )
    target_partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
        print(
            json.dumps(
                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,
        args.ranking_batch_size,
    )
    write_ranking(ranking, args.output.resolve())
    print(
        json.dumps(
            {
                "baseline": "yambda-windowed-sequence-two-tower-base",
                "stage": args.stage,
                "output": str(args.output.resolve()),
                "rows": len(ranking),
                "users": int(ranking.uid.nunique()),
                "runtime_seconds": round(time.monotonic() - started, 3),
                "public_inputs_only": True,
                "architecture": {
                    "family": "pure-windowed-sequence-two-tower",
                    "sequence_training": (
                        "every-adjacent-target-within-recent-chronological-window"
                    ),
                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
                    "bptt_hidden_state": "carried-and-detached",
                    "model_selection": "chronological-validation-BCE-early-stopping",
                    "loss_execution": "gradient-accumulated-microbatches",
                    "user_id_embedding": False,
                    "user_mlp": "582-512-256-192",
                    "item_mlp": "196-512-256-192",
                    "normalization": "none",
                    "score": "raw-dot-product",
                    "objective": "sample-weighted-BCE-at-every-timestep",
                    "negative_sampling": (
                        "24-uniform-plus-8-propensity-corrected-inbatch"
                    ),
                    "false_negative_filter": config.negative_filter_scope,
                    "target_weighting": config.target_weighting,
                },
                "config": asdict(config),
                "sequence_tokens": len(prepared.sequence_items),
                "all_next_item_targets": prepared.target_count,
                "selected_training_targets": int(
                    target_partition.training_counts.sum()
                ),
                "selected_validation_targets": int(
                    target_partition.validation_counts.sum()
                ),
                "discarded_old_targets": int(target_partition.window_starts.sum()),
                "training_history": training_history,
                "embedding_diagnostics": embedding_diagnostics,
                "torch_version": torch.__version__,
                "device": str(device),
            },
            indent=2,
        )
    )
    return 0


if __name__ == "__main__":
    raise SystemExit(main())
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d29e3b0f-5f1f-4f48-8185-f0f07be26b66",
    "process_id": "56872",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '320,460p' /app/solution/two_tower_model.py\nsed -n '540,780p' /app/solution/train_current_model.py\nsed -n '780,940p' /app/solution/train_current_model.py\nsed -n '1080,1380p' /app/solution/train_current_model.py\nsed -n '1520,1790p' /app/solution/train_current_model.py\nsed -n '1920,2165p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '320,460p' /app/solution/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/app/solution/two_tower_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '540,780p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '780,940p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1080,1380p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1520,1790p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1920,2165p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
    "stderr": "",
    "aggregated_output": "            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3471
    },
    "formatted_output": "Warning: truncated output (original token count: 12391)\nTotal output lines: 1333\n\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compu…2391 tokens truncated…\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44747797887d2bb97f4703351aeca",
  "usage": {
    "input_tokens": 27319,
    "cached_input_tokens": 22144,
    "cache_write_input_tokens": 0,
    "output_tokens": 171,
    "reasoning_output_tokens": 9,
    "total_tokens": 27490
  },
  "turn_token_usage": {
    "input_tokens": 123121,
    "cached_input_tokens": 102144,
    "cache_write_input_tokens": 0,
    "output_tokens": 911,
    "reasoning_output_tokens": 105,
    "total_tokens": 124032
  },
  "thread_token_usage": {
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    "cached_input_tokens": 102144,
    "cache_write_input_tokens": 0,
    "output_tokens": 911,
    "reasoning_output_tokens": 105,
    "total_tokens": 124032
  }
}
13

exec command

+00:41
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:1000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 5.0 seconds
Output:

{"chunk_id":"cc4168","wall_time_seconds":5.001538112,"session_id":43565,"original_token_count":45,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\n"}
Harness context token usage record
{
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  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4474e87bc87d2adff5e84268763e2",
  "usage": {
    "input_tokens": 35953,
    "cached_input_tokens": 27136,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 8,
    "total_tokens": 36009
  },
  "turn_token_usage": {
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    "cached_input_tokens": 129280,
    "cache_write_input_tokens": 0,
    "output_tokens": 967,
    "reasoning_output_tokens": 113,
    "total_tokens": 160041
  },
  "thread_token_usage": {
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    "cached_input_tokens": 129280,
    "cache_write_input_tokens": 0,
    "output_tokens": 967,
    "reasoning_output_tokens": 113,
    "total_tokens": 160041
  }
}
14

exec command

+00:50
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b8bc9f","wall_time_seconds":30.00169323,"session_id":43565,"original_token_count":191,"output":"prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\n"}
Harness context token usage record
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  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44756321c87d2b62595ebb64cb992",
  "usage": {
    "input_tokens": 36130,
    "cached_input_tokens": 35712,
    "cache_write_input_tokens": 0,
    "output_tokens": 96,
    "reasoning_output_tokens": 48,
    "total_tokens": 36226
  },
  "turn_token_usage": {
    "input_tokens": 195204,
    "cached_input_tokens": 164992,
    "cache_write_input_tokens": 0,
    "output_tokens": 1063,
    "reasoning_output_tokens": 161,
    "total_tokens": 196267
  },
  "thread_token_usage": {
    "input_tokens": 195204,
    "cached_input_tokens": 164992,
    "cache_write_input_tokens": 0,
    "output_tokens": 1063,
    "reasoning_output_tokens": 161,
    "total_tokens": 196267
  }
}
15

exec command

+01:32
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The baseline prepares about 20.0M sequence tokens but trains on only the most recent 1.05M targets. Its 24 uniform negatives are drawn from all 30,000 tracks, so most are likely trivially irrelevant; only eight negatives come from positive targets, and those are selected randomly rather than by model confusion. I’m checking the score distribution and candidate/popularity structure while the baseline trains, then I’ll test a retrieval-aligned hard-negative sampler using the same two towers.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"sed -n '1180,1380p' /app/solution/train_current_model.py\nsed -n '1380,1520p' /app/solution/train_current_model.py\nsed -n '1840,1925p' /app/solution/train_current_model.py\nsed -n '1,240p' /task/evaluate_public.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":30000}); text(r.output)

Tool response 

Script completed
Wall time 0.0 seconds
Output:

        ).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
        statistics["pair_correct"] += float(
            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
        )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=True,
            )
            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()

            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
        name: value.detach().cpu().clone()
        for name, value in model.state_dict().items()
    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path



        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
    model.eval()
    queries = inference_queries(model, prepared, config, device)
    item_parts: list[torch.Tensor] = []
    for start in range(0, len(prepared.item_ids), 2_048):
        rows = torch.arange(
            start,
            min(start + 2_048, len(prepared.item_ids)),
            dtype=torch.long,
            device=device,
        )
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        scores = queries[start:end] @ items.T
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics


def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")


def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1f352ab5-9995-4b16-ba24-2d235f25b28d",
    "process_id": "91659",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1180,1380p' /app/solution/train_current_model.py\nsed -n '1380,1520p' /app/solution/train_current_model.py\nsed -n '1840,1925p' /app/solution/train_current_model.py\nsed -n '1,240p' /task/evaluate_public.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1180,1380p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1380,1520p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1840,1925p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "stderr": "",
    "aggregated_output": "        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3560
    },
    "formatted_output": "        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
  }
}
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16

exec command

+01:44
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d0109f","wall_time_seconds":30.000533887,"session_id":43565,"original_token_count":55,"output":"inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}
Harness context token usage record
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17

exec command

+02:18
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"3b8e75","wall_time_seconds":30.001163254,"session_id":43565,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
18

exec command

+02:52
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"19842f","wall_time_seconds":30.000924524,"session_id":43565,"original_token_count":0,"output":""}
Harness context token usage record
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}
19

exec command

+03:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current|PID'","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/bin/sh: 1: ps: not found
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-0527571a-4068-4d7b-a136-43a7376b9bb0",
    "process_id": "52531",
    "command": [
      "/bin/sh",
      "-lc",
      "ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current|PID'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -eo pid,etime,pcpu,pmem,rss,cmd | rg 'train_current|PID'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: ps: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 4981
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    "formatted_output": "/bin/sh: 1: ps: not found\n"
  }
}
20

exec command

+03:30
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d0add5","wall_time_seconds":30.002299945,"session_id":43565,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
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    "output_tokens": 64,
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  },
  "turn_token_usage": {
    "input_tokens": 442256,
    "cached_input_tokens": 404864,
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  },
  "thread_token_usage": {
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    "cached_input_tokens": 404864,
    "cache_write_input_tokens": 0,
    "output_tokens": 2030,
    "reasoning_output_tokens": 636,
    "total_tokens": 444286
  }
}
21

exec command

+04:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"sed -n '1110,1200p' /app/solution/train_current_model.py\nsed -n '300,346p' /app/solution/two_tower_model.py\nls -lh /task/data | head -30","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:


def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    negatives: torch.Tensor,
    negative_weights: torch.Tensor,
    sample_weights: np.ndarray,
    device: torch.device,
    backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compute one logical block's exact BCE in bounded-memory slices."""
    target_count = len(positive_items)
    total_weight = float(sample_weights.sum())
    if total_weight <= 0:
        raise RuntimeError("target block has no positive sample weight")
    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
    statistics = {
        "loss_sum": 0.0,
        "positive_sum": 0.0,
        "negative_sum": 0.0,
        "pair_correct": 0.0,
        "weight_sum": 0.0,
    }
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
        selected_states = flat_states[begin:end]
        if backward:
            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
            torch.from_numpy(numeric).to(device),
        )
        positive_vectors, negative_vectors = candidate_vectors(
            model,
            positive_items[begin:end],
            negatives[begin:end],
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
        )
        positive_loss = F.softplus(-positive_logits)
        negative_loss = corrected_negative_loss(
            negative_logits, negative_weights[begin:end], config
        )
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        loss_sum = (
            selected_weights * (positive_loss + negative_loss) / 2.0
        ).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
        statistics["pair_correct"] += float(
            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
        )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
        torch.mps.empty_cache()
            input_size=config.hidden_dim,
            hidden_size=dim,
            num_layers=1,
            batch_first=True,
        )
        self.user_tower = nn.Sequential(
            nn.Linear(dim * 3 + 6, config.user_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_1, config.user_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_2, dim),
        )
        self.candidate_embedding = nn.Embedding(n_items, dim)
        self.register_buffer(
            "item_features", torch.from_numpy(item_features), persistent=True
        )
        self.item_tower = nn.Sequential(
            nn.Linear(dim + n_item_features, config.item_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_1, config.item_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_2, dim),
        )
        nn.init.normal_(self.context_embedding.weight, std=0.02)
        nn.init.normal_(self.candidate_embedding.weight, std=0.02)
        with torch.no_grad():
            self.context_embedding.weight[n_items].zero_()

    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
        mask = items.ne(self.n_items)
        vectors = self.context_embedding(items)
        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
            dim=1, keepdim=True
        ).clamp_min(1)

    def encode_items(self, items: torch.Tensor) -> torch.Tensor:
        inputs = torch.cat(
            [self.candidate_embedding(items), self.item_features[items]], dim=1
        )
        return self.item_tower(inputs)


def invalid_negative_mask(
total 340M
-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 23 21:34 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 23 21:35 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 23 21:34 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 23 21:34 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 23 21:35 validation_preference_state.parquet
Harness context token usage record
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2070d78b-28d1-4f22-a9ab-147b0fdd6a33",
    "process_id": "6195",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1110,1200p' /app/solution/train_current_model.py\nsed -n '300,346p' /app/solution/two_tower_model.py\nls -lh /task/data | head -30"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "sed -n '1110,1200p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '300,346p' /app/solution/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/app/solution/two_tower_model.py"
      },
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 validation_preference_state.parquet\n",
    "stderr": "",
    "aggregated_output": "\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 validation_preference_state.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2931
    },
    "formatted_output": "\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 23 21:34 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:34 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:34 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:34 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:34 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:34 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:34 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:34 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:34 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:34 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:34 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:34 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:34 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:34 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:35 validation_preference_state.parquet\n"
  }
}
22

exec command

+04:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f5280c","wall_time_seconds":30.001973655,"session_id":43565,"original_token_count":0,"output":""}
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23

exec command

+04:46
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output)

Tool response 

Script completed
Wall time 0.2 seconds
Output:

Warning: truncated output (original token count: 4555)
Total output lines: 357

/proc/251 /bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 
/proc/252 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet 
/proc/386 /bin/sh -c for p in /proc/[0-9]*; do tr '\0' ' ' < "$p/cmdline" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' "$p"; tr '\0' ' ' < "$p/cmdline"; printf '\n'; }; done 
/proc/60 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower

You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team'"'"'s next launch gate.

Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.

The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.

## Production Context

The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.

The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.

## What You Have

All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` window. For the final submission, that
validation window becomes observed history, and another 30-minute gap precedes
the hidden seven-day outcome window.

The package includes:

- `/task/current_model/train_current_model.py`: the production training and
  ranking script. It can reproduce the public validation ranking and produce a
  hidden-test submission after refitting.
- `/task/current_model/two_tower_model.py`: model primitives used by the
  production script.
- `/task/evaluate_public.py`: the public validation evaluator for development.
- `/task/data/validation_eligible_candidates.npz` and
  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for
  the validation and hidden-test cutoffs.
- `/task/data/validation_preference_state.parquet` and
  `/task/data/test_preference_state.parquet`: active explicit preference state
  at the corresponding cutoffs.
- `/task/requirements.txt`: the exact offline Python distribution inventory.

You can reproduce the production public backtest with:

```sh
python /task/current_model/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet
python /task/evaluate_public.py /app/solution/production_validation.parquet
```

## Data

For ea…2555 tokens truncated… retrieval-catalog tracks, ordered by
  training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
  before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
  cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
  both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
  cutoff-correct candidate sets for every target user. Each contains aligned
  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
  the Boolean item mask with `np.unpackbits(packed_eligible[r],
  count=len(item_ids), bitorder="little")`.

Validation candidates exclude all `train_*` encounters. Test candidates exclude
all `train_*` and `val_*` encounters. Future test interactions are hidden and
used only by the separate Harbor verifier.

## Discovery Objective

Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:

- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.

The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.

## Deliverable

Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.

The ranking must:

- include the columns `uid`, `item_id`, and `rank`; only these columns are
  evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
  users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
  candidate set.

Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.

## Constraints

- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
  other trials, or host paths.
- Do not install packages or use network access except for the model-provider
  connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
  relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
  model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
  correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
  the deadline.
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Harness context codex item completed
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      "/bin/sh",
      "-lc",
      "for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done"
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        "cmd": "for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done"
      }
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    "stdout": "/proc/251 /bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/252 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/386 /bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done \n/proc/60 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/66 codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n",
    "stderr": "",
    "aggregated_output": "/proc/251 /bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/252 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/386 /bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done \n/proc/60 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/66 codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 4750
    },
    "formatted_output": "/proc/251 /bin/sh -c /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/252 /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet \n/proc/386 /bin/sh -c for p in /proc/[0-9]*; do tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null | rg -q 'train_current_model.py' && { printf '%s ' \"$p\"; tr '\\0' ' ' < \"$p/cmdline\"; printf '\\n'; }; done \n/proc/60 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- '# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team'\"'\"'s next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user'\"'\"'s `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/66 codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=high -- # Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n \n"
  }
}
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exec command

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"awk '{print $14,$15,$23,$24}' /proc/252/stat; sleep 1; awk '{print $14,$15,$23,$24}' /proc/252/stat","workdir":"/app","yield_time_ms":2000,"max_output_tokens":1000}); text(r.output)

Tool response 

Script completed
Wall time 1.1 seconds
Output:

109174 9454 7669645312 1327112
109608 9488 7866355712 1351482
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
    "id": "exec-56fcb314-2d2b-4762-a453-c82f92df07f2",
    "process_id": "81934",
    "command": [
      "/bin/sh",
      "-lc",
      "awk '{print $14,$15,$23,$24}' /proc/252/stat; sleep 1; awk '{print $14,$15,$23,$24}' /proc/252/stat"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "awk '{print $14,$15,$23,$24}' /proc/252/stat; sleep 1; awk '{print $14,$15,$23,$24}' /proc/252/stat"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "109608 9488 7866355712 1351482\n",
    "stderr": "",
    "aggregated_output": "109608 9488 7866355712 1351482\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 860382131
    },
    "formatted_output": "109608 9488 7866355712 1351482\n"
  }
}
25

exec command

+05:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\np=Path('/task/data/validation_eligible_candidates.npz')\na=np.load(p)\nuids=a['uids'].astype('int64'); items=a['item_ids'].astype('int64'); packed=a['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n    m=np.unpackbits(packed[r], count=len(items), bitorder='little').astype(bool)\n    idx=np.flatnonzero(m)[:100]\n    rows.extend((int(u),int(items[i]),j+1) for j,i in enumerate(idx))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"e59cbd","wall_time_seconds":1.000571399,"session_id":30946,"original_token_count":0,"output":""}
Harness context token usage record
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4484e678487d2a100bcb1f0747767",
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}
26

exec command

+05:18
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:30946,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 4.8 seconds
Output:

{"chunk_id":"28a1a9","wall_time_seconds":4.793243082,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"}
Harness context token usage record
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  "turn_token_usage": {
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  },
  "thread_token_usage": {
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Harness context codex item completed
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    "type": "CommandExecution",
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    "process_id": "30946",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\np=Path('/task/data/validation_eligible_candidates.npz')\na=np.load(p)\nuids=a['uids'].astype('int64'); items=a['item_ids'].astype('int64'); packed=a['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n    m=np.unpackbits(packed[r], count=len(items), bitorder='little').astype(bool)\n    idx=np.flatnonzero(m)[:100]\n    rows.extend((int(u),int(items[i]),j+1) for j,i in enumerate(idx))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport numpy as np, pandas as pd\np=Path('/task/data/validation_eligible_candidates.npz')\na=np.load(p)\nuids=a['uids'].astype('int64'); items=a['item_ids'].astype('int64'); packed=a['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n    m=np.unpackbits(packed[r], count=len(items), bitorder='little').astype(bool)\n    idx=np.flatnonzero(m)[:100]\n    rows.extend((int(u),int(items[i]),j+1) for j,i in enumerate(idx))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/popularity_validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/popularity_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 381242528
    },
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  }
}
27

exec command

+05:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f01b15","wall_time_seconds":30.001415268,"session_id":43565,"original_token_count":81,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.7s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=15.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"}
Harness context token usage record
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28

exec command

+05:59
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ca77d3","wall_time_seconds":30.001001315,"session_id":43565,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
29

exec command

+06:33
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:43565,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 9.3 seconds
Output:

{"chunk_id":"13ade7","wall_time_seconds":9.27611128,"exit_code":0,"original_token_count":803,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 372.585,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5398380465508344,\n      \"positive_logit\": 0.7427799573520816,\n      \"negative_logit\": -0.6222247078543962,\n      \"pair_accuracy\": 0.7547733315257186,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.93376620200002,\n      \"validation_loss\": 0.4966362161510468,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.579428466999957\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497010231018,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.7s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=15.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 372.585,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5398380465508344,\n      \"positive_logit\": 0.7427799573520816,\n      \"negative_logit\": -0.6222247078543962,\n      \"pair_accuracy\": 0.7547733315257186,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.93376620200002,\n      \"validation_loss\": 0.4966362161510468,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.579428466999957\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497010231018,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.7s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=15.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 372.585,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5398380465508344,\n      \"positive_logit\": 0.7427799573520816,\n      \"negative_logit\": -0.6222247078543962,\n      \"pair_accuracy\": 0.7547733315257186,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.93376620200002,\n      \"validation_loss\": 0.4966362161510468,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.579428466999957\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497010231018,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 376,
      "nanos": 860434307
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.7s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.9 validation_seconds=15.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 372.585,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5398380465508344,\n      \"positive_logit\": 0.7427799573520816,\n      \"negative_logit\": -0.6222247078543962,\n      \"pair_accuracy\": 0.7547733315257186,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.93376620200002,\n      \"validation_loss\": 0.4966362161510468,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.579428466999957\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.603016972541809,\n    \"query_norm_std\": 0.7697497010231018,\n    \"item_norm_mean\": 1.7570281028747559,\n    \"item_norm_std\": 0.8279485702514648\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
30

exec command

+06:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"819fbf","wall_time_seconds":1.000491792,"session_id":88708,"original_token_count":0,"output":""}
Harness context token usage record
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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Harness context codex item completed
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    "parsed_cmd": [
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        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/production_validation.parquet"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:88708,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"f37444","wall_time_seconds":0.00000286,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n"}
Harness context token usage record
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  }
}
31

exec command

+07:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Baseline public NDCG@10 is 0.021280 (versus 0.010710 for catalog popularity), so the learned personalization is meaningful. I’m now creating a valid baseline hidden-stage submission as a safety artifact while caching the train+validation preparation; that cache will make the final improved refit cheaper. Then I’ll compare the hard-negative objective against 0.021280.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --output /app/solution/submission.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"5db10a","wall_time_seconds":1.000733116,"session_id":22850,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
32

Agent message

+07:25
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

33

Agent message

+07:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

34

exec command

+07:49
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 3\n+    hard_negatives: int = 16\n@@\n def draw_full_history_filtered_negatives(\n@@\n-    uniform = torch.randint(\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n+    uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n@@\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n@@\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n@@\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n@@\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n@@\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n@@\n     return base.draw_filtered_mixed_negatives(\n@@\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n@@\n def corrected_negative_loss(\n@@\n     return result / total\n+\n+\n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus an unbiased random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered.  Mining only\n+    changes the training loss: serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n*** Update File: /app/solution/train_current_model.py\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n+            negative_logits, negative_weights[begin:end], config\n+        )\n         positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n-        )\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n*** Update File: /app/solution/train_current_model.py\n@@\n     parser.add_argument(\n         \"--negative-filter-scope\",\n@@\n     )\n+    parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n@@\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script failed
Wall time 0.0 seconds
Output:

Script error:
apply_patch verification failed: invalid patch: multiple operations target /app/solution/train_current_model.py
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35

exec command

+09:07
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 3\n+    hard_negatives: int = 16\n@@\n-    uniform = torch.randint(\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n+    uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n@@\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n@@\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n@@\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n@@\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n@@\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n@@\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n@@\n def corrected_negative_loss(\n@@\n     return result / total\n+\n+\n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n+            negative_logits, negative_weights[begin:end], config\n+        )\n         positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n-        )\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n@@\n     parser.add_argument(\n         \"--negative-filter-scope\",\n@@\n         default=Config.negative_filter_scope,\n     )\n+    parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n@@\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script failed
Wall time 0.1 seconds
Output:

Script error:
Exit code: 1
Wall time: 0 seconds
Output:
Failed to write file /app/solution/train_current_model.py
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-460a4ad9-d600-4064-956f-40342553a5b1",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -57,2 +57,4 @@\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 3\n+    hard_negatives: int = 16\n     inbatch_correction_power: float = 0.5\n@@ -644,5 +646,7 @@\n     batch_size = len(targets_cpu)\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n         generator=generator,\n@@ -652,6 +656,6 @@\n     corrected_count = 0\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n         unique_targets = torch.unique(targets.detach().cpu())\n         corrected_count = min(\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n         )\n@@ -671,6 +675,6 @@\n             inbatch = unique_targets[columns]\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n             filler = torch.randint(\n                 n_items,\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n                 generator=generator,\n@@ -684,3 +688,3 @@\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n@@ -754,4 +758,4 @@\n         len(prepared.item_ids),\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n         item_correction,\n@@ -866,2 +870,30 @@\n \n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n+\n+\n def retained_target_window_starts(\n@@ -1170,6 +1202,11 @@\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n             negative_logits, negative_weights[begin:end], config\n         )\n+        positive_loss = F.softplus(-positive_logits)\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n         selected_weights = torch.from_numpy(\n@@ -1948,2 +1985,14 @@\n     parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n+    parser.add_argument(\n         \"--target-weighting\",\n@@ -2020,2 +2069,5 @@\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n     ):\n@@ -2033,2 +2085,4 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "failed",
    "stdout": "",
    "stderr": "Failed to write file /app/solution/train_current_model.py\n"
  }
}
36

exec command

+09:09
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"ls -l /app/solution/train_current_model.py /app/solution && df -h /app","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output)

Tool response 

Script completed
Wall time 0.0 seconds
Output:

-r--r--r-- 1 agent agent 80058 Sep 23 21:40 /app/solution/train_current_model.py

/app/solution:
total 541764
-rw-r--r-- 1 agent agent    728599 Sep 23 21:45 popularity_validation.parquet
-rw-r--r-- 1 agent agent   1244228 Sep 23 21:46 production_validation.parquet
-rw-r--r-- 1 agent agent 552686232 Sep 23 21:47 submission_prepared.npz
-r--r--r-- 1 agent agent     80058 Sep 23 21:40 train_current_model.py
-r--r--r-- 1 agent agent     15985 Sep 23 21:40 two_tower_model.py
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   15G   82G  16% /
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    "stderr": "",
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37

exec command

+09:17
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:
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Harness context codex item completed
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Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 3\n+    hard_negatives: int = 16\n@@\n-    uniform = torch.randint(\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n+    uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n@@\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n@@\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n@@\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n@@\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n@@\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n@@\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n@@\n def corrected_negative_loss(\n@@\n     return result / total\n+\n+\n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n@@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n+            negative_logits, negative_weights[begin:end], config\n+        )\n         positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n-        )\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n@@\n     parser.add_argument(\n         \"--negative-filter-scope\",\n@@\n         default=Config.negative_filter_scope,\n     )\n+    parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n@@\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n@@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-65ceb9ba-9354-4cf2-9744-e1bfa1860155",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -57,2 +57,4 @@\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 3\n+    hard_negatives: int = 16\n     inbatch_correction_power: float = 0.5\n@@ -644,5 +646,7 @@\n     batch_size = len(targets_cpu)\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n         generator=generator,\n@@ -652,6 +656,6 @@\n     corrected_count = 0\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n         unique_targets = torch.unique(targets.detach().cpu())\n         corrected_count = min(\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n         )\n@@ -671,6 +675,6 @@\n             inbatch = unique_targets[columns]\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n             filler = torch.randint(\n                 n_items,\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n                 generator=generator,\n@@ -684,3 +688,3 @@\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n@@ -754,4 +758,4 @@\n         len(prepared.item_ids),\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n         item_correction,\n@@ -866,2 +870,30 @@\n \n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n+\n+\n def retained_target_window_starts(\n@@ -1170,6 +1202,11 @@\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n             negative_logits, negative_weights[begin:end], config\n         )\n+        positive_loss = F.softplus(-positive_logits)\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n         selected_weights = torch.from_numpy(\n@@ -1948,2 +1985,14 @@\n     parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n+    parser.add_argument(\n         \"--target-weighting\",\n@@ -2020,2 +2069,5 @@\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n     ):\n@@ -2033,2 +2085,4 @@\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n         negative_filter_scope=args.negative_filter_scope,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}
38

exec command

+10:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"sed -n '465,490p' /app/solution/train_current_model.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\nrg -n \"negative_pool|hard_negative|negative_sampling\" /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(r.output)

Tool response 

Script completed
Wall time 0.2 seconds
Output:

        f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
        f"tokens={len(prepared.sequence_items):,} "
        f"next_item_targets={prepared.target_count:,} "
        f"states={len(prepared.liked_states):,}"
    )
    return prepared, eligibility


def cache_metadata(stage: str, config: Config) -> dict[str, object]:
    return {
        "cache_version": CACHE_VERSION,
        "stage": stage,
        "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
        "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
        "event_time_feature": "exp-negative-gap-over-60-days",
    }


def save_cache(
    path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    np.savez(
        path,
        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
        **prepared.__dict__,
58:    negative_pool_multiplier: int = 3
59:    hard_negatives: int = 16
647:    uniform_count = config.uniform_negatives * config.negative_pool_multiplier
648:    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier
759:        config.uniform_negatives * config.negative_pool_multiplier,
760:        config.inbatch_negatives * config.negative_pool_multiplier,
871:def select_mixed_hard_negatives(
884:    hard_count = min(config.hard_negatives, retained)
1203:        negative_logits, selected_negative_weights = select_mixed_hard_negatives(
1988:        default=Config.negative_pool_multiplier,
1994:        default=Config.hard_negatives,
2070:        or args.negative_pool_multiplier < 1
2071:        or args.hard_negatives < 0
2072:        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives
2086:        negative_pool_multiplier=args.negative_pool_multiplier,
2087:        hard_negatives=args.hard_negatives,
2190:                    "negative_sampling": (
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Harness context codex item completed
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    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "sed -n '465,490p' /app/solution/train_current_model.py\n/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\nrg -n \"negative_pool|hard_negative|negative_sampling\" /app/solution/train_current_model.py"
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      }
    ],
    "source": "unified_exec_startup",
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    "stdout": "58:    negative_pool_multiplier: int = 3\n59:    hard_negatives: int = 16\n647:    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n648:    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n759:        config.uniform_negatives * config.negative_pool_multiplier,\n760:        config.inbatch_negatives * config.negative_pool_multiplier,\n871:def select_mixed_hard_negatives(\n884:    hard_count = min(config.hard_negatives, retained)\n1203:        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n1988:        default=Config.negative_pool_multiplier,\n1994:        default=Config.hard_negatives,\n2070:        or args.negative_pool_multiplier < 1\n2071:        or args.hard_negatives < 0\n2072:        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n2086:        negative_pool_multiplier=args.negative_pool_multiplier,\n2087:        hard_negatives=args.hard_negatives,\n2190:                    \"negative_sampling\": (\n",
    "stderr": "",
    "aggregated_output": "58:    negative_pool_multiplier: int = 3\n59:    hard_negatives: int = 16\n647:    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n648:    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n759:        config.uniform_negatives * config.negative_pool_multiplier,\n760:        config.inbatch_negatives * config.negative_pool_multiplier,\n871:def select_mixed_hard_negatives(\n884:    hard_count = min(config.hard_negatives, retained)\n1203:        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n1988:        default=Config.negative_pool_multiplier,\n1994:        default=Config.hard_negatives,\n2070:        or args.negative_pool_multiplier < 1\n2071:        or args.hard_negatives < 0\n2072:        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n2086:        negative_pool_multiplier=args.negative_pool_multiplier,\n2087:        hard_negatives=args.hard_negatives,\n2190:                    \"negative_sampling\": (\n",
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      "nanos": 27619445
    },
    "formatted_output": "58:    negative_pool_multiplier: int = 3\n59:    hard_negatives: int = 16\n647:    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n648:    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n759:        config.uniform_negatives * config.negative_pool_multiplier,\n760:        config.inbatch_negatives * config.negative_pool_multiplier,\n871:def select_mixed_hard_negatives(\n884:    hard_count = min(config.hard_negatives, retained)\n1203:        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n1988:        default=Config.negative_pool_multiplier,\n1994:        default=Config.hard_negatives,\n2070:        or args.negative_pool_multiplier < 1\n2071:        or args.hard_negatives < 0\n2072:        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n2086:        negative_pool_multiplier=args.negative_pool_multiplier,\n2087:        hard_negatives=args.hard_negatives,\n2190:                    \"negative_sampling\": (\n"
  }
}
39

exec command

+10:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:22850,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c479f4","wall_time_seconds":30.000989943,"session_id":22850,"original_token_count":310,"output":"loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.2s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.6s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=16.0s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.8s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.9s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=43.3s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=48.2s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"}
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40

exec command

+10:47
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nimport evaluate_public as ev\nfrom ranking_evaluation import CandidateSet, ranked_items\nD='/task/data'\ntargets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\nelig=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz'))\ng=ev.validation_truth(elig,targets)\ncat=pd.read_parquet(D+'/candidate_catalog.parquet')\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nallr=np.array([rank[i] for x in g.values() for i in x])\nlikes=np.array([rank[i] for x in g.values() for i,v in x.items() if v==3])\nprint('relevant',len(allr),'rank quantiles',np.quantile(allr,[0,.1,.25,.5,.75,.9,1]))\nprint('likes',len(likes),'rank quantiles',np.quantile(likes,[0,.1,.25,.5,.75,.9,1]) if len(likes) else [])\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nrr={int(u): list(z.item_id.astype(int)) for u,z in r.groupby('uid',sort=False)}\nh=[]\nfor u,t in g.items():\n  for i,v in t.items():\n    if i in rr[u][:10]: h.append((rr[u].index(i)+1,v,rank[i]))\nprint('top10 hits',len(h),'likes',sum(v==3 for _,v,_ in h),'hit popularity median',np.median([x[2] for x in h]) if h else None)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"58934f","wall_time_seconds":1.000877262,"session_id":1801,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-b1796d1b-8202-4716-80b1-2788aacf3055",
    "process_id": "1801",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nimport evaluate_public as ev\nfrom ranking_evaluation import CandidateSet, ranked_items\nD='/task/data'\ntargets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\nelig=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz'))\ng=ev.validation_truth(elig,targets)\ncat=pd.read_parquet(D+'/candidate_catalog.parquet')\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nallr=np.array([rank[i] for x in g.values() for i in x])\nlikes=np.array([rank[i] for x in g.values() for i,v in x.items() if v==3])\nprint('relevant',len(allr),'rank quantiles',np.quantile(allr,[0,.1,.25,.5,.75,.9,1]))\nprint('likes',len(likes),'rank quantiles',np.quantile(likes,[0,.1,.25,.5,.75,.9,1]) if len(likes) else [])\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nrr={int(u): list(z.item_id.astype(int)) for u,z in r.groupby('uid',sort=False)}\nh=[]\nfor u,t in g.items():\n  for i,v in t.items():\n    if i in rr[u][:10]: h.append((rr[u].index(i)+1,v,rank[i]))\nprint('top10 hits',len(h),'likes',sum(v==3 for _,v,_ in h),'hit popularity median',np.median([x[2] for x in h]) if h else None)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nimport evaluate_public as ev\nfrom ranking_evaluation import CandidateSet, ranked_items\nD='/task/data'\ntargets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int))\nelig=CandidateSet(__import__('pathlib').Path(D+'/validation_eligible_candidates.npz'))\ng=ev.validation_truth(elig,targets)\ncat=pd.read_parquet(D+'/candidate_catalog.parquet')\nrank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nallr=np.array([rank[i] for x in g.values() for i in x])\nlikes=np.array([rank[i] for x in g.values() for i,v in x.items() if v==3])\nprint('relevant',len(allr),'rank quantiles',np.quantile(allr,[0,.1,.25,.5,.75,.9,1]))\nprint('likes',len(likes),'rank quantiles',np.quantile(likes,[0,.1,.25,.5,.75,.9,1]) if len(likes) else [])\nr=pd.read_parquet('/app/solution/production_validation.parquet').sort_values(['uid','rank'])\nrr={int(u): list(z.item_id.astype(int)) for u,z in r.groupby('uid',sort=False)}\nh=[]\nfor u,t in g.items():\n  for i,v in t.items():\n    if i in rr[u][:10]: h.append((rr[u].index(i)+1,v,rank[i]))\nprint('top10 hits',len(h),'likes',sum(v==3 for _,v,_ in h),'hit popularity median',np.median([x[2] for x in h]) if h else None)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "relevant 37769 rank quantiles [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04\n 3.00000e+04]\nlikes 4417 rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04\n 2.99890e+04]\ntop10 hits 644 likes 80 hit popularity median 138.0\n",
    "stderr": "",
    "aggregated_output": "relevant 37769 rank quantiles [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04\n 3.00000e+04]\nlikes 4417 rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04\n 2.99890e+04]\ntop10 hits 644 likes 80 hit popularity median 138.0\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 902566466
    },
    "formatted_output": "relevant 37769 rank quantiles [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04\n 3.00000e+04]\nlikes 4417 rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04\n 2.99890e+04]\ntop10 hits 644 likes 80 hit popularity median 138.0\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:1801,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"ab86a8","wall_time_seconds":0.00000194,"exit_code":0,"original_token_count":71,"output":"relevant 37769 rank quantiles [1.00000e+00 4.28000e+02 1.73300e+03 5.62200e+03 1.33740e+04 2.16802e+04\n 3.00000e+04]\nlikes 4417 rank quantiles [2.00000e+00 6.18000e+02 2.10700e+03 6.37100e+03 1.41810e+04 2.18018e+04\n 2.99890e+04]\ntop10 hits 644 likes 80 hit popularity median 138.0\n"}
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41

Agent message

+11:25
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

42

exec command

+11:30
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); m=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little')\n n=m.sum(1)\n print(s, len(n), n.mean(), np.quantile(n,[0,.1,.5,.9,1]))\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"486d8e","wall_time_seconds":1.000740582,"session_id":40507,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-52e9b728-af6e-4767-971d-66e098aae973",
    "process_id": "40507",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); m=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little')\n n=m.sum(1)\n print(s, len(n), n.mean(), np.quantile(n,[0,.1,.5,.9,1]))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np\nfor s in ['validation','test']:\n a=np.load('/task/data/'+s+'_eligible_candidates.npz'); m=np.unpackbits(a['packed_eligible'],axis=1,count=len(a['item_ids']),bitorder='little')\n n=m.sum(1)\n print(s, len(n), n.mean(), np.quantile(n,[0,.1,.5,.9,1]))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "validation 8456 29067.306291390727 [24657.  28038.5 29242.  29852.  30000. ]\ntest 8456 29041.3178807947 [24552. 28001. 29220. 29835. 30000.]\n",
    "stderr": "",
    "aggregated_output": "validation 8456 29067.306291390727 [24657.  28038.5 29242.  29852.  30000. ]\ntest 8456 29041.3178807947 [24552. 28001. 29220. 29835. 30000.]\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
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    },
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:40507,chars:"",yield_time_ms:1000,max_output_tokens:2000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"05366a","wall_time_seconds":0.00000322,"exit_code":0,"original_token_count":36,"output":"validation 8456 29067.306291390727 [24657.  28038.5 29242.  29852.  30000. ]\ntest 8456 29041.3178807947 [24552. 28001. 29220. 29835. 30000.]\n"}
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43

exec command

+11:56
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-def build_known_positive_filter(\n-    prepared: SequenceData, training_counts: np.ndarray\n+def build_known_positive_filter(\n+    prepared: SequenceData,\n+    training_counts: np.ndarray,\n+    eligibility: CandidateSet,\n ) -> np.ndarray:\n-    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n+    \"\"\"Pack negatives that cannot occur in the stage's retrieval candidate set.\n+\n+    For target users the platform artifact is authoritative and excludes every\n+    prior encounter, not merely >=50% listens. Other training users fall back\n+    to the complete positive-listen prefix.\n+    \"\"\"\n@@\n-    log(\n-        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n-        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n-    )\n+    eligible_rows = pd.Index(eligibility.uids).get_indexer(prepared.user_ids)\n+    matched = eligible_rows >= 0\n+    packed[matched] = np.bitwise_not(eligibility.packed[eligible_rows[matched]])\n+    log(\n+        f\"candidate-aware negative filter: users={len(prepared.user_ids):,} \"\n+        f\"target_users={int(matched.sum()):,} fallback_positive_items={retained:,} \"\n+        f\"bytes={packed.nbytes:,}\"\n+    )\n@@\n def train_model(\n@@\n     prepared: SequenceData,\n+    eligibility: CandidateSet,\n@@\n-        build_known_positive_filter(prepared, partition.history_training_counts)\n+        build_known_positive_filter(\n+            prepared, partition.history_training_counts, eligibility\n+        )\n@@\n     model, training_history = train_model(\n         public,\n         args.stage,\n         prepared,\n+        eligibility,\n         config,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-f1e0ad9f-4997-4a43-b08e-3212644e7577",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -580,5 +580,12 @@\n def build_known_positive_filter(\n-    prepared: SequenceData, training_counts: np.ndarray\n+    prepared: SequenceData,\n+    training_counts: np.ndarray,\n+    eligibility: CandidateSet,\n ) -> np.ndarray:\n-    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n+    \"\"\"Pack negatives that cannot occur in the stage's retrieval candidate set.\n+\n+    For target users the platform artifact is authoritative and excludes every\n+    prior encounter, not merely >=50% listens. Other training users fall back\n+    to the complete positive-listen prefix.\n+    \"\"\"\n     width = (len(prepared.item_ids) + 7) // 8\n@@ -603,5 +610,9 @@\n         retained += len(items)\n+    eligible_rows = pd.Index(eligibility.uids).get_indexer(prepared.user_ids)\n+    matched = eligible_rows >= 0\n+    packed[matched] = np.bitwise_not(eligibility.packed[eligible_rows[matched]])\n     log(\n-        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n-        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n+        f\"candidate-aware negative filter: users={len(prepared.user_ids):,} \"\n+        f\"target_users={int(matched.sum()):,} fallback_positive_items={retained:,} \"\n+        f\"bytes={packed.nbytes:,}\"\n     )\n@@ -1561,2 +1572,3 @@\n     prepared: SequenceData,\n+    eligibility: CandidateSet,\n     config: Config,\n@@ -1602,3 +1614,5 @@\n     known_positive_packed = (\n-        build_known_positive_filter(prepared, partition.history_training_counts)\n+        build_known_positive_filter(\n+            prepared, partition.history_training_counts, eligibility\n+        )\n         if config.negative_filter_scope == \"full-training-history\"\n@@ -2150,2 +2164,3 @@\n         prepared,\n+        eligibility,\n         config,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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44

exec command

+12:13
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && sed -n '1540,1585p' /app/solution/train_current_model.py && sed -n '2150,2175p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000}); text(r.output)

Tool response 

Script completed
Wall time 0.4 seconds
Output:

    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path


def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        eligibility,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-44181b5a-611d-4392-8828-5605a7e07d8b",
    "process_id": "96319",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && sed -n '1540,1585p' /app/solution/train_current_model.py && sed -n '2150,2175p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && sed -n '1540,1585p' /app/solution/train_current_model.py && sed -n '2150,2175p' /app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n",
    "stderr": "",
    "aggregated_output": "    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 144883895
    },
    "formatted_output": "    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n"
  }
}
45

exec command

+12:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:22850,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"eb674e","wall_time_seconds":30.000783501,"session_id":22850,"original_token_count":81,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=204.4s\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=220.0 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\n"}
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}
46

exec command

+12:52
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:22850,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"7523e5","wall_time_seconds":30.000872666,"session_id":22850,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:22850,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.9 seconds
Output:

{"chunk_id":"313a3e","wall_time_seconds":0.885623248,"exit_code":0,"original_token_count":800,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 377.702,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531180707777,\n      \"positive_logit\": 0.7500169969320248,\n      \"negative_logit\": -0.6300986805672104,\n      \"pair_accuracy\": 0.7552423365923384,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.0365495410001,\n      \"validation_loss\": 0.4979486985899121,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 16.415424950999977\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269155502319,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}
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Harness context codex item completed
{
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      }
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    "source": "unified_exec_startup",
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    "stdout": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.2s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.6s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=16.0s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.8s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.9s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=43.3s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=48.2s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=204.4s\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=220.0 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 377.702,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531180707777,\n      \"positive_logit\": 0.7500169969320248,\n      \"negative_logit\": -0.6300986805672104,\n      \"pair_accuracy\": 0.7552423365923384,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.0365495410001,\n      \"validation_loss\": 0.4979486985899121,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 16.415424950999977\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269155502319,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.2s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.6s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=16.0s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.8s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.9s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=43.3s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=48.2s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=204.4s\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=220.0 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 377.702,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531180707777,\n      \"positive_logit\": 0.7500169969320248,\n      \"negative_logit\": -0.6300986805672104,\n      \"pair_accuracy\": 0.7552423365923384,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.0365495410001,\n      \"validation_loss\": 0.4979486985899121,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 16.415424950999977\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269155502319,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 382,
      "nanos": 36359125
    },
    "formatted_output": "loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.2s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.6s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=16.0s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.8s\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.9s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.9s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=43.3s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=48.2s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=204.4s\nepoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=220.0 validation_seconds=16.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 377.702,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5383531180707777,\n      \"positive_logit\": 0.7500169969320248,\n      \"negative_logit\": -0.6300986805672104,\n      \"pair_accuracy\": 0.7552423365923384,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 220.0365495410001,\n      \"validation_loss\": 0.4979486985899121,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 16.415424950999977\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.651851773262024,\n    \"query_norm_std\": 0.8145269155502319,\n    \"item_norm_mean\": 1.9033515453338623,\n    \"item_norm_std\": 0.9598208665847778\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
47

exec command

+13:31
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

A valid baseline submission.parquet is now safely in place. The improved experiment combines two objective fixes: negatives for target users are restricted to the actual stage-eligible pool (so training does not spend capacity on tracks the platform will filter), and each target draws 96 filtered proposals, retaining 16 model-hard plus 16 random negatives. The random half limits overfitting to mislabeled/unobserved positives. I’m running that exact objective on the public cutoff now.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/hard_validation_checkpoints --output /app/solution/hard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"623fcf","wall_time_seconds":1.00047668,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
48

exec command

+13:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e41e5b","wall_time_seconds":30.001229926,"session_id":19025,"original_token_count":96,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\n"}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"738482","wall_time_seconds":30.00090503,"session_id":19025,"original_token_count":220,"output":"prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\n"}
Harness context token usage record
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}
49

exec command

+14:48
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"fbb640","wall_time_seconds":30.000683283,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
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  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44a9d04e487d28d2f341d27d1cc6b",
  "usage": {
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  "thread_token_usage": {
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"0fc53e","wall_time_seconds":30.001304292,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44abe83b487d2926e414824ac48ef",
  "usage": {
    "input_tokens": 62593,
    "cached_input_tokens": 62336,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 62639
  },
  "turn_token_usage": {
    "input_tokens": 2216288,
    "cached_input_tokens": 2152832,
    "cache_write_input_tokens": 0,
    "output_tokens": 12370,
    "reasoning_output_tokens": 4299,
    "total_tokens": 2228658
  },
  "thread_token_usage": {
    "input_tokens": 2216288,
    "cached_input_tokens": 2152832,
    "cache_write_input_tokens": 0,
    "output_tokens": 12370,
    "reasoning_output_tokens": 4299,
    "total_tokens": 2228658
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"208eb9","wall_time_seconds":30.000584173,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44adefcf887d2a95576fa068b6d34",
  "usage": {
    "input_tokens": 62697,
    "cached_input_tokens": 62464,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 62743
  },
  "turn_token_usage": {
    "input_tokens": 2278985,
    "cached_input_tokens": 2215296,
    "cache_write_input_tokens": 0,
    "output_tokens": 12416,
    "reasoning_output_tokens": 4299,
    "total_tokens": 2291401
  },
  "thread_token_usage": {
    "input_tokens": 2278985,
    "cached_input_tokens": 2215296,
    "cache_write_input_tokens": 0,
    "output_tokens": 12416,
    "reasoning_output_tokens": 4299,
    "total_tokens": 2291401
  }
}
50

exec command

+16:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"060f52","wall_time_seconds":30.001263227,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44aff8cb487d2afb7209540492408",
  "usage": {
    "input_tokens": 62800,
    "cached_input_tokens": 62464,
    "cache_write_input_tokens": 0,
    "output_tokens": 61,
    "reasoning_output_tokens": 13,
    "total_tokens": 62861
  },
  "turn_token_usage": {
    "input_tokens": 2341785,
    "cached_input_tokens": 2277760,
    "cache_write_input_tokens": 0,
    "output_tokens": 12477,
    "reasoning_output_tokens": 4312,
    "total_tokens": 2354262
  },
  "thread_token_usage": {
    "input_tokens": 2341785,
    "cached_input_tokens": 2277760,
    "cache_write_input_tokens": 0,
    "output_tokens": 12477,
    "reasoning_output_tokens": 4312,
    "total_tokens": 2354262
  }
}
51

exec command

+16:59
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"for p in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$c\" in *hard_validation.parquet*) awk '{print FILENAME,$14,$15,$23,$24}' \"$p/stat\";; esac; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000}); text(r.output)

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/proc/668/stat 0 0 2654208 359
/proc/669/stat 66288 8764 6972067840 1147530
/proc/735/stat 0 0 2654208 391
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-12cea2f0-cf8a-4b57-b2c9-08eead2f647c",
    "process_id": "18246",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$c\" in *hard_validation.parquet*) awk '{print FILENAME,$14,$15,$23,$24}' \"$p/stat\";; esac; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in /proc/[0-9]*; do c=$(tr '\\0' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$c\" in *hard_validation.parquet*) awk '{print FILENAME,$14,$15,$23,$24}' \"$p/stat\";; esac; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/proc/668/stat 0 0 2654208 359\n/proc/669/stat 66288 8764 6972067840 1147530\n/proc/735/stat 0 0 2654208 391\n",
    "stderr": "",
    "aggregated_output": "/proc/668/stat 0 0 2654208 359\n/proc/669/stat 66288 8764 6972067840 1147530\n/proc/735/stat 0 0 2654208 391\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4480
    },
    "formatted_output": "/proc/668/stat 0 0 2654208 359\n/proc/669/stat 66288 8764 6972067840 1147530\n/proc/735/stat 0 0 2654208 391\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44b20a70c87d280db36fa0ae88608",
  "usage": {
    "input_tokens": 62918,
    "cached_input_tokens": 62592,
    "cache_write_input_tokens": 0,
    "output_tokens": 123,
    "reasoning_output_tokens": 9,
    "total_tokens": 63041
  },
  "turn_token_usage": {
    "input_tokens": 2404703,
    "cached_input_tokens": 2340352,
    "cache_write_input_tokens": 0,
    "output_tokens": 12600,
    "reasoning_output_tokens": 4321,
    "total_tokens": 2417303
  },
  "thread_token_usage": {
    "input_tokens": 2404703,
    "cached_input_tokens": 2340352,
    "cache_write_input_tokens": 0,
    "output_tokens": 12600,
    "reasoning_output_tokens": 4321,
    "total_tokens": 2417303
  }
}
52

exec command

+17:05
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"86e523","wall_time_seconds":30.001853277,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44b25d90c87d28c4e8d76f9dde1c5",
  "usage": {
    "input_tokens": 63116,
    "cached_input_tokens": 62720,
    "cache_write_input_tokens": 0,
    "output_tokens": 67,
    "reasoning_output_tokens": 19,
    "total_tokens": 63183
  },
  "turn_token_usage": {
    "input_tokens": 2467819,
    "cached_input_tokens": 2403072,
    "cache_write_input_tokens": 0,
    "output_tokens": 12667,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2480486
  },
  "thread_token_usage": {
    "input_tokens": 2467819,
    "cached_input_tokens": 2403072,
    "cache_write_input_tokens": 0,
    "output_tokens": 12667,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2480486
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ff3235","wall_time_seconds":30.000874812,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44b4740b887d280ba86dbd676a766",
  "usage": {
    "input_tokens": 63240,
    "cached_input_tokens": 62976,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 63286
  },
  "turn_token_usage": {
    "input_tokens": 2531059,
    "cached_input_tokens": 2466048,
    "cache_write_input_tokens": 0,
    "output_tokens": 12713,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2543772
  },
  "thread_token_usage": {
    "input_tokens": 2531059,
    "cached_input_tokens": 2466048,
    "cache_write_input_tokens": 0,
    "output_tokens": 12713,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2543772
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"a7e1e2","wall_time_seconds":30.00071526,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44b67f73c87d29fe275ae52656394",
  "usage": {
    "input_tokens": 63343,
    "cached_input_tokens": 63104,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 63389
  },
  "turn_token_usage": {
    "input_tokens": 2594402,
    "cached_input_tokens": 2529152,
    "cache_write_input_tokens": 0,
    "output_tokens": 12759,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2607161
  },
  "thread_token_usage": {
    "input_tokens": 2594402,
    "cached_input_tokens": 2529152,
    "cache_write_input_tokens": 0,
    "output_tokens": 12759,
    "reasoning_output_tokens": 4340,
    "total_tokens": 2607161
  }
}
53

exec command

+18:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"700a4a","wall_time_seconds":30.001444317,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44b89194087d2993fa27869f41e9b",
  "usage": {
    "input_tokens": 63449,
    "cached_input_tokens": 63104,
    "cache_write_input_tokens": 0,
    "output_tokens": 63,
    "reasoning_output_tokens": 15,
    "total_tokens": 63512
  },
  "turn_token_usage": {
    "input_tokens": 2657851,
    "cached_input_tokens": 2592256,
    "cache_write_input_tokens": 0,
    "output_tokens": 12822,
    "reasoning_output_tokens": 4355,
    "total_tokens": 2670673
  },
  "thread_token_usage": {
    "input_tokens": 2657851,
    "cached_input_tokens": 2592256,
    "cache_write_input_tokens": 0,
    "output_tokens": 12822,
    "reasoning_output_tokens": 4355,
    "total_tokens": 2670673
  }
}
54

exec command

+19:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f2e9b3","wall_time_seconds":30.001559159,"session_id":19025,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.641615 running_bce=0.626945 elapsed=302.5s\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44babb28887d2bce7ed6d335caf76",
  "usage": {
    "input_tokens": 63570,
    "cached_input_tokens": 63232,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 10,
    "total_tokens": 63628
  },
  "turn_token_usage": {
    "input_tokens": 2721421,
    "cached_input_tokens": 2655488,
    "cache_write_input_tokens": 0,
    "output_tokens": 12880,
    "reasoning_output_tokens": 4365,
    "total_tokens": 2734301
  },
  "thread_token_usage": {
    "input_tokens": 2721421,
    "cached_input_tokens": 2655488,
    "cache_write_input_tokens": 0,
    "output_tokens": 12880,
    "reasoning_output_tokens": 4365,
    "total_tokens": 2734301
  }
}
55

exec command

+19:54
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"38e1c1","wall_time_seconds":30.000805149,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44bcda12087d2a308cca1060f1c98",
  "usage": {
    "input_tokens": 63723,
    "cached_input_tokens": 63360,
    "cache_write_input_tokens": 0,
    "output_tokens": 66,
    "reasoning_output_tokens": 18,
    "total_tokens": 63789
  },
  "turn_token_usage": {
    "input_tokens": 2785144,
    "cached_input_tokens": 2718848,
    "cache_write_input_tokens": 0,
    "output_tokens": 12946,
    "reasoning_output_tokens": 4383,
    "total_tokens": 2798090
  },
  "thread_token_usage": {
    "input_tokens": 2785144,
    "cached_input_tokens": 2718848,
    "cache_write_input_tokens": 0,
    "output_tokens": 12946,
    "reasoning_output_tokens": 4383,
    "total_tokens": 2798090
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"03a0c1","wall_time_seconds":30.000662277,"session_id":19025,"original_token_count":78,"output":"saved epoch checkpoint: /app/solution/hard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.62598 validation_loss=0.60134 pair_accuracy=0.594 train_targets=1,052,953 validation_targets=130,103 train_seconds=322.4 validation_seconds=28.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.601343\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44bf0783887d28f3a8e54fea4889b",
  "usage": {
    "input_tokens": 63848,
    "cached_input_tokens": 63488,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 63894
  },
  "turn_token_usage": {
    "input_tokens": 2848992,
    "cached_input_tokens": 2782336,
    "cache_write_input_tokens": 0,
    "output_tokens": 12992,
    "reasoning_output_tokens": 4383,
    "total_tokens": 2861984
  },
  "thread_token_usage": {
    "input_tokens": 2848992,
    "cached_input_tokens": 2782336,
    "cache_write_input_tokens": 0,
    "output_tokens": 12992,
    "reasoning_output_tokens": 4383,
    "total_tokens": 2861984
  }
}
56

exec command

+21:02
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c712e8","wall_time_seconds":30.000608282,"session_id":19025,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44c12227487d29a1f0356519284ac",
  "usage": {
    "input_tokens": 64042,
    "cached_input_tokens": 63616,
    "cache_write_input_tokens": 0,
    "output_tokens": 57,
    "reasoning_output_tokens": 9,
    "total_tokens": 64099
  },
  "turn_token_usage": {
    "input_tokens": 2913034,
    "cached_input_tokens": 2845952,
    "cache_write_input_tokens": 0,
    "output_tokens": 13049,
    "reasoning_output_tokens": 4392,
    "total_tokens": 2926083
  },
  "thread_token_usage": {
    "input_tokens": 2913034,
    "cached_input_tokens": 2845952,
    "cache_write_input_tokens": 0,
    "output_tokens": 13049,
    "reasoning_output_tokens": 4392,
    "total_tokens": 2926083
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:19025,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 9.2 seconds
Output:

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Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
    "id": "exec-6c91610c-326b-4757-9803-7b4162f6a520",
    "process_id": "19025",
    "command": [
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      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/hard_validation_checkpoints --output /app/solution/hard_validation.parquet"
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    "parsed_cmd": [
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        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/hard_validation_checkpoints --output /app/solution/hard_validation.parquet"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.641615 running_bce=0.626945 elapsed=302.5s\nsaved epoch checkpoint: /app/solution/hard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.62598 validation_loss=0.60134 pair_accuracy=0.594 train_targets=1,052,953 validation_targets=130,103 train_seconds=322.4 validation_seconds=28.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.601343\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 485.143,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 3,\n    \"hard_negatives\": 16,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.6259799186684242,\n      \"positive_logit\": 0.30608463611870856,\n      \"negative_logit\": -0.20270241024870375,\n      \"pair_accuracy\": 0.5936495681668602,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 322.4322490700001,\n      \"validation_loss\": 0.6013428888216362,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 28.591543543999933\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.10056471824646,\n    \"query_norm_std\": 0.6721532344818115,\n    \"item_norm_mean\": 1.6144580841064453,\n    \"item_norm_std\": 0.9948017001152039\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.641615 running_bce=0.626945 elapsed=302.5s\nsaved epoch checkpoint: /app/solution/hard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.62598 validation_loss=0.60134 pair_accuracy=0.594 train_targets=1,052,953 validation_targets=130,103 train_seconds=322.4 validation_seconds=28.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.601343\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 485.143,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 3,\n    \"hard_negatives\": 16,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.6259799186684242,\n      \"positive_logit\": 0.30608463611870856,\n      \"negative_logit\": -0.20270241024870375,\n      \"pair_accuracy\": 0.5936495681668602,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 322.4322490700001,\n      \"validation_loss\": 0.6013428888216362,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 28.591543543999933\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.10056471824646,\n    \"query_norm_std\": 0.6721532344818115,\n    \"item_norm_mean\": 1.6144580841064453,\n    \"item_norm_std\": 0.9948017001152039\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 489,
      "nanos": 366996341
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.4s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.9s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.7s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.641615 running_bce=0.626945 elapsed=302.5s\nsaved epoch checkpoint: /app/solution/hard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.62598 validation_loss=0.60134 pair_accuracy=0.594 train_targets=1,052,953 validation_targets=130,103 train_seconds=322.4 validation_seconds=28.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.601343\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 485.143,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 3,\n    \"hard_negatives\": 16,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.6259799186684242,\n      \"positive_logit\": 0.30608463611870856,\n      \"negative_logit\": -0.20270241024870375,\n      \"pair_accuracy\": 0.5936495681668602,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 322.4322490700001,\n      \"validation_loss\": 0.6013428888216362,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 28.591543543999933\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.10056471824646,\n    \"query_norm_std\": 0.6721532344818115,\n    \"item_norm_mean\": 1.6144580841064453,\n    \"item_norm_std\": 0.9948017001152039\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
57

exec command

+21:50
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"2d66c0","wall_time_seconds":1.000650974,"session_id":76797,"original_token_count":0,"output":""}
Harness context token usage record
{
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    "total_tokens": 3055793
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-677c1142-9510-40f6-a73c-855f994e8c5d",
    "process_id": "76797",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014988,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014988,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 191133213
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014988,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:76797,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"97ec6d","wall_time_seconds":0.00000236,"exit_code":0,"original_token_count":34,"output":"{\n  \"model\": \"/app/solution/hard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014988,\n    \"quality_users\": 4263\n  }\n}\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44c485f6c87d2816e681957368365",
  "usage": {
    "input_tokens": 65566,
    "cached_input_tokens": 65280,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
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  },
  "turn_token_usage": {
    "input_tokens": 3108187,
    "cached_input_tokens": 3039104,
    "cache_write_input_tokens": 0,
    "output_tokens": 13218,
    "reasoning_output_tokens": 4398,
    "total_tokens": 3121405
  },
  "thread_token_usage": {
    "input_tokens": 3108187,
    "cached_input_tokens": 3039104,
    "cache_write_input_tokens": 0,
    "output_tokens": 13218,
    "reasoning_output_tokens": 4398,
    "total_tokens": 3121405
  }
}
58

Agent message

+22:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

59

exec command

+22:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The first mining setting was too aggressive: public NDCG fell to 0.014988. Its much lower embedding norms and 0.594 pair accuracy show that letting half the loss come from the top 16 of 96 proposals overwhelms positive learning—especially because organic future positives are unlabeled and can look “hard.” I’m retaining the candidate-aware filter but reducing mining to 4 hard negatives out of 32, drawn from 64 proposals; the other 28 remain random. This tests the diagnosis without allowing likely false negatives to dominate.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --negative-pool-multiplier 2 --hard-negatives 4 --checkpoint-dir /app/solution/semihard_validation_checkpoints --output /app/solution/semihard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"a7f74a","wall_time_seconds":1.000400764,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44c4e19c087d2904dad96e9360801",
  "usage": {
    "input_tokens": 65721,
    "cached_input_tokens": 65408,
    "cache_write_input_tokens": 0,
    "output_tokens": 1050,
    "reasoning_output_tokens": 803,
    "total_tokens": 66771
  },
  "turn_token_usage": {
    "input_tokens": 3173908,
    "cached_input_tokens": 3104512,
    "cache_write_input_tokens": 0,
    "output_tokens": 14268,
    "reasoning_output_tokens": 5201,
    "total_tokens": 3188176
  },
  "thread_token_usage": {
    "input_tokens": 3173908,
    "cached_input_tokens": 3104512,
    "cache_write_input_tokens": 0,
    "output_tokens": 14268,
    "reasoning_output_tokens": 5201,
    "total_tokens": 3188176
  }
}
60

exec command

+22:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6bf1d8","wall_time_seconds":30.001141067,"session_id":42557,"original_token_count":109,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\n"}
Harness context token usage record
{
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  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
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    "cache_write_input_tokens": 0,
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  },
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    "reasoning_output_tokens": 5224,
    "total_tokens": 3255077
  }
}
61

exec command

+23:18
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"df6557","wall_time_seconds":30.000921365,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44c988aec87d29b0fb899ed7b4b91",
  "usage": {
    "input_tokens": 67099,
    "cached_input_tokens": 66688,
    "cache_write_input_tokens": 0,
    "output_tokens": 57,
    "reasoning_output_tokens": 9,
    "total_tokens": 67156
  },
  "turn_token_usage": {
    "input_tokens": 3307837,
    "cached_input_tokens": 3236736,
    "cache_write_input_tokens": 0,
    "output_tokens": 14396,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3322233
  },
  "thread_token_usage": {
    "input_tokens": 3307837,
    "cached_input_tokens": 3236736,
    "cache_write_input_tokens": 0,
    "output_tokens": 14396,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3322233
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"3561dd","wall_time_seconds":30.001374198,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44cbcbc8c87d29fead48027e94a21",
  "usage": {
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 0,
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 14442,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3389492
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2689e6","wall_time_seconds":30.000531738,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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  "usage": {
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    "cached_input_tokens": 67072,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 67362
  },
  "turn_token_usage": {
    "input_tokens": 3442366,
    "cached_input_tokens": 3370752,
    "cache_write_input_tokens": 0,
    "output_tokens": 14488,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3456854
  },
  "thread_token_usage": {
    "input_tokens": 3442366,
    "cached_input_tokens": 3370752,
    "cache_write_input_tokens": 0,
    "output_tokens": 14488,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3456854
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"8c2d03","wall_time_seconds":30.001693452,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44cff1e9087d2a92b2891a67e35ac",
  "usage": {
    "input_tokens": 67420,
    "cached_input_tokens": 67200,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 67466
  },
  "turn_token_usage": {
    "input_tokens": 3509786,
    "cached_input_tokens": 3437952,
    "cache_write_input_tokens": 0,
    "output_tokens": 14534,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3524320
  },
  "thread_token_usage": {
    "input_tokens": 3509786,
    "cached_input_tokens": 3437952,
    "cache_write_input_tokens": 0,
    "output_tokens": 14534,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3524320
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6ee89b","wall_time_seconds":30.001021391,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44d20a0e887d2803db5b1f0c62ac4",
  "usage": {
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    "cached_input_tokens": 67200,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 0,
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  },
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  },
  "thread_token_usage": {
    "input_tokens": 3577311,
    "cached_input_tokens": 3505152,
    "cache_write_input_tokens": 0,
    "output_tokens": 14580,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3591891
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f94301","wall_time_seconds":30.001254092,"session_id":42557,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab44d41f43487d2ae3469998b22b120",
  "usage": {
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    "cached_input_tokens": 67328,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
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  },
  "turn_token_usage": {
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    "cached_input_tokens": 3572480,
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    "total_tokens": 3659566
  },
  "thread_token_usage": {
    "input_tokens": 3644940,
    "cached_input_tokens": 3572480,
    "cache_write_input_tokens": 0,
    "output_tokens": 14626,
    "reasoning_output_tokens": 5233,
    "total_tokens": 3659566
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"97c43e","wall_time_seconds":30.001385771,"session_id":42557,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.570423 running_bce=0.563064 elapsed=253.5s\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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  "usage": {
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  "thread_token_usage": {
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    "cached_input_tokens": 3639936,
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    "reasoning_output_tokens": 5233,
    "total_tokens": 3727344
  }
}
62

exec command

+27:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e806db","wall_time_seconds":30.001271886,"session_id":42557,"original_token_count":79,"output":"saved epoch checkpoint: /app/solution/semihard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56142 validation_loss=0.51991 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=270.4 validation_seconds=21.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.519913\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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  "usage": {
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  },
  "turn_token_usage": {
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Tool callexecTool call Returned

Tool call · Input 

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Tool response 

Script completed
Wall time 30.0 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:42557,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 4.0 seconds
Output:

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Harness context codex item completed
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    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.570423 running_bce=0.563064 elapsed=253.5s\nsaved epoch checkpoint: /app/solution/semihard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56142 validation_loss=0.51991 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=270.4 validation_seconds=21.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.519913\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.125,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.474623680114746,\n    \"query_norm_std\": 0.7963162660598755,\n    \"item_norm_mean\": 1.867469310760498,\n    \"item_norm_std\": 0.9103963971138\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.570423 running_bce=0.563064 elapsed=253.5s\nsaved epoch checkpoint: /app/solution/semihard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56142 validation_loss=0.51991 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=270.4 validation_seconds=21.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.519913\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.125,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.474623680114746,\n    \"query_norm_std\": 0.7963162660598755,\n    \"item_norm_mean\": 1.867469310760498,\n    \"item_norm_std\": 0.9103963971138\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
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      "nanos": 303481496
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.570423 running_bce=0.563064 elapsed=253.5s\nsaved epoch checkpoint: /app/solution/semihard_validation_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.56142 validation_loss=0.51991 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=270.4 validation_seconds=21.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.519913\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 376.125,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.474623680114746,\n    \"query_norm_std\": 0.7963162660598755,\n    \"item_norm_mean\": 1.867469310760498,\n    \"item_norm_std\": 0.9103963971138\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
63

exec command

+28:59
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"955588","wall_time_seconds":1.000494461,"session_id":17113,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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    "process_id": "17113",
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/semihard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020535,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/semihard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020535,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:17113,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"7f326f","wall_time_seconds":0.00000216,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/semihard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020535,\n    \"quality_users\": 4263\n  }\n}\n"}
Harness context token usage record
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}
64

exec command

+29:18
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The moderated one-epoch miner recovered most of the baseline but still scored 0.020535. Its chronological BCE (0.5199) remains materially above the baseline (0.4966), indicating one pass has not converged under the harder task rather than showing immediate over-mining collapse. I’m continuing that exact checkpoint for one additional epoch; this is the cheapest controlled test now and preserves the same public boundary.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --negative-pool-multiplier 2 --hard-negatives 4 --epochs 2 --early-stopping-patience 1 --resume-checkpoint /app/solution/semihard_validation_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard2_validation_checkpoints --output /app/solution/semihard2_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"5c10ea","wall_time_seconds":1.001044581,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
65

exec command

+29:31
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"85fccb","wall_time_seconds":30.001153834,"session_id":3557,"original_token_count":144,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/semihard_validation_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.519913\n"}
Harness context token usage record
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  }
}
66

exec command

+30:07
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd\nsys.path.insert(0,'/task'); import evaluate_public as ev\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=ev.validation_truth(e,targets)\ndef hits(p):\n f=pd.read_parquet(p).query('rank<=10'); r={int(u):set(z.item_id.astype(int)) for u,z in f.groupby('uid')}; return {(u,i):v for u,t in g.items() for i,v in t.items() if i in r[u]}\na=hits('/app/solution/production_validation.parquet'); b=hits('/app/solution/semihard_validation.parquet'); c=hits('/app/solution/hard_validation.parquet')\nfor name,x in [('base',a),('semi',b),('hard',c)]: print(name,len(x),sum(v==3 for v in x.values()))\nprint('semi gained/lost',len(set(b)-set(a)),len(set(a)-set(b)),'likes',sum(b[x]==3 for x in set(b)-set(a)),sum(a[x]==3 for x in set(a)-set(b)))\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"4243ef","wall_time_seconds":1.000947322,"session_id":70773,"original_token_count":0,"output":""}
Harness context token usage record
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  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:70773,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.1 seconds
Output:

{"chunk_id":"5fa827","wall_time_seconds":1.06582998,"exit_code":0,"original_token_count":19,"output":"base 644 80\nsemi 617 73\nhard 459 48\nsemi gained/lost 138 165 likes 15 22\n"}
Harness context token usage record
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-11ccadb0-54be-4e25-9d35-efb24881a511",
    "process_id": "70773",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd\nsys.path.insert(0,'/task'); import evaluate_public as ev\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=ev.validation_truth(e,targets)\ndef hits(p):\n f=pd.read_parquet(p).query('rank<=10'); r={int(u):set(z.item_id.astype(int)) for u,z in f.groupby('uid')}; return {(u,i):v for u,t in g.items() for i,v in t.items() if i in r[u]}\na=hits('/app/solution/production_validation.parquet'); b=hits('/app/solution/semihard_validation.parquet'); c=hits('/app/solution/hard_validation.parquet')\nfor name,x in [('base',a),('semi',b),('hard',c)]: print(name,len(x),sum(v==3 for v in x.values()))\nprint('semi gained/lost',len(set(b)-set(a)),len(set(a)-set(b)),'likes',sum(b[x]==3 for x in set(b)-set(a)),sum(a[x]==3 for x in set(a)-set(b)))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,pandas as pd\nsys.path.insert(0,'/task'); import evaluate_public as ev\nfrom ranking_evaluation import CandidateSet\nfrom pathlib import Path\nD=Path('/task/data'); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); e=CandidateSet(D/'validation_eligible_candidates.npz'); g=ev.validation_truth(e,targets)\ndef hits(p):\n f=pd.read_parquet(p).query('rank<=10'); r={int(u):set(z.item_id.astype(int)) for u,z in f.groupby('uid')}; return {(u,i):v for u,t in g.items() for i,v in t.items() if i in r[u]}\na=hits('/app/solution/production_validation.parquet'); b=hits('/app/solution/semihard_validation.parquet'); c=hits('/app/solution/hard_validation.parquet')\nfor name,x in [('base',a),('semi',b),('hard',c)]: print(name,len(x),sum(v==3 for v in x.values()))\nprint('semi gained/lost',len(set(b)-set(a)),len(set(a)-set(b)),'likes',sum(b[x]==3 for x in set(b)-set(a)),sum(a[x]==3 for x in set(a)-set(b)))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "base 644 80\nsemi 617 73\nhard 459 48\nsemi gained/lost 138 165 likes 15 22\n",
    "stderr": "",
    "aggregated_output": "base 644 80\nsemi 617 73\nhard 459 48\nsemi gained/lost 138 165 likes 15 22\n",
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  }
}
67

exec command

+30:25
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"rg -n \"def build_target_weights|discovery-graded|organic-strong\" /app/solution/train_current_model.py && sed -n '960,1090p' /app/solution/train_current_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":12000}); text(r.output)

Tool response 

Script completed
Wall time 0.0 seconds
Output:

64:    target_weighting: str = "organic-strong"
998:def build_target_weights(
1019:        if config.target_weighting == "discovery-graded":
2013:        choices=("discovery-graded", "organic-strong", "uniform"),
2022:        "--organic-strong-target-weight",
    """Load mapped organic-like timestamps from public fitting history."""
    prefixes = ("train",) if stage == "validation" else ("train", "val")
    frames = [
        pd.read_parquet(
            public / "data" / f"{prefix}_likes.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic"],
        )
        for prefix in prefixes
    ]
    likes = pd.concat(frames, ignore_index=True)
    likes = likes[likes.is_organic == 1].copy()
    mapped = pd.Index(item_ids).get_indexer(
        likes.item_id.to_numpy(dtype=np.int64)
    )
    likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
    likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
    likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
    nested: dict[int, dict[int, list[int]]] = {}
    for raw_uid, raw_timestamp, raw_item in likes.itertuples(
        index=False, name=None
    ):
        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
            int(raw_timestamp)
        )
    result = {
        uid: {
            item: np.asarray(timestamps, dtype=np.int64)
            for item, timestamps in items.items()
        }
        for uid, items in nested.items()
    }
    log(
        f"loaded organic-like outcomes: rows={len(likes):,} "
        f"users={len(result):,}"
    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
    config: Config,
) -> np.ndarray:
    """Build nonzero per-token weights without dropping adjacent targets."""
    if config.target_weighting == "uniform":
        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
    else:
        weights = np.full(
            len(prepared.sequence_items),
            config.ordinary_target_weight,
            dtype=np.float32,
        )
        organic_strong = (
            (prepared.event_features[:, 0] > 0.5)
            & (prepared.event_features[:, 1] >= 0.8)
        )
        weights[organic_strong] = config.organic_strong_target_weight
        if config.target_weighting == "discovery-graded":
            like_outcomes = load_organic_like_outcomes(
                public, stage, prepared.item_ids
            )
            history_end = (
                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
            )
            horizon = config.discovery_horizon_days * base.DAY
            grade_three = 0
            for user_row, uid in enumerate(prepared.user_ids):
                start = int(prepared.offsets[user_row])
                end = int(prepared.offsets[user_row + 1])
                if end - start < 2:
                    continue
                last_training_target = start + int(training_counts[user_row])
                first_validation_target = last_training_target + 1
                split_timestamp = (
                    int(prepared.sequence_timestamps[first_validation_target])
                    if first_validation_target < end
                    else history_end
                )
                user_likes = like_outcomes.get(int(uid), {})
                seen = {int(prepared.sequence_items[start])}
                for position in range(start + 1, end):
                    item = int(prepared.sequence_items[position])
                    novel = item not in seen
                    seen.add(item)
                    if not novel:
                        # Repeats remain training examples, but they cannot be
                        # eligible discovery outcomes at the corresponding
                        # cutoff and therefore keep only the ordinary weight.
                        weights[position] = config.ordinary_target_weight
                        continue
                    timestamps = user_likes.get(item)
                    if timestamps is None:
                        continue
                    timestamp = int(prepared.sequence_timestamps[position])
                    upper = min(
                        timestamp + horizon,
                        split_timestamp
                        if position <= last_training_target
                        else history_end,
                    )
                    outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
                        weights[position] = config.organic_like_target_weight
                        grade_three += 1
            log(
                f"grade-3 first-encounter targets={grade_three:,} "
                f"horizon_days={config.discovery_horizon_days}"
            )
    log(
        f"target weighting={config.target_weighting} "
        f"min={weights.min():.3f} mean={weights.mean():.3f} "
        f"max={weights.max():.3f}"
    )
    return weights


def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
    window_starts: np.ndarray,
    process_counts: np.ndarray,
    score_starts: np.ndarray,
    start: int,
    steps: int,
    config: Config,
) -> tuple[np.ndarray, ...]:
    """Build one causal sequence block and mark the target rows to score."""
    batch_size = len(users)
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ea2f0c67-2363-43fa-b78f-ef5955735e6b",
    "process_id": "56677",
    "command": [
      "/bin/sh",
      "-lc",
      "rg -n \"def build_target_weights|discovery-graded|organic-strong\" /app/solution/train_current_model.py && sed -n '960,1090p' /app/solution/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "search",
        "cmd": "rg -n 'def build_target_weights|discovery-graded|organic-strong' /app/solution/train_current_model.py",
        "query": "def build_target_weights|discovery-graded|organic-strong",
        "path": "train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '960,1090p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "64:    target_weighting: str = \"organic-strong\"\n998:def build_target_weights(\n1019:        if config.target_weighting == \"discovery-graded\":\n2013:        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n2022:        \"--organic-strong-target-weight\",\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n",
    "stderr": "",
    "aggregated_output": "64:    target_weighting: str = \"organic-strong\"\n998:def build_target_weights(\n1019:        if config.target_weighting == \"discovery-graded\":\n2013:        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n2022:        \"--organic-strong-target-weight\",\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3940
    },
    "formatted_output": "64:    target_weighting: str = \"organic-strong\"\n998:def build_target_weights(\n1019:        if config.target_weighting == \"discovery-graded\":\n2013:        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n2022:        \"--organic-strong-target-weight\",\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n"
  }
}
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68

Agent message

+30:46
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

69

exec command

+30:48
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"7cee48","wall_time_seconds":30.001487915,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"810f95","wall_time_seconds":30.001448621,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"6b88ae","wall_time_seconds":30.001887023,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"9208c1","wall_time_seconds":30.001748065,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"e0a54b","wall_time_seconds":30.000929061,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"dee8c4","wall_time_seconds":30.002166654,"session_id":3557,"original_token_count":24,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.447662 running_bce=0.485936 elapsed=261.0s\n"}
Harness context token usage record
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}
70

exec command

+34:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d86704","wall_time_seconds":30.00051337,"session_id":3557,"original_token_count":79,"output":"saved epoch checkpoint: /app/solution/semihard2_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48466 validation_loss=0.45986 pair_accuracy=0.797 train_targets=1,052,953 validation_targets=130,103 train_seconds=277.7 validation_seconds=22.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.459860\n"}
Harness context token usage record
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71

exec command

+34:40
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"46e12d","wall_time_seconds":30.000841481,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"b0c10e","wall_time_seconds":30.000832953,"session_id":3557,"original_token_count":0,"output":""}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3557,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 7.0 seconds
Output:

{"chunk_id":"e02b0b","wall_time_seconds":6.974694635,"exit_code":0,"original_token_count":943,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard2_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 384.333,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4846561676434637,\n      \"positive_logit\": 0.9101272816475532,\n      \"negative_logit\": -1.2338925825854314,\n      \"pair_accuracy\": 0.7965999847571544,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 277.6880012679999,\n      \"validation_loss\": 0.4598604619298523,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 22.250706159999936\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7216148376464844,\n    \"query_norm_std\": 0.7438143491744995,\n    \"item_norm_mean\": 2.722867250442505,\n    \"item_norm_std\": 1.0894688367843628\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2728d5d6-8652-4d6a-9f24-5da9000d1c52",
    "process_id": "3557",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --negative-pool-multiplier 2 --hard-negatives 4 --epochs 2 --early-stopping-patience 1 --resume-checkpoint /app/solution/semihard_validation_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard2_validation_checkpoints --output /app/solution/semihard2_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --negative-pool-multiplier 2 --hard-negatives 4 --epochs 2 --early-stopping-patience 1 --resume-checkpoint /app/solution/semihard_validation_checkpoints/epoch-01.pt --checkpoint-dir /app/solution/semihard2_validation_checkpoints --output /app/solution/semihard2_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/semihard_validation_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.519913\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.447662 running_bce=0.485936 elapsed=261.0s\nsaved epoch checkpoint: /app/solution/semihard2_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48466 validation_loss=0.45986 pair_accuracy=0.797 train_targets=1,052,953 validation_targets=130,103 train_seconds=277.7 validation_seconds=22.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.459860\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard2_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 384.333,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4846561676434637,\n      \"positive_logit\": 0.9101272816475532,\n      \"negative_logit\": -1.2338925825854314,\n      \"pair_accuracy\": 0.7965999847571544,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 277.6880012679999,\n      \"validation_loss\": 0.4598604619298523,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 22.250706159999936\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7216148376464844,\n    \"query_norm_std\": 0.7438143491744995,\n    \"item_norm_mean\": 2.722867250442505,\n    \"item_norm_std\": 1.0894688367843628\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/semihard_validation_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.519913\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.447662 running_bce=0.485936 elapsed=261.0s\nsaved epoch checkpoint: /app/solution/semihard2_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48466 validation_loss=0.45986 pair_accuracy=0.797 train_targets=1,052,953 validation_targets=130,103 train_seconds=277.7 validation_seconds=22.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.459860\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard2_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 384.333,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4846561676434637,\n      \"positive_logit\": 0.9101272816475532,\n      \"negative_logit\": -1.2338925825854314,\n      \"pair_accuracy\": 0.7965999847571544,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 277.6880012679999,\n      \"validation_loss\": 0.4598604619298523,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 22.250706159999936\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7216148376464844,\n    \"query_norm_std\": 0.7438143491744995,\n    \"item_norm_mean\": 2.722867250442505,\n    \"item_norm_std\": 1.0894688367843628\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 388,
      "nanos": 504109375
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\ncandidate-aware negative filter: users=9,135 target_users=8,449 fallback_positive_items=5,524,275 bytes=34,256,250\nrestored optimizer state from /app/solution/semihard_validation_checkpoints/epoch-01.pt\nresumed validation-BCE training: epoch=1 loss=0.519913\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.447662 running_bce=0.485936 elapsed=261.0s\nsaved epoch checkpoint: /app/solution/semihard2_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48466 validation_loss=0.45986 pair_accuracy=0.797 train_targets=1,052,953 validation_targets=130,103 train_seconds=277.7 validation_seconds=22.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.459860\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/semihard2_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 384.333,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5614174965940508,\n      \"positive_logit\": 0.5837055593212621,\n      \"negative_logit\": -0.5205735416225179,\n      \"pair_accuracy\": 0.7231550814708728,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 270.4383610560001,\n      \"validation_loss\": 0.5199125453864243,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.876968810000108\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4846561676434637,\n      \"positive_logit\": 0.9101272816475532,\n      \"negative_logit\": -1.2338925825854314,\n      \"pair_accuracy\": 0.7965999847571544,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 277.6880012679999,\n      \"validation_loss\": 0.4598604619298523,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 22.250706159999936\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7216148376464844,\n    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  }
}
72

exec command

+35:58
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard2_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"f3daa6","wall_time_seconds":1.000798452,"session_id":65558,"original_token_count":0,"output":""}
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
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    "command": [
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      "-lc",
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    ],
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        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/semihard2_validation.parquet"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/semihard2_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.022595,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:65558,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

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Harness context token usage record
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}
73

Agent message

+36:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

74

exec command

+36:38
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Two epochs of moderate semi-hard mining now clear the baseline: 0.022595 vs 0.021280 (+6.2% relative), while chronological BCE improves to 0.4599. Before final refit, I’m tightening the implementation to preserve the internal chronological split exactly: the stage candidate artifact must govern ranking, but should not reveal encounters from the internal held-out suffix to its training prefix. I’ll rerun the selected miner with the original prefix-only false-negative filter; only that boundary-clean result will be eligible for final selection.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-    epochs: int = 1\n+    epochs: int = 2\n@@\n-    negative_pool_multiplier: int = 3\n-    hard_negatives: int = 16\n+    negative_pool_multiplier: int = 2\n+    hard_negatives: int = 4\n@@\n def build_known_positive_filter(\n     prepared: SequenceData,\n     training_counts: np.ndarray,\n-    eligibility: CandidateSet,\n ) -> np.ndarray:\n-    \"\"\"Pack negatives that cannot occur in the stage's retrieval candidate set.\n-\n-    For target users the platform artifact is authoritative and excludes every\n-    prior encounter, not merely >=50% listens. Other training users fall back\n-    to the complete positive-listen prefix.\n-    \"\"\"\n+    \"\"\"Pack each user's complete chronological training-prefix positive set.\"\"\"\n@@\n-    eligible_rows = pd.Index(eligibility.uids).get_indexer(prepared.user_ids)\n-    matched = eligible_rows >= 0\n-    packed[matched] = np.bitwise_not(eligibility.packed[eligible_rows[matched]])\n     log(\n-        f\"candidate-aware negative filter: users={len(prepared.user_ids):,} \"\n-        f\"target_users={int(matched.sum()):,} fallback_positive_items={retained:,} \"\n-        f\"bytes={packed.nbytes:,}\"\n+        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n+        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n@@\n     public: Path,\n     stage: str,\n     prepared: SequenceData,\n-    eligibility: CandidateSet,\n     config: Config,\n@@\n-        build_known_positive_filter(\n-            prepared, partition.history_training_counts, eligibility\n-        )\n+        build_known_positive_filter(prepared, partition.history_training_counts)\n@@\n         public,\n         args.stage,\n         prepared,\n-        eligibility,\n         config,\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-619ee0d8-3c19-4db3-ae4b-a007318c3f77",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -50,3 +50,3 @@\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n@@ -57,4 +57,4 @@\n     inbatch_negatives: int = 8\n-    negative_pool_multiplier: int = 3\n-    hard_negatives: int = 16\n+    negative_pool_multiplier: int = 2\n+    hard_negatives: int = 4\n     inbatch_correction_power: float = 0.5\n@@ -582,10 +582,4 @@\n     training_counts: np.ndarray,\n-    eligibility: CandidateSet,\n ) -> np.ndarray:\n-    \"\"\"Pack negatives that cannot occur in the stage's retrieval candidate set.\n-\n-    For target users the platform artifact is authoritative and excludes every\n-    prior encounter, not merely >=50% listens. Other training users fall back\n-    to the complete positive-listen prefix.\n-    \"\"\"\n+    \"\"\"Pack each user's complete chronological training-prefix positive set.\"\"\"\n     width = (len(prepared.item_ids) + 7) // 8\n@@ -610,9 +604,5 @@\n         retained += len(items)\n-    eligible_rows = pd.Index(eligibility.uids).get_indexer(prepared.user_ids)\n-    matched = eligible_rows >= 0\n-    packed[matched] = np.bitwise_not(eligibility.packed[eligible_rows[matched]])\n     log(\n-        f\"candidate-aware negative filter: users={len(prepared.user_ids):,} \"\n-        f\"target_users={int(matched.sum()):,} fallback_positive_items={retained:,} \"\n-        f\"bytes={packed.nbytes:,}\"\n+        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n+        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n     )\n@@ -1572,3 +1562,2 @@\n     prepared: SequenceData,\n-    eligibility: CandidateSet,\n     config: Config,\n@@ -1614,5 +1603,3 @@\n     known_positive_packed = (\n-        build_known_positive_filter(\n-            prepared, partition.history_training_counts, eligibility\n-        )\n+        build_known_positive_filter(prepared, partition.history_training_counts)\n         if config.negative_filter_scope == \"full-training-history\"\n@@ -2164,3 +2151,2 @@\n         prepared,\n-        eligibility,\n         config,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}
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}
75

exec command

+37:01
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/clean_semihard_validation_checkpoints --output /app/solution/clean_semihard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"60959f","wall_time_seconds":1.001046727,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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}
76

exec command

+37:08
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"05f7c9","wall_time_seconds":30.00028383,"session_id":15707,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}
Harness context token usage record
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}
77

exec command

+37:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"4cdf99","wall_time_seconds":30.001997165,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
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    "total_tokens": 5697157
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"186146","wall_time_seconds":30.001246531,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"42f0a0","wall_time_seconds":30.001449359,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"34da0d","wall_time_seconds":30.000731973,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"aea58b","wall_time_seconds":30.000902165,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"8afc2c","wall_time_seconds":30.005254866,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
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  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"be87e3","wall_time_seconds":30.000797558,"session_id":15707,"original_token_count":24,"output":"epoch=1/2 batches=250 targets=987,417 batch_bce=0.571924 running_bce=0.566652 elapsed=250.6s\n"}
Harness context token usage record
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}
78

exec command

+41:34
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"bfc06d","wall_time_seconds":30.000863701,"session_id":15707,"original_token_count":64,"output":"saved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56501 validation_loss=0.52461 pair_accuracy=0.718 train_targets=1,052,953 validation_targets=130,103 train_seconds=267.3 validation_seconds=21.8\n"}
Harness context token usage record
{
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  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab450e2e9ac87d287ff9682fcad6106",
  "usage": {
    "input_tokens": 79067,
    "cached_input_tokens": 78720,
    "cache_write_input_tokens": 0,
    "output_tokens": 64,
    "reasoning_output_tokens": 16,
    "total_tokens": 79131
  },
  "turn_token_usage": {
    "input_tokens": 6228868,
    "cached_input_tokens": 6138496,
    "cache_write_input_tokens": 0,
    "output_tokens": 19703,
    "reasoning_output_tokens": 7402,
    "total_tokens": 6248571
  },
  "thread_token_usage": {
    "input_tokens": 6228868,
    "cached_input_tokens": 6138496,
    "cache_write_input_tokens": 0,
    "output_tokens": 19703,
    "reasoning_output_tokens": 7402,
    "total_tokens": 6248571
  }
}
79

exec command

+42:08
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"767988","wall_time_seconds":30.001896719,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab451045c9887d2b204bb949f8380ff",
  "usage": {
    "input_tokens": 79263,
    "cached_input_tokens": 78848,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 7,
    "total_tokens": 79318
  },
  "turn_token_usage": {
    "input_tokens": 6308131,
    "cached_input_tokens": 6217344,
    "cache_write_input_tokens": 0,
    "output_tokens": 19758,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6327889
  },
  "thread_token_usage": {
    "input_tokens": 6308131,
    "cached_input_tokens": 6217344,
    "cache_write_input_tokens": 0,
    "output_tokens": 19758,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6327889
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c06cb9","wall_time_seconds":30.000543098,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab45126f7fc87d28eee62c49f9896a0",
  "usage": {
    "input_tokens": 79374,
    "cached_input_tokens": 79104,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79420
  },
  "turn_token_usage": {
    "input_tokens": 6387505,
    "cached_input_tokens": 6296448,
    "cache_write_input_tokens": 0,
    "output_tokens": 19804,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6407309
  },
  "thread_token_usage": {
    "input_tokens": 6387505,
    "cached_input_tokens": 6296448,
    "cache_write_input_tokens": 0,
    "output_tokens": 19804,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6407309
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c7300a","wall_time_seconds":30.001473602,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab45148478087d290b3c4c41e4ab641",
  "usage": {
    "input_tokens": 79478,
    "cached_input_tokens": 79232,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79524
  },
  "turn_token_usage": {
    "input_tokens": 6466983,
    "cached_input_tokens": 6375680,
    "cache_write_input_tokens": 0,
    "output_tokens": 19850,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6486833
  },
  "thread_token_usage": {
    "input_tokens": 6466983,
    "cached_input_tokens": 6375680,
    "cache_write_input_tokens": 0,
    "output_tokens": 19850,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6486833
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"026788","wall_time_seconds":30.001061637,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab45168efb487d2a0b6831f407728c5",
  "usage": {
    "input_tokens": 79582,
    "cached_input_tokens": 79360,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79628
  },
  "turn_token_usage": {
    "input_tokens": 6546565,
    "cached_input_tokens": 6455040,
    "cache_write_input_tokens": 0,
    "output_tokens": 19896,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6566461
  },
  "thread_token_usage": {
    "input_tokens": 6546565,
    "cached_input_tokens": 6455040,
    "cache_write_input_tokens": 0,
    "output_tokens": 19896,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6566461
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"cf1be0","wall_time_seconds":30.000812446,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4518987ac87d2a470d32762db1c0e",
  "usage": {
    "input_tokens": 79684,
    "cached_input_tokens": 79360,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79730
  },
  "turn_token_usage": {
    "input_tokens": 6626249,
    "cached_input_tokens": 6534400,
    "cache_write_input_tokens": 0,
    "output_tokens": 19942,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6646191
  },
  "thread_token_usage": {
    "input_tokens": 6626249,
    "cached_input_tokens": 6534400,
    "cache_write_input_tokens": 0,
    "output_tokens": 19942,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6646191
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"eede3d","wall_time_seconds":30.002068279,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab451aa534487d2aecacbc1d8535197",
  "usage": {
    "input_tokens": 79788,
    "cached_input_tokens": 79488,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79834
  },
  "turn_token_usage": {
    "input_tokens": 6706037,
    "cached_input_tokens": 6613888,
    "cache_write_input_tokens": 0,
    "output_tokens": 19988,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6726025
  },
  "thread_token_usage": {
    "input_tokens": 6706037,
    "cached_input_tokens": 6613888,
    "cache_write_input_tokens": 0,
    "output_tokens": 19988,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6726025
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f3af55","wall_time_seconds":30.000407526,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab451cbdff087d2be667a326b16291d",
  "usage": {
    "input_tokens": 79892,
    "cached_input_tokens": 79616,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 79938
  },
  "turn_token_usage": {
    "input_tokens": 6785929,
    "cached_input_tokens": 6693504,
    "cache_write_input_tokens": 0,
    "output_tokens": 20034,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6805963
  },
  "thread_token_usage": {
    "input_tokens": 6785929,
    "cached_input_tokens": 6693504,
    "cache_write_input_tokens": 0,
    "output_tokens": 20034,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6805963
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"5ef3f1","wall_time_seconds":30.001213332,"session_id":15707,"original_token_count":24,"output":"epoch=2/2 batches=250 targets=987,417 batch_bce=0.458472 running_bce=0.491059 elapsed=251.2s\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab451ec8f3887d2b57989215ede2fd6",
  "usage": {
    "input_tokens": 79996,
    "cached_input_tokens": 79744,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 80042
  },
  "turn_token_usage": {
    "input_tokens": 6865925,
    "cached_input_tokens": 6773248,
    "cache_write_input_tokens": 0,
    "output_tokens": 20080,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6886005
  },
  "thread_token_usage": {
    "input_tokens": 6865925,
    "cached_input_tokens": 6773248,
    "cache_write_input_tokens": 0,
    "output_tokens": 20080,
    "reasoning_output_tokens": 7409,
    "total_tokens": 6886005
  }
}
80

exec command

+46:32
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"866c24","wall_time_seconds":30.000752491,"session_id":15707,"original_token_count":80,"output":"saved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.49007 validation_loss=0.46147 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=268.0 validation_seconds=21.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.461466\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4520d49ec87d287bcd92edbadd9ca",
  "usage": {
    "input_tokens": 80136,
    "cached_input_tokens": 79872,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 8,
    "total_tokens": 80192
  },
  "turn_token_usage": {
    "input_tokens": 6946061,
    "cached_input_tokens": 6853120,
    "cache_write_input_tokens": 0,
    "output_tokens": 20136,
    "reasoning_output_tokens": 7417,
    "total_tokens": 6966197
  },
  "thread_token_usage": {
    "input_tokens": 6946061,
    "cached_input_tokens": 6853120,
    "cache_write_input_tokens": 0,
    "output_tokens": 20136,
    "reasoning_output_tokens": 7417,
    "total_tokens": 6966197
  }
}
81

exec command

+47:05
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d1f721","wall_time_seconds":30.001113536,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4522e771c87d2a7647f7e4aac1091",
  "usage": {
    "input_tokens": 80341,
    "cached_input_tokens": 80000,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 80395
  },
  "turn_token_usage": {
    "input_tokens": 7026402,
    "cached_input_tokens": 6933120,
    "cache_write_input_tokens": 0,
    "output_tokens": 20190,
    "reasoning_output_tokens": 7423,
    "total_tokens": 7046592
  },
  "thread_token_usage": {
    "input_tokens": 7026402,
    "cached_input_tokens": 6933120,
    "cache_write_input_tokens": 0,
    "output_tokens": 20190,
    "reasoning_output_tokens": 7423,
    "total_tokens": 7046592
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"3d335b","wall_time_seconds":30.00115065,"session_id":15707,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4524f9d2c87d29a8153d8e8bf701e",
  "usage": {
    "input_tokens": 80453,
    "cached_input_tokens": 80128,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 80499
  },
  "turn_token_usage": {
    "input_tokens": 7106855,
    "cached_input_tokens": 7013248,
    "cache_write_input_tokens": 0,
    "output_tokens": 20236,
    "reasoning_output_tokens": 7423,
    "total_tokens": 7127091
  },
  "thread_token_usage": {
    "input_tokens": 7106855,
    "cached_input_tokens": 7013248,
    "cache_write_input_tokens": 0,
    "output_tokens": 20236,
    "reasoning_output_tokens": 7423,
    "total_tokens": 7127091
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-baa7c0f0-670c-41a9-9706-5036f90f7947",
    "process_id": "15707",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/clean_semihard_validation_checkpoints --output /app/solution/clean_semihard_validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/clean_semihard_validation_checkpoints --output /app/solution/clean_semihard_validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/2 batches=250 targets=987,417 batch_bce=0.571924 running_bce=0.566652 elapsed=250.6s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56501 validation_loss=0.52461 pair_accuracy=0.718 train_targets=1,052,953 validation_targets=130,103 train_seconds=267.3 validation_seconds=21.8\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.458472 running_bce=0.491059 elapsed=251.2s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.49007 validation_loss=0.46147 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=268.0 validation_seconds=21.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.461466\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/clean_semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 662.483,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5650139070648023,\n      \"positive_logit\": 0.5571393957318779,\n      \"negative_logit\": -0.49884416743947857,\n      \"pair_accuracy\": 0.7181721608181941,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 267.3387809699998,\n      \"validation_loss\": 0.5246087211603323,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.76341946499997\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4900651798868152,\n      \"positive_logit\": 0.8761440226988263,\n      \"negative_logit\": -1.165674550384126,\n      \"pair_accuracy\": 0.7908356605185607,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 268.01424384899974,\n      \"validation_loss\": 0.4614657787416502,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.2696409590003\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.948482871055603,\n    \"query_norm_std\": 0.8249732255935669,\n    \"item_norm_mean\": 2.5218734741210938,\n    \"item_norm_std\": 1.0423455238342285\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/2 batches=250 targets=987,417 batch_bce=0.571924 running_bce=0.566652 elapsed=250.6s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56501 validation_loss=0.52461 pair_accuracy=0.718 train_targets=1,052,953 validation_targets=130,103 train_seconds=267.3 validation_seconds=21.8\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.458472 running_bce=0.491059 elapsed=251.2s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.49007 validation_loss=0.46147 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=268.0 validation_seconds=21.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.461466\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/clean_semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 662.483,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5650139070648023,\n      \"positive_logit\": 0.5571393957318779,\n      \"negative_logit\": -0.49884416743947857,\n      \"pair_accuracy\": 0.7181721608181941,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 267.3387809699998,\n      \"validation_loss\": 0.5246087211603323,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.76341946499997\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4900651798868152,\n      \"positive_logit\": 0.8761440226988263,\n      \"negative_logit\": -1.165674550384126,\n      \"pair_accuracy\": 0.7908356605185607,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 268.01424384899974,\n      \"validation_loss\": 0.4614657787416502,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.2696409590003\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.948482871055603,\n    \"query_norm_std\": 0.8249732255935669,\n    \"item_norm_mean\": 2.5218734741210938,\n    \"item_norm_std\": 1.0423455238342285\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 666,
      "nanos": 848661610
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/2 batches=250 targets=987,417 batch_bce=0.571924 running_bce=0.566652 elapsed=250.6s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56501 validation_loss=0.52461 pair_accuracy=0.718 train_targets=1,052,953 validation_targets=130,103 train_seconds=267.3 validation_seconds=21.8\nepoch=2/2 batches=250 targets=987,417 batch_bce=0.458472 running_bce=0.491059 elapsed=251.2s\nsaved epoch checkpoint: /app/solution/clean_semihard_validation_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.49007 validation_loss=0.46147 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=268.0 validation_seconds=21.3\nrestored best validation-BCE checkpoint: epoch=2 loss=0.461466\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/clean_semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 662.483,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5650139070648023,\n      \"positive_logit\": 0.5571393957318779,\n      \"negative_logit\": -0.49884416743947857,\n      \"pair_accuracy\": 0.7181721608181941,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 267.3387809699998,\n      \"validation_loss\": 0.5246087211603323,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.76341946499997\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4900651798868152,\n      \"positive_logit\": 0.8761440226988263,\n      \"negative_logit\": -1.165674550384126,\n      \"pair_accuracy\": 0.7908356605185607,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 268.01424384899974,\n      \"validation_loss\": 0.4614657787416502,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.2696409590003\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.948482871055603,\n    \"query_norm_std\": 0.8249732255935669,\n    \"item_norm_mean\": 2.5218734741210938,\n    \"item_norm_std\": 1.0423455238342285\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:15707,chars:"",yield_time_ms:30000,max_output_tokens:12000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"e2f8ed","wall_time_seconds":0.00000162,"exit_code":0,"original_token_count":944,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/clean_semihard_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 662.483,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5650139070648023,\n      \"positive_logit\": 0.5571393957318779,\n      \"negative_logit\": -0.49884416743947857,\n      \"pair_accuracy\": 0.7181721608181941,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 267.3387809699998,\n      \"validation_loss\": 0.5246087211603323,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.76341946499997\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4900651798868152,\n      \"positive_logit\": 0.8761440226988263,\n      \"negative_logit\": -1.165674550384126,\n      \"pair_accuracy\": 0.7908356605185607,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 268.01424384899974,\n      \"validation_loss\": 0.4614657787416502,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 21.2696409590003\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.948482871055603,\n    \"query_norm_std\": 0.8249732255935669,\n    \"item_norm_mean\": 2.5218734741210938,\n    \"item_norm_std\": 1.0423455238342285\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}
Harness context token usage record
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  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
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    "cached_input_tokens": 80256,
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    "reasoning_output_tokens": 0,
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  "turn_token_usage": {
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    "cached_input_tokens": 7093504,
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  "thread_token_usage": {
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    "total_tokens": 7207694
  }
}
82

exec command

+48:14
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/clean_semihard_validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

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      "/bin/sh",
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    "stdout": "{\n  \"model\": \"/app/solution/clean_semihard_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.025858,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:50821,chars:"",yield_time_ms:1000,max_output_tokens:4000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 0.0 seconds
Output:

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83

exec command

+48:23
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The boundary-clean result is stronger: 0.025858 NDCG@10, up from 0.021280 (+21.5% relative). This confirms the gain comes from moderate semi-hard negative mining plus sufficient optimization—not from using stage eligibility during training. I’m now refitting that exact two-epoch configuration on train+validation using the cached submission-stage sequences; the existing valid baseline submission remains in place until the improved file is atomically written at completion.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{"chunk_id":"823f57","wall_time_seconds":1.00097247,"session_id":3190,"original_token_count":0,"output":""}
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84

exec command

+48:37
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+The model is a pure two-tower retriever: no user-ID embedding, no output\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique\n+in-batch proposals after chronological false-negative filtering.  The loss\n+retains four model-hard and 28 random proposals, limiting false-negative bias\n+while adding retrieval pressure. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n@@\n         current_window_values = (\n@@\n         )\n+        stored_mining_values = (\n+            stored_config.get(\"negative_pool_multiplier\", 1),\n+            stored_config.get(\"hard_negatives\", 0),\n+        )\n+        current_mining_values = (\n+            config.negative_pool_multiplier,\n+            config.hard_negatives,\n+        )\n         objective_changed = (\n@@\n             or stored_window_values != current_window_values\n+            or stored_mining_values != current_mining_values\n@@\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"64-filtered-proposals-to-4-hard-plus-28-random\"\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-0c5e98f2-d166-483a-a33e-fe6b9801a13e",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -12,6 +12,8 @@\n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique\n+in-batch proposals after chronological false-negative filtering.  The loss\n+retains four model-hard and 28 random proposals, limiting false-negative bias\n+while adding retrieval pressure. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n@@ -1665,2 +1667,10 @@\n         )\n+        stored_mining_values = (\n+            stored_config.get(\"negative_pool_multiplier\", 1),\n+            stored_config.get(\"hard_negatives\", 0),\n+        )\n+        current_mining_values = (\n+            config.negative_pool_multiplier,\n+            config.hard_negatives,\n+        )\n         objective_changed = (\n@@ -1670,2 +1680,3 @@\n             or stored_window_values != current_window_values\n+            or stored_mining_values != current_mining_values\n         )\n@@ -2191,3 +2202,3 @@\n                     \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"64-filtered-proposals-to-4-hard-plus-28-random\"\n                     ),\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}
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85

exec command

+48:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/train_current_model.py\n@@\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-semi-hard\",\n*** Add File: /app/solution/README.md\n+# Semi-hard two-tower retriever\n+\n+## Diagnosis\n+\n+The production loss uses 24 catalog-uniform and eight randomly selected\n+in-batch negatives per positive. Most uniform draws are easy, so they contribute\n+little useful top-of-list ordering pressure. Increasing hardness without a\n+guardrail is unsafe because unobserved organic preferences are not verified\n+negatives: retaining 16 hard examples from 96 proposals reduced public NDCG.\n+\n+## Change\n+\n+The final trainer keeps the architecture, chronological split, feature boundary,\n+candidate service, and raw two-tower dot-product serving path unchanged. For\n+each target it now:\n+\n+1. draws 48 uniform and 16 propensity-corrected in-batch proposals;\n+2. rejects the target, the user's chronological training-prefix positives,\n+   active likes, and row duplicates;\n+3. retains four highest-scoring model-hard negatives and 28 random proposals;\n+4. trains for up to two epochs with the existing chronological BCE selection.\n+\n+The hard selection is detached, so gradients remain the ordinary sampled BCE\n+gradients through the same user and item towers. The 28 random examples keep\n+the objective calibrated and limit latent-positive false-negative bias.\n+\n+## Public backtest\n+\n+| Model | NDCG@10 | Scored users |\n+| --- | ---: | ---: |\n+| Production reproduction | 0.021280 | 4,263 |\n+| Final boundary-clean semi-hard model | **0.025858** | 4,263 |\n+\n+This is a 21.5% relative improvement. An overly aggressive 16-of-32 hard mix\n+scored 0.014988, while the selected one-epoch moderate mix scored 0.020535; the\n+second epoch was necessary for the harder objective to converge.\n+\n+## Reproduce submission\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n+  --stage submission \\\n+  --public-dir /task \\\n+  --prepared-cache /app/solution/submission_prepared.npz \\\n+  --output /app/solution/submission.parquet\n+```\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-ff3dc089-2ae3-43cf-a6c7-46cdfe602f4d",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Semi-hard two-tower retriever\n\n## Diagnosis\n\nThe production loss uses 24 catalog-uniform and eight randomly selected\nin-batch negatives per positive. Most uniform draws are easy, so they contribute\nlittle useful top-of-list ordering pressure. Increasing hardness without a\nguardrail is unsafe because unobserved organic preferences are not verified\nnegatives: retaining 16 hard examples from 96 proposals reduced public NDCG.\n\n## Change\n\nThe final trainer keeps the architecture, chronological split, feature boundary,\ncandidate service, and raw two-tower dot-product serving path unchanged. For\neach target it now:\n\n1. draws 48 uniform and 16 propensity-corrected in-batch proposals;\n2. rejects the target, the user's chronological training-prefix positives,\n   active likes, and row duplicates;\n3. retains four highest-scoring model-hard negatives and 28 random proposals;\n4. trains for up to two epochs with the existing chronological BCE selection.\n\nThe hard selection is detached, so gradients remain the ordinary sampled BCE\ngradients through the same user and item towers. The 28 random examples keep\nthe objective calibrated and limit latent-positive false-negative bias.\n\n## Public backtest\n\n| Model | NDCG@10 | Scored users |\n| --- | ---: | ---: |\n| Production reproduction | 0.021280 | 4,263 |\n| Final boundary-clean semi-hard model | **0.025858** | 4,263 |\n\nThis is a 21.5% relative improvement. An overly aggressive 16-of-32 hard mix\nscored 0.014988, while the selected one-epoch moderate mix scored 0.020535; the\nsecond epoch was necessary for the harder objective to converge.\n\n## Reproduce submission\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission \\\n  --public-dir /task \\\n  --prepared-cache /app/solution/submission_prepared.npz \\\n  --output /app/solution/submission.parquet\n```\n"
      },
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -2179,3 +2179,3 @@\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-semi-hard\",\n                 \"stage\": args.stage,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}
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86

exec command

+49:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\ndiff -u /task/current_model/train_current_model.py /app/solution/train_current_model.py | sed -n '1,260p'","workdir":"/app","yield_time_ms":1000,"max_output_tokens":20000}); text(r.output)

Tool response 

Script completed
Wall time 0.3 seconds
Output:

--- /task/current_model/train_current_model.py	2026-09-23 21:34:36.000000000 +0000
+++ /app/solution/train_current_model.py	2026-09-23 22:29:02.762203021 +0000
@@ -10,10 +10,12 @@
 into the next block.
 
 The model is a pure two-tower retriever: no user-ID embedding, no output
-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
-selected adjacent target retains a nonzero weight. Only files under the supplied
-public directory are read.
+normalization, raw dot-product retrieval, and sample-weighted binary cross
+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique
+in-batch proposals after chronological false-negative filtering.  The loss
+retains four model-hard and 28 random proposals, limiting false-negative bias
+while adding retrieval pressure. Every selected adjacent target retains a
+nonzero weight. Only files under the supplied public directory are read.
 """
 from __future__ import annotations
 
@@ -48,13 +50,15 @@
     item_hidden_1: int = 512
     item_hidden_2: int = 256
     dropout: float = 0.05
-    epochs: int = 1
+    epochs: int = 2
     learning_rate: float = 1.0e-3
     weight_decay: float = 1.0e-5
     gradient_clip: float = 2.0
     loss_microbatch_size: int = 65_536
     uniform_negatives: int = 24
     inbatch_negatives: int = 8
+    negative_pool_multiplier: int = 2
+    hard_negatives: int = 4
     inbatch_correction_power: float = 0.5
     inbatch_correction_min: float = 0.25
     inbatch_correction_max: float = 4.0
@@ -576,9 +580,10 @@
 
 
 def build_known_positive_filter(
-    prepared: SequenceData, training_counts: np.ndarray
+    prepared: SequenceData,
+    training_counts: np.ndarray,
 ) -> np.ndarray:
-    """Pack each user's complete public-training positive-item set."""
+    """Pack each user's complete chronological training-prefix positive set."""
     width = (len(prepared.item_ids) + 7) // 8
     packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
     retained = 0
@@ -642,18 +647,20 @@
     targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
     liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
     batch_size = len(targets_cpu)
+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier
+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier
     uniform = torch.randint(
         n_items,
-        (batch_size, config.uniform_negatives),
+        (batch_size, uniform_count),
         generator=generator,
         device="cpu",
     )
     inbatch = torch.empty((batch_size, 0), dtype=torch.long)
     corrected_count = 0
-    if config.inbatch_negatives:
+    if inbatch_count:
         unique_targets = torch.unique(targets.detach().cpu())
         corrected_count = min(
-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
+            inbatch_count, max(len(unique_targets) - 1, 0)
         )
         if corrected_count:
             priorities = torch.rand(
@@ -669,10 +676,10 @@
                 priorities, k=corrected_count, dim=1
             ).indices
             inbatch = unique_targets[columns]
-        if corrected_count < config.inbatch_negatives:
+        if corrected_count < inbatch_count:
             filler = torch.randint(
                 n_items,
-                (batch_size, config.inbatch_negatives - corrected_count),
+                (batch_size, inbatch_count - corrected_count),
                 generator=generator,
                 device="cpu",
             )
@@ -682,7 +689,7 @@
     correction = np.ones_like(negatives, dtype=np.float32)
     if corrected_count:
         correction[
-            :, config.uniform_negatives : config.uniform_negatives + corrected_count
+            :, uniform_count : uniform_count + corrected_count
         ] = item_correction[inbatch[:, :corrected_count]].numpy()
     for _ in range(32):
         invalid = invalid_full_history_negatives(
@@ -752,8 +759,8 @@
         known_history,
         liked_items,
         len(prepared.item_ids),
-        config.uniform_negatives,
-        config.inbatch_negatives,
+        config.uniform_negatives * config.negative_pool_multiplier,
+        config.inbatch_negatives * config.negative_pool_multiplier,
         item_correction,
         generator,
         device,
@@ -864,6 +871,34 @@
     return result / total
 
 
+def select_mixed_hard_negatives(
+    logits: torch.Tensor,
+    correction: torch.Tensor,
+    config: Config,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Retain model-confusing negatives plus a random exploration set.
+
+    Proposal rows are already unique and false-negative filtered. Mining only
+    changes the training loss; serving remains one query/item dot product.
+    """
+    retained = config.uniform_negatives + config.inbatch_negatives
+    if logits.shape[1] == retained:
+        return logits, correction
+    hard_count = min(config.hard_negatives, retained)
+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices
+    selected = torch.zeros_like(logits, dtype=torch.bool)
+    selected.scatter_(1, hard_columns, True)
+    random_count = retained - hard_count
+    if random_count:
+        priorities = torch.rand_like(logits)
+        priorities.masked_fill_(selected, -torch.inf)
+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices
+        columns = torch.cat([hard_columns, random_columns], dim=1)
+    else:
+        columns = hard_columns
+    return logits.gather(1, columns), correction.gather(1, columns)
+
+
 def retained_target_window_starts(
     total_targets: np.ndarray,
     validation_counts: np.ndarray,
@@ -1168,10 +1203,15 @@
         negative_logits = torch.einsum(
             "bd,bkd->bk", query_vectors, negative_vectors
         )
-        positive_loss = F.softplus(-positive_logits)
-        negative_loss = corrected_negative_loss(
+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(
             negative_logits, negative_weights[begin:end], config
         )
+        positive_loss = F.softplus(-positive_logits)
+        negative_raw = F.softplus(negative_logits)
+        negative_loss = (
+            (negative_raw * selected_negative_weights).sum(dim=1)
+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)
+        )
         selected_weights = torch.from_numpy(
             sample_weights[begin:end].astype(np.float32, copy=False)
         ).to(device)
@@ -1625,11 +1665,20 @@
             config.max_train_targets_per_user,
             config.max_validation_targets_per_user,
         )
+        stored_mining_values = (
+            stored_config.get("negative_pool_multiplier", 1),
+            stored_config.get("hard_negatives", 0),
+        )
+        current_mining_values = (
+            config.negative_pool_multiplier,
+            config.hard_negatives,
+        )
         objective_changed = (
             stored_filter_scope != config.negative_filter_scope
             or stored_target_weighting != config.target_weighting
             or stored_weight_values != current_weight_values
             or stored_window_values != current_window_values
+            or stored_mining_values != current_mining_values
         )
         model.load_state_dict(payload["model_state"])
         if "optimizer_state" in payload and not objective_changed:
@@ -1946,6 +1995,18 @@
         default=Config.negative_filter_scope,
     )
     parser.add_argument(
+        "--negative-pool-multiplier",
+        type=int,
+        default=Config.negative_pool_multiplier,
+        help="draw this multiple of the 32 loss negatives before online mining",
+    )
+    parser.add_argument(
+        "--hard-negatives",
+        type=int,
+        default=Config.hard_negatives,
+        help="number of model-hard negatives among the 32 retained per target",
+    )
+    parser.add_argument(
         "--target-weighting",
         choices=("discovery-graded", "organic-strong", "uniform"),
         default=Config.target_weighting,
@@ -2018,6 +2079,9 @@
         or args.discovery_horizon_days < 1
         or args.max_train_targets_per_user < 0
         or args.max_validation_targets_per_user < 0
+        or args.negative_pool_multiplier < 1
+        or args.hard_negatives < 0
+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives
     ):
         parser.error("training counts, learning rate, and patience are invalid")
     if not 0.0 < args.validation_fraction < 1.0:
@@ -2031,6 +2095,8 @@
         epochs=args.epochs,
         learning_rate=args.learning_rate,
         loss_microbatch_size=args.loss_microbatch_size,
+        negative_pool_multiplier=args.negative_pool_multiplier,
+        hard_negatives=args.hard_negatives,
         negative_filter_scope=args.negative_filter_scope,
         target_weighting=args.target_weighting,
         ordinary_target_weight=args.ordinary_target_weight,
@@ -2111,7 +2177,7 @@
     print(
         json.dumps(
             {
-                "baseline": "yambda-windowed-sequence-two-tower-base",
+                "model": "yambda-windowed-sequence-two-tower-semi-hard",
                 "stage": args.stage,
                 "output": str(args.output.resolve()),
                 "rows": len(ranking),
@@ -2134,7 +2200,7 @@
                     "score": "raw-dot-product",
                     "objective": "sample-weighted-BCE-at-every-timestep",
                     "negative_sampling": (
-                        "24-uniform-plus-8-propensity-corrected-inbatch"
+                        "64-filtered-proposals-to-4-hard-plus-28-random"
                     ),
                     "false_negative_filter": config.negative_filter_scope,
                     "target_weighting": config.target_weighting,
Harness context token usage record
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  "type": "token_usage_record",
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  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab452af4fe087d298340c99090f5c00",
  "usage": {
    "input_tokens": 83857,
    "cached_input_tokens": 83072,
    "cache_write_input_tokens": 0,
    "output_tokens": 168,
    "reasoning_output_tokens": 60,
    "total_tokens": 84025
  },
  "turn_token_usage": {
    "input_tokens": 7683597,
    "cached_input_tokens": 7585280,
    "cache_write_input_tokens": 0,
    "output_tokens": 22010,
    "reasoning_output_tokens": 7713,
    "total_tokens": 7705607
  },
  "thread_token_usage": {
    "input_tokens": 7683597,
    "cached_input_tokens": 7585280,
    "cache_write_input_tokens": 0,
    "output_tokens": 22010,
    "reasoning_output_tokens": 7713,
    "total_tokens": 7705607
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-bd41008d-2720-4267-aa76-07c122200d9a",
    "process_id": "99645",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\ndiff -u /task/current_model/train_current_model.py /app/solution/train_current_model.py | sed -n '1,260p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\ndiff -u /task/current_model/train_current_model.py /app/solution/train_current_model.py | sed -n '1,260p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "--- /task/current_model/train_current_model.py\t2026-09-23 21:34:36.000000000 +0000\n+++ /app/solution/train_current_model.py\t2026-09-23 22:29:02.762203021 +0000\n@@ -10,10 +10,12 @@\n into the next block.\n \n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique\n+in-batch proposals after chronological false-negative filtering.  The loss\n+retains four model-hard and 28 random proposals, limiting false-negative bias\n+while adding retrieval pressure. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n from __future__ import annotations\n \n@@ -48,13 +50,15 @@\n     item_hidden_1: int = 512\n     item_hidden_2: int = 256\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n     weight_decay: float = 1.0e-5\n     gradient_clip: float = 2.0\n     loss_microbatch_size: int = 65_536\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 2\n+    hard_negatives: int = 4\n     inbatch_correction_power: float = 0.5\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n@@ -576,9 +580,10 @@\n \n \n def build_known_positive_filter(\n-    prepared: SequenceData, training_counts: np.ndarray\n+    prepared: SequenceData,\n+    training_counts: np.ndarray,\n ) -> np.ndarray:\n-    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n+    \"\"\"Pack each user's complete chronological training-prefix positive set.\"\"\"\n     width = (len(prepared.item_ids) + 7) // 8\n     packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n     retained = 0\n@@ -642,18 +647,20 @@\n     targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n     liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n     batch_size = len(targets_cpu)\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n         generator=generator,\n         device=\"cpu\",\n     )\n     inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n     corrected_count = 0\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n         unique_targets = torch.unique(targets.detach().cpu())\n         corrected_count = min(\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n         )\n         if corrected_count:\n             priorities = torch.rand(\n@@ -669,10 +676,10 @@\n                 priorities, k=corrected_count, dim=1\n             ).indices\n             inbatch = unique_targets[columns]\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n             filler = torch.randint(\n                 n_items,\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n                 generator=generator,\n                 device=\"cpu\",\n             )\n@@ -682,7 +689,7 @@\n     correction = np.ones_like(negatives, dtype=np.float32)\n     if corrected_count:\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n     for _ in range(32):\n         invalid = invalid_full_history_negatives(\n@@ -752,8 +759,8 @@\n         known_history,\n         liked_items,\n         len(prepared.item_ids),\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n         item_correction,\n         generator,\n         device,\n@@ -864,6 +871,34 @@\n     return result / total\n \n \n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n+\n+\n def retained_target_window_starts(\n     total_targets: np.ndarray,\n     validation_counts: np.ndarray,\n@@ -1168,10 +1203,15 @@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n             negative_logits, negative_weights[begin:end], config\n         )\n+        positive_loss = F.softplus(-positive_logits)\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n         selected_weights = torch.from_numpy(\n             sample_weights[begin:end].astype(np.float32, copy=False)\n         ).to(device)\n@@ -1625,11 +1665,20 @@\n             config.max_train_targets_per_user,\n             config.max_validation_targets_per_user,\n         )\n+        stored_mining_values = (\n+            stored_config.get(\"negative_pool_multiplier\", 1),\n+            stored_config.get(\"hard_negatives\", 0),\n+        )\n+        current_mining_values = (\n+            config.negative_pool_multiplier,\n+            config.hard_negatives,\n+        )\n         objective_changed = (\n             stored_filter_scope != config.negative_filter_scope\n             or stored_target_weighting != config.target_weighting\n             or stored_weight_values != current_weight_values\n             or stored_window_values != current_window_values\n+            or stored_mining_values != current_mining_values\n         )\n         model.load_state_dict(payload[\"model_state\"])\n         if \"optimizer_state\" in payload and not objective_changed:\n@@ -1946,6 +1995,18 @@\n         default=Config.negative_filter_scope,\n     )\n     parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n+    parser.add_argument(\n         \"--target-weighting\",\n         choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n         default=Config.target_weighting,\n@@ -2018,6 +2079,9 @@\n         or args.discovery_horizon_days < 1\n         or args.max_train_targets_per_user < 0\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n     if not 0.0 < args.validation_fraction < 1.0:\n@@ -2031,6 +2095,8 @@\n         epochs=args.epochs,\n         learning_rate=args.learning_rate,\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n         negative_filter_scope=args.negative_filter_scope,\n         target_weighting=args.target_weighting,\n         ordinary_target_weight=args.ordinary_target_weight,\n@@ -2111,7 +2177,7 @@\n     print(\n         json.dumps(\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-semi-hard\",\n                 \"stage\": args.stage,\n                 \"output\": str(args.output.resolve()),\n                 \"rows\": len(ranking),\n@@ -2134,7 +2200,7 @@\n                     \"score\": \"raw-dot-product\",\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                     \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"64-filtered-proposals-to-4-hard-plus-28-random\"\n                     ),\n                     \"false_negative_filter\": config.negative_filter_scope,\n                     \"target_weighting\": config.target_weighting,\n",
    "stderr": "",
    "aggregated_output": "--- /task/current_model/train_current_model.py\t2026-09-23 21:34:36.000000000 +0000\n+++ /app/solution/train_current_model.py\t2026-09-23 22:29:02.762203021 +0000\n@@ -10,10 +10,12 @@\n into the next block.\n \n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique\n+in-batch proposals after chronological false-negative filtering.  The loss\n+retains four model-hard and 28 random proposals, limiting false-negative bias\n+while adding retrieval pressure. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n from __future__ import annotations\n \n@@ -48,13 +50,15 @@\n     item_hidden_1: int = 512\n     item_hidden_2: int = 256\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n     weight_decay: float = 1.0e-5\n     gradient_clip: float = 2.0\n     loss_microbatch_size: int = 65_536\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 2\n+    hard_negatives: int = 4\n     inbatch_correction_power: float = 0.5\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n@@ -576,9 +580,10 @@\n \n \n def build_known_positive_filter(\n-    prepared: SequenceData, training_counts: np.ndarray\n+    prepared: SequenceData,\n+    training_counts: np.ndarray,\n ) -> np.ndarray:\n-    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n+    \"\"\"Pack each user's complete chronological training-prefix positive set.\"\"\"\n     width = (len(prepared.item_ids) + 7) // 8\n     packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n     retained = 0\n@@ -642,18 +647,20 @@\n     targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n     liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n     batch_size = len(targets_cpu)\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n         generator=generator,\n         device=\"cpu\",\n     )\n     inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n     corrected_count = 0\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n         unique_targets = torch.unique(targets.detach().cpu())\n         corrected_count = min(\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n         )\n         if corrected_count:\n             priorities = torch.rand(\n@@ -669,10 +676,10 @@\n                 priorities, k=corrected_count, dim=1\n             ).indices\n             inbatch = unique_targets[columns]\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n             filler = torch.randint(\n                 n_items,\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n                 generator=generator,\n                 device=\"cpu\",\n             )\n@@ -682,7 +689,7 @@\n     correction = np.ones_like(negatives, dtype=np.float32)\n     if corrected_count:\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n     for _ in range(32):\n         invalid = invalid_full_history_negatives(\n@@ -752,8 +759,8 @@\n         known_history,\n         liked_items,\n         len(prepared.item_ids),\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n         item_correction,\n         generator,\n         device,\n@@ -864,6 +871,34 @@\n     return result / total\n \n \n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n+\n+\n def retained_target_window_starts(\n     total_targets: np.ndarray,\n     validation_counts: np.ndarray,\n@@ -1168,10 +1203,15 @@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n             negative_logits, negative_weights[begin:end], config\n         )\n+        positive_loss = F.softplus(-positive_logits)\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n         selected_weights = torch.from_numpy(\n             sample_weights[begin:end].astype(np.float32, copy=False)\n         ).to(device)\n@@ -1625,11 +1665,20 @@\n             config.max_train_targets_per_user,\n             config.max_validation_targets_per_user,\n         )\n+        stored_mining_values = (\n+            stored_config.get(\"negative_pool_multiplier\", 1),\n+            stored_config.get(\"hard_negatives\", 0),\n+        )\n+        current_mining_values = (\n+            config.negative_pool_multiplier,\n+            config.hard_negatives,\n+        )\n         objective_changed = (\n             stored_filter_scope != config.negative_filter_scope\n             or stored_target_weighting != config.target_weighting\n             or stored_weight_values != current_weight_values\n             or stored_window_values != current_window_values\n+            or stored_mining_values != current_mining_values\n         )\n         model.load_state_dict(payload[\"model_state\"])\n         if \"optimizer_state\" in payload and not objective_changed:\n@@ -1946,6 +1995,18 @@\n         default=Config.negative_filter_scope,\n     )\n     parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n+    parser.add_argument(\n         \"--target-weighting\",\n         choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n         default=Config.target_weighting,\n@@ -2018,6 +2079,9 @@\n         or args.discovery_horizon_days < 1\n         or args.max_train_targets_per_user < 0\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n     if not 0.0 < args.validation_fraction < 1.0:\n@@ -2031,6 +2095,8 @@\n         epochs=args.epochs,\n         learning_rate=args.learning_rate,\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n         negative_filter_scope=args.negative_filter_scope,\n         target_weighting=args.target_weighting,\n         ordinary_target_weight=args.ordinary_target_weight,\n@@ -2111,7 +2177,7 @@\n     print(\n         json.dumps(\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-semi-hard\",\n                 \"stage\": args.stage,\n                 \"output\": str(args.output.resolve()),\n                 \"rows\": len(ranking),\n@@ -2134,7 +2200,7 @@\n                     \"score\": \"raw-dot-product\",\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                     \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"64-filtered-proposals-to-4-hard-plus-28-random\"\n                     ),\n                     \"false_negative_filter\": config.negative_filter_scope,\n                     \"target_weighting\": config.target_weighting,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 89487782
    },
    "formatted_output": "--- /task/current_model/train_current_model.py\t2026-09-23 21:34:36.000000000 +0000\n+++ /app/solution/train_current_model.py\t2026-09-23 22:29:02.762203021 +0000\n@@ -10,10 +10,12 @@\n into the next block.\n \n The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+normalization, raw dot-product retrieval, and sample-weighted binary cross\n+entropy.  Each target draws 48 uniform plus 16 propensity-corrected unique\n+in-batch proposals after chronological false-negative filtering.  The loss\n+retains four model-hard and 28 random proposals, limiting false-negative bias\n+while adding retrieval pressure. Every selected adjacent target retains a\n+nonzero weight. Only files under the supplied public directory are read.\n \"\"\"\n from __future__ import annotations\n \n@@ -48,13 +50,15 @@\n     item_hidden_1: int = 512\n     item_hidden_2: int = 256\n     dropout: float = 0.05\n-    epochs: int = 1\n+    epochs: int = 2\n     learning_rate: float = 1.0e-3\n     weight_decay: float = 1.0e-5\n     gradient_clip: float = 2.0\n     loss_microbatch_size: int = 65_536\n     uniform_negatives: int = 24\n     inbatch_negatives: int = 8\n+    negative_pool_multiplier: int = 2\n+    hard_negatives: int = 4\n     inbatch_correction_power: float = 0.5\n     inbatch_correction_min: float = 0.25\n     inbatch_correction_max: float = 4.0\n@@ -576,9 +580,10 @@\n \n \n def build_known_positive_filter(\n-    prepared: SequenceData, training_counts: np.ndarray\n+    prepared: SequenceData,\n+    training_counts: np.ndarray,\n ) -> np.ndarray:\n-    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n+    \"\"\"Pack each user's complete chronological training-prefix positive set.\"\"\"\n     width = (len(prepared.item_ids) + 7) // 8\n     packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n     retained = 0\n@@ -642,18 +647,20 @@\n     targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n     liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n     batch_size = len(targets_cpu)\n+    uniform_count = config.uniform_negatives * config.negative_pool_multiplier\n+    inbatch_count = config.inbatch_negatives * config.negative_pool_multiplier\n     uniform = torch.randint(\n         n_items,\n-        (batch_size, config.uniform_negatives),\n+        (batch_size, uniform_count),\n         generator=generator,\n         device=\"cpu\",\n     )\n     inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n     corrected_count = 0\n-    if config.inbatch_negatives:\n+    if inbatch_count:\n         unique_targets = torch.unique(targets.detach().cpu())\n         corrected_count = min(\n-            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n+            inbatch_count, max(len(unique_targets) - 1, 0)\n         )\n         if corrected_count:\n             priorities = torch.rand(\n@@ -669,10 +676,10 @@\n                 priorities, k=corrected_count, dim=1\n             ).indices\n             inbatch = unique_targets[columns]\n-        if corrected_count < config.inbatch_negatives:\n+        if corrected_count < inbatch_count:\n             filler = torch.randint(\n                 n_items,\n-                (batch_size, config.inbatch_negatives - corrected_count),\n+                (batch_size, inbatch_count - corrected_count),\n                 generator=generator,\n                 device=\"cpu\",\n             )\n@@ -682,7 +689,7 @@\n     correction = np.ones_like(negatives, dtype=np.float32)\n     if corrected_count:\n         correction[\n-            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n+            :, uniform_count : uniform_count + corrected_count\n         ] = item_correction[inbatch[:, :corrected_count]].numpy()\n     for _ in range(32):\n         invalid = invalid_full_history_negatives(\n@@ -752,8 +759,8 @@\n         known_history,\n         liked_items,\n         len(prepared.item_ids),\n-        config.uniform_negatives,\n-        config.inbatch_negatives,\n+        config.uniform_negatives * config.negative_pool_multiplier,\n+        config.inbatch_negatives * config.negative_pool_multiplier,\n         item_correction,\n         generator,\n         device,\n@@ -864,6 +871,34 @@\n     return result / total\n \n \n+def select_mixed_hard_negatives(\n+    logits: torch.Tensor,\n+    correction: torch.Tensor,\n+    config: Config,\n+) -> tuple[torch.Tensor, torch.Tensor]:\n+    \"\"\"Retain model-confusing negatives plus a random exploration set.\n+\n+    Proposal rows are already unique and false-negative filtered. Mining only\n+    changes the training loss; serving remains one query/item dot product.\n+    \"\"\"\n+    retained = config.uniform_negatives + config.inbatch_negatives\n+    if logits.shape[1] == retained:\n+        return logits, correction\n+    hard_count = min(config.hard_negatives, retained)\n+    hard_columns = torch.topk(logits.detach(), k=hard_count, dim=1).indices\n+    selected = torch.zeros_like(logits, dtype=torch.bool)\n+    selected.scatter_(1, hard_columns, True)\n+    random_count = retained - hard_count\n+    if random_count:\n+        priorities = torch.rand_like(logits)\n+        priorities.masked_fill_(selected, -torch.inf)\n+        random_columns = torch.topk(priorities, k=random_count, dim=1).indices\n+        columns = torch.cat([hard_columns, random_columns], dim=1)\n+    else:\n+        columns = hard_columns\n+    return logits.gather(1, columns), correction.gather(1, columns)\n+\n+\n def retained_target_window_starts(\n     total_targets: np.ndarray,\n     validation_counts: np.ndarray,\n@@ -1168,10 +1203,15 @@\n         negative_logits = torch.einsum(\n             \"bd,bkd->bk\", query_vectors, negative_vectors\n         )\n-        positive_loss = F.softplus(-positive_logits)\n-        negative_loss = corrected_negative_loss(\n+        negative_logits, selected_negative_weights = select_mixed_hard_negatives(\n             negative_logits, negative_weights[begin:end], config\n         )\n+        positive_loss = F.softplus(-positive_logits)\n+        negative_raw = F.softplus(negative_logits)\n+        negative_loss = (\n+            (negative_raw * selected_negative_weights).sum(dim=1)\n+            / selected_negative_weights.sum(dim=1).clamp_min(1.0e-6)\n+        )\n         selected_weights = torch.from_numpy(\n             sample_weights[begin:end].astype(np.float32, copy=False)\n         ).to(device)\n@@ -1625,11 +1665,20 @@\n             config.max_train_targets_per_user,\n             config.max_validation_targets_per_user,\n         )\n+        stored_mining_values = (\n+            stored_config.get(\"negative_pool_multiplier\", 1),\n+            stored_config.get(\"hard_negatives\", 0),\n+        )\n+        current_mining_values = (\n+            config.negative_pool_multiplier,\n+            config.hard_negatives,\n+        )\n         objective_changed = (\n             stored_filter_scope != config.negative_filter_scope\n             or stored_target_weighting != config.target_weighting\n             or stored_weight_values != current_weight_values\n             or stored_window_values != current_window_values\n+            or stored_mining_values != current_mining_values\n         )\n         model.load_state_dict(payload[\"model_state\"])\n         if \"optimizer_state\" in payload and not objective_changed:\n@@ -1946,6 +1995,18 @@\n         default=Config.negative_filter_scope,\n     )\n     parser.add_argument(\n+        \"--negative-pool-multiplier\",\n+        type=int,\n+        default=Config.negative_pool_multiplier,\n+        help=\"draw this multiple of the 32 loss negatives before online mining\",\n+    )\n+    parser.add_argument(\n+        \"--hard-negatives\",\n+        type=int,\n+        default=Config.hard_negatives,\n+        help=\"number of model-hard negatives among the 32 retained per target\",\n+    )\n+    parser.add_argument(\n         \"--target-weighting\",\n         choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n         default=Config.target_weighting,\n@@ -2018,6 +2079,9 @@\n         or args.discovery_horizon_days < 1\n         or args.max_train_targets_per_user < 0\n         or args.max_validation_targets_per_user < 0\n+        or args.negative_pool_multiplier < 1\n+        or args.hard_negatives < 0\n+        or args.hard_negatives > Config.uniform_negatives + Config.inbatch_negatives\n     ):\n         parser.error(\"training counts, learning rate, and patience are invalid\")\n     if not 0.0 < args.validation_fraction < 1.0:\n@@ -2031,6 +2095,8 @@\n         epochs=args.epochs,\n         learning_rate=args.learning_rate,\n         loss_microbatch_size=args.loss_microbatch_size,\n+        negative_pool_multiplier=args.negative_pool_multiplier,\n+        hard_negatives=args.hard_negatives,\n         negative_filter_scope=args.negative_filter_scope,\n         target_weighting=args.target_weighting,\n         ordinary_target_weight=args.ordinary_target_weight,\n@@ -2111,7 +2177,7 @@\n     print(\n         json.dumps(\n             {\n-                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n+                \"model\": \"yambda-windowed-sequence-two-tower-semi-hard\",\n                 \"stage\": args.stage,\n                 \"output\": str(args.output.resolve()),\n                 \"rows\": len(ranking),\n@@ -2134,7 +2200,7 @@\n                     \"score\": \"raw-dot-product\",\n                     \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                     \"negative_sampling\": (\n-                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n+                        \"64-filtered-proposals-to-4-hard-plus-28-random\"\n                     ),\n                     \"false_negative_filter\": config.negative_filter_scope,\n                     \"target_weighting\": config.target_weighting,\n"
  }
}
87

exec command

+49:27
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"f2a588","wall_time_seconds":30.001086884,"session_id":3190,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"}
Harness context token usage record
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  }
}
88

exec command

+50:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"928647","wall_time_seconds":30.001134308,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"0e690c","wall_time_seconds":30.000911181,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"5ae4bc","wall_time_seconds":30.000940694,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"773ae5","wall_time_seconds":30.001387933,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"931ad7","wall_time_seconds":30.000861584,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
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}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"94ef86","wall_time_seconds":30.001434579,"session_id":3190,"original_token_count":24,"output":"epoch=1/2 batches=250 targets=994,228 batch_bce=0.478605 running_bce=0.566793 elapsed=253.0s\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab453858ba487d29d6af608084ee992",
  "usage": {
    "input_tokens": 87359,
    "cached_input_tokens": 87040,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 87405
  },
  "turn_token_usage": {
    "input_tokens": 8292598,
    "cached_input_tokens": 8189568,
    "cache_write_input_tokens": 0,
    "output_tokens": 22530,
    "reasoning_output_tokens": 7907,
    "total_tokens": 8315128
  },
  "thread_token_usage": {
    "input_tokens": 8292598,
    "cached_input_tokens": 8189568,
    "cache_write_input_tokens": 0,
    "output_tokens": 22530,
    "reasoning_output_tokens": 7907,
    "total_tokens": 8315128
  }
}
89

exec command

+53:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"43a27f","wall_time_seconds":30.001294986,"session_id":3190,"original_token_count":59,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56370 validation_loss=0.53222 pair_accuracy=0.719 train_targets=1,067,606 validation_targets=131,997 train_seconds=271.4 validation_seconds=22.7\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab453a79c2c87d285cda4f6b6de00cf",
  "usage": {
    "input_tokens": 87497,
    "cached_input_tokens": 87168,
    "cache_write_input_tokens": 0,
    "output_tokens": 58,
    "reasoning_output_tokens": 10,
    "total_tokens": 87555
  },
  "turn_token_usage": {
    "input_tokens": 8380095,
    "cached_input_tokens": 8276736,
    "cache_write_input_tokens": 0,
    "output_tokens": 22588,
    "reasoning_output_tokens": 7917,
    "total_tokens": 8402683
  },
  "thread_token_usage": {
    "input_tokens": 8380095,
    "cached_input_tokens": 8276736,
    "cache_write_input_tokens": 0,
    "output_tokens": 22588,
    "reasoning_output_tokens": 7917,
    "total_tokens": 8402683
  }
}
90

exec command

+53:56
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"ae9475","wall_time_seconds":30.000886749,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab453c8b14487d2a04c1de219e02013",
  "usage": {
    "input_tokens": 87683,
    "cached_input_tokens": 87296,
    "cache_write_input_tokens": 0,
    "output_tokens": 74,
    "reasoning_output_tokens": 26,
    "total_tokens": 87757
  },
  "turn_token_usage": {
    "input_tokens": 8467778,
    "cached_input_tokens": 8364032,
    "cache_write_input_tokens": 0,
    "output_tokens": 22662,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8490440
  },
  "thread_token_usage": {
    "input_tokens": 8467778,
    "cached_input_tokens": 8364032,
    "cache_write_input_tokens": 0,
    "output_tokens": 22662,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8490440
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"c794f0","wall_time_seconds":30.000724751,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab453eb008887d28ffc3dc25b569f42",
  "usage": {
    "input_tokens": 87814,
    "cached_input_tokens": 87552,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 87860
  },
  "turn_token_usage": {
    "input_tokens": 8555592,
    "cached_input_tokens": 8451584,
    "cache_write_input_tokens": 0,
    "output_tokens": 22708,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8578300
  },
  "thread_token_usage": {
    "input_tokens": 8555592,
    "cached_input_tokens": 8451584,
    "cache_write_input_tokens": 0,
    "output_tokens": 22708,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8578300
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"fc4202","wall_time_seconds":30.001014496,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4540bb87087d2bca17f0f102a15f8",
  "usage": {
    "input_tokens": 87918,
    "cached_input_tokens": 87680,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 87964
  },
  "turn_token_usage": {
    "input_tokens": 8643510,
    "cached_input_tokens": 8539264,
    "cache_write_input_tokens": 0,
    "output_tokens": 22754,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8666264
  },
  "thread_token_usage": {
    "input_tokens": 8643510,
    "cached_input_tokens": 8539264,
    "cache_write_input_tokens": 0,
    "output_tokens": 22754,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8666264
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"495fae","wall_time_seconds":30.001577825,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4542c686887d28e36d40077322e13",
  "usage": {
    "input_tokens": 88021,
    "cached_input_tokens": 87680,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 88067
  },
  "turn_token_usage": {
    "input_tokens": 8731531,
    "cached_input_tokens": 8626944,
    "cache_write_input_tokens": 0,
    "output_tokens": 22800,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8754331
  },
  "thread_token_usage": {
    "input_tokens": 8731531,
    "cached_input_tokens": 8626944,
    "cache_write_input_tokens": 0,
    "output_tokens": 22800,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8754331
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"d57fd5","wall_time_seconds":30.000924798,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4544d52f887d2ac837d59bbe53653",
  "usage": {
    "input_tokens": 88124,
    "cached_input_tokens": 87808,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 88170
  },
  "turn_token_usage": {
    "input_tokens": 8819655,
    "cached_input_tokens": 8714752,
    "cache_write_input_tokens": 0,
    "output_tokens": 22846,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8842501
  },
  "thread_token_usage": {
    "input_tokens": 8819655,
    "cached_input_tokens": 8714752,
    "cache_write_input_tokens": 0,
    "output_tokens": 22846,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8842501
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"396065","wall_time_seconds":30.001356167,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4546de36087d282c6b525b36ec6b6",
  "usage": {
    "input_tokens": 88228,
    "cached_input_tokens": 87936,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 88274
  },
  "turn_token_usage": {
    "input_tokens": 8907883,
    "cached_input_tokens": 8802688,
    "cache_write_input_tokens": 0,
    "output_tokens": 22892,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8930775
  },
  "thread_token_usage": {
    "input_tokens": 8907883,
    "cached_input_tokens": 8802688,
    "cache_write_input_tokens": 0,
    "output_tokens": 22892,
    "reasoning_output_tokens": 7943,
    "total_tokens": 8930775
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"2ae7da","wall_time_seconds":30.001469038,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab4548f52a487d2bb0c0a17840ab987",
  "usage": {
    "input_tokens": 88330,
    "cached_input_tokens": 88064,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 88376
  },
  "turn_token_usage": {
    "input_tokens": 8996213,
    "cached_input_tokens": 8890752,
    "cache_write_input_tokens": 0,
    "output_tokens": 22938,
    "reasoning_output_tokens": 7943,
    "total_tokens": 9019151
  },
  "thread_token_usage": {
    "input_tokens": 8996213,
    "cached_input_tokens": 8890752,
    "cache_write_input_tokens": 0,
    "output_tokens": 22938,
    "reasoning_output_tokens": 7943,
    "total_tokens": 9019151
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"963de4","wall_time_seconds":30.00112913,"session_id":3190,"original_token_count":24,"output":"epoch=2/2 batches=250 targets=985,686 batch_bce=0.478222 running_bce=0.488791 elapsed=256.2s\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab454b02c8087d2bf3451bfa1438ac7",
  "usage": {
    "input_tokens": 88434,
    "cached_input_tokens": 88192,
    "cache_write_input_tokens": 0,
    "output_tokens": 46,
    "reasoning_output_tokens": 0,
    "total_tokens": 88480
  },
  "turn_token_usage": {
    "input_tokens": 9084647,
    "cached_input_tokens": 8978944,
    "cache_write_input_tokens": 0,
    "output_tokens": 22984,
    "reasoning_output_tokens": 7943,
    "total_tokens": 9107631
  },
  "thread_token_usage": {
    "input_tokens": 9084647,
    "cached_input_tokens": 8978944,
    "cache_write_input_tokens": 0,
    "output_tokens": 22984,
    "reasoning_output_tokens": 7943,
    "total_tokens": 9107631
  }
}
91

exec command

+58:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"0d9ea4","wall_time_seconds":30.000998859,"session_id":3190,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48605 validation_loss=0.45771 pair_accuracy=0.794 train_targets=1,067,606 validation_targets=131,997 train_seconds=277.2 validation_seconds=21.2\nrestored best validation-BCE checkpoint: epoch=2 loss=0.457707\n"}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab454d0df1087d298db2c73e3e8242e",
  "usage": {
    "input_tokens": 88572,
    "cached_input_tokens": 88192,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 88626
  },
  "turn_token_usage": {
    "input_tokens": 9173219,
    "cached_input_tokens": 9067136,
    "cache_write_input_tokens": 0,
    "output_tokens": 23038,
    "reasoning_output_tokens": 7949,
    "total_tokens": 9196257
  },
  "thread_token_usage": {
    "input_tokens": 9173219,
    "cached_input_tokens": 9067136,
    "cache_write_input_tokens": 0,
    "output_tokens": 23038,
    "reasoning_output_tokens": 7949,
    "total_tokens": 9196257
  }
}
92

exec command

+58:54
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:8000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

{"chunk_id":"a6bfe8","wall_time_seconds":30.001343707,"session_id":3190,"original_token_count":0,"output":""}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "session_id": "01a0d035-ee58-7dc3-be99-cc0afb266620",
  "root_turn_id": "01a0d035-ee67-7972-ab75-2b2928538d0b",
  "response_id": "resp_02210e5ee7639c14016ab454f3364487d2b624bf608bf77735",
  "usage": {
    "input_tokens": 88773,
    "cached_input_tokens": 88448,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 6,
    "total_tokens": 88827
  },
  "turn_token_usage": {
    "input_tokens": 9261992,
    "cached_input_tokens": 9155584,
    "cache_write_input_tokens": 0,
    "output_tokens": 23092,
    "reasoning_output_tokens": 7955,
    "total_tokens": 9285084
  },
  "thread_token_usage": {
    "input_tokens": 9261992,
    "cached_input_tokens": 9155584,
    "cache_write_input_tokens": 0,
    "output_tokens": 23092,
    "reasoning_output_tokens": 7955,
    "total_tokens": 9285084
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({session_id:3190,chars:"",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r))

Tool response 

Script completed
Wall time 30.0 seconds
Output:

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Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9f20a05f-16be-450b-8026-ae29f043a146",
    "process_id": "3190",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/2 batches=250 targets=994,228 batch_bce=0.478605 running_bce=0.566793 elapsed=253.0s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56370 validation_loss=0.53222 pair_accuracy=0.719 train_targets=1,067,606 validation_targets=131,997 train_seconds=271.4 validation_seconds=22.7\nepoch=2/2 batches=250 targets=985,686 batch_bce=0.478222 running_bce=0.488791 elapsed=256.2s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48605 validation_loss=0.45771 pair_accuracy=0.794 train_targets=1,067,606 validation_targets=131,997 train_seconds=277.2 validation_seconds=21.2\nrestored best validation-BCE checkpoint: epoch=2 loss=0.457707\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 683.548,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5637007149914555,\n      \"positive_logit\": 0.5615078713751265,\n      \"negative_logit\": -0.5073811457780169,\n      \"pair_accuracy\": 0.7190139667630193,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 271.3634799480001,\n      \"validation_loss\": 0.5322182945979346,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 22.7082407150001\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4860542185398041,\n      \"positive_logit\": 0.885633078234663,\n      \"negative_logit\": -1.2068447200414483,\n      \"pair_accuracy\": 0.794258924874907,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 277.2085949389998,\n      \"validation_loss\": 0.45770715755320596,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 21.169446117000007\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8230187892913818,\n    \"query_norm_std\": 0.6988999247550964,\n    \"item_norm_mean\": 2.976486921310425,\n    \"item_norm_std\": 1.2747087478637695\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "stderr": "",
    "aggregated_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/2 batches=250 targets=994,228 batch_bce=0.478605 running_bce=0.566793 elapsed=253.0s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56370 validation_loss=0.53222 pair_accuracy=0.719 train_targets=1,067,606 validation_targets=131,997 train_seconds=271.4 validation_seconds=22.7\nepoch=2/2 batches=250 targets=985,686 batch_bce=0.478222 running_bce=0.488791 elapsed=256.2s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48605 validation_loss=0.45771 pair_accuracy=0.794 train_targets=1,067,606 validation_targets=131,997 train_seconds=277.2 validation_seconds=21.2\nrestored best validation-BCE checkpoint: epoch=2 loss=0.457707\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 683.548,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 2,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"negative_pool_multiplier\": 2,\n    \"hard_negatives\": 4,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5637007149914555,\n      \"positive_logit\": 0.5615078713751265,\n      \"negative_logit\": -0.5073811457780169,\n      \"pair_accuracy\": 0.7190139667630193,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 271.3634799480001,\n      \"validation_loss\": 0.5322182945979346,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 22.7082407150001\n    },\n    {\n      \"epoch\": 2.0,\n      \"loss\": 0.4860542185398041,\n      \"positive_logit\": 0.885633078234663,\n      \"negative_logit\": -1.2068447200414483,\n      \"pair_accuracy\": 0.794258924874907,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 641383.25,\n      \"batches\": 270.0,\n      \"seconds\": 277.2085949389998,\n      \"validation_loss\": 0.45770715755320596,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 80328.75,\n      \"validation_seconds\": 21.169446117000007\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8230187892913818,\n    \"query_norm_std\": 0.6988999247550964,\n    \"item_norm_mean\": 2.976486921310425,\n    \"item_norm_std\": 1.2747087478637695\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 687,
      "nanos": 917360113
    },
    "formatted_output": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nepoch=1/2 batches=250 targets=994,228 batch_bce=0.478605 running_bce=0.566793 elapsed=253.0s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/2 train_loss=0.56370 validation_loss=0.53222 pair_accuracy=0.719 train_targets=1,067,606 validation_targets=131,997 train_seconds=271.4 validation_seconds=22.7\nepoch=2/2 batches=250 targets=985,686 batch_bce=0.478222 running_bce=0.488791 elapsed=256.2s\nsaved epoch checkpoint: /app/solution/final_checkpoints/epoch-02.pt\nepoch=2/2 train_loss=0.48605 validation_loss=0.45771 pair_accuracy=0.794 train_targets=1,067,606 validation_targets=131,997 train_seconds=277.2 validation_seconds=21.2\nrestored best validation-BCE checkpoint: epoch=2 loss=0.457707\nranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 683.548,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": 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\"validation_seconds\": 21.169446117000007\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8230187892913818,\n    \"query_norm_std\": 0.6988999247550964,\n    \"item_norm_mean\": 2.976486921310425,\n    \"item_norm_std\": 1.2747087478637695\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"
  }
}
About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
case1-sol-high-cli-0-153-4-r05-recovery-20260923T212844Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d035-ee58-7dc3-be99-cc0afb266620
Source SHA-256
f439d82a3ab5496bcf8a8cf7fa7181989bb47428b846ee8f5b7d8c4d7f5296a0

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. System instructions present in this archive are included above.

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