yambda-discovery-ranking-two-tower-case-1 / run-007-gpt-5-6-sol-high-2026-08-31T12-16-20 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T19:24:34.849Z 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 rather than ", "I will do , not ". ## 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](/abs/path/app.py:12): 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](). * 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. ## Step 2 · developer · 2026-08-31T19:24:36.459Z ## Skills A skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism. ### 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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md) - skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /tmp/codex-home/skills/.system/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$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. ## Step 3 · developer · 2026-08-31T19:24:36.459Z 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: Sender: Payload: ``` 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. ## Step 4 · developer · 2026-08-31T19:24:36.459Z 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. ## Step 5 · user · 2026-08-31T19:24:36.459Z Here is a list of plugins that are available but not installed. - Airtable (airtable@openai-curated-remote) - Alpaca (alpaca@openai-curated-remote) - Apollo.io (apollo@openai-curated-remote) - Spotify (app-68de829bf7648191acd70a907364c67c@openai-curated-remote) - AllTrails (app-68f1afc5a6008191a701eaaab428816c@openai-curated-remote) - Instacart (app-691eab1e001081919e57189f8b2f03bc@openai-curated-remote) - Apple Music (app-6938a94a61d881918ef32cb999ff937c@openai-curated-remote) - LONA Trading Assistant (app-694336b0c0948191a4ad234f9942885b@openai-curated-remote) - SciSpace (app-69439d715a7c8191aed9e2f6649e105f@openai-curated-remote) - Tarot (app-6943a2c078b0819188de39e4fe168d9b@openai-curated-remote) - Todoist: To Do List & Calendar (app-6943b73823548191a9f9216c6790c453@openai-curated-remote) - Consensus (app-6943e6f4a928819195962de16fb9ffe4@openai-curated-remote) - Sider Scholar (app-6948b485f5bc8191adb4df13f369cec7@openai-curated-remote) - True Sky (app-69490a4a06148191a0dd78606a3dbf1f@openai-curated-remote) - Bigdata.com (app-69491eceef3c8191beb70788b7840429@openai-curated-remote) - Gamma (app-698a098735908191989f5788d7ee317e@openai-curated-remote) - Tredict (app-69aef5b699a0819184512d57743fc1cd@openai-curated-remote) - Maersk (app-69b2b5a768d4819190d3a86c5f12e6d9@openai-curated-remote) - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Parqet (app-69b68652f0308191a27d7c7096cab4f6@openai-curated-remote) - Interactive Brokers (IBKR) (app-69bc11db874881918718abaca20b68ce@openai-curated-remote) - Financial Datasets (app-69cacd9394a88191ba6564e1bb0430fa@openai-curated-remote) - Fathom (app-69d88b99c5c481918e8da9225737e1e9@openai-curated-remote) - vidIQ (app-69dd11f3e50c8191b1ca48d03cf7e2ad@openai-curated-remote) - TickTick:To-Do List & Calendar (app-69ddbaba3fb48191a825f22c21b0599d@openai-curated-remote) - Plaud (app-69f3c30d68288191bbd428a394a78407@openai-curated-remote) - Wolfram (app-69fe0bf66c8481919c513d799406436e@openai-curated-remote) - Runway (app-6a05e3b201788191be12b590b43e6ce3@openai-curated-remote) - Caliber (app-6a05e8f22d408191b13ba3897157f6df@openai-curated-remote) - COROS (app-6a0694cbb2608191bbefb74ba810ab68@openai-curated-remote) - TradingCursor (app-6a0d835ff1dc8191972eeabd14967446@openai-curated-remote) - CoinMarketCap (app-6a172fe86f5481919f73cbc3bc3ad5bb@openai-curated-remote) - Trello (app-6a20b18a639081918c1b438f8381b27e@openai-curated-remote) - Longbridge (app-6a2baf2fad748191812393c3e00308ef@openai-curated-remote) - freddy (app-6a322b52a82c8191b7fb653f9e9f7891@openai-curated-remote) - Higgsfield (app-6a3293e129088191abf0875820e839da@openai-curated-remote) - Stocktwits (app-6a427a19b1f481919c5db13838af00c2@openai-curated-remote) - Webull (app-6a4b8801d1e8819182f624f012878a81@openai-curated-remote) - CoinGecko (app-6a4f02d735388191959c8328877e0bbd@openai-curated-remote) - Asana (asana@openai-curated-remote) - Atlassian Rovo (atlassian-rovo@openai-curated-remote) - Base44 (base44@openai-curated-remote) - Binance (binance@openai-curated-remote) - Box (box@openai-curated-remote) - Canva (canva@openai-curated-remote) - ClickUp (clickup@openai-curated-remote) - Cloudflare (cloudflare@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) /app sh 2026-08-31 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-08-31", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/plugin-creator/SKILL.md)\n- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /tmp/codex-home/skills/.system/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: /tmp/codex-home/skills/.system/skill-installer/SKILL.md)\n", "includeInstructions": true }, "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": [] }, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a05947-d499-7920-bda5-94e710b7c837", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-08-31", "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" } ## Step 6 · user · 2026-08-31T19:24:36.469Z # 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 each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `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. ## Step 7 · agent · 2026-08-31T19:24:39.133Z ### Thinking unavailable — no text recorded I’ll first reproduce and profile the production baseline, then inspect its negative-sampling path and data distributions. I’ll keep every artifact under `/app/solution/`, preserve the chronological boundary and two-tower scorer, and save a valid submission early before iterating. ### exec · 2026-08-31T19:24:50.798Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Copy and inspect the production implementation and data shapes\",status:\"in_progress\"},\n {step:\"Reproduce the public baseline and diagnose negative sampling\",status:\"pending\"},\n {step:\"Implement and measure focused objective improvements\",status:\"pending\"},\n {step:\"Refit the best approach on train+validation and validate submission\",status:\"pending\"},\n {step:\"Write the experiment README and final handoff\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"mkdir -p /app/solution && cp /task/current_model/train_current_model.py /app/solution/train_current_model.py && cp /task/current_model/two_tower_model.py /app/solution/two_tower_model.py && rg -n \\\"negative|sample|loss|epoch|argparse|validation|stage|batch|eligible|score|rank\\\" /app/solution/*.py && sed -n '1,260p' /app/solution/two_tower_model.py && sed -n '1,320p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":30000});\ntext(p);\ntext(r);\n" } ### Tool output · 2026-08-31T19:24:50.884Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 14755) Total output lines: 2 {} {"chunk_id":"e924e9","wall_time_seconds":0.000060112,"exit_code":0,"original_token_count":14377,"output":"/app/solution/two_tower_model.py:36: epochs: int = 1\n/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:42: inbatch_correction_power: float = 0.5\n/app/solution/two_tower_model.py:43: inbatch_correction_min: float = 0.25\n/app/solution/two_tower_model.py:44: inbatch_correction_max: float = 4.0\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:114: for batch in parquet.iter_batches(\n/app/solution/two_tower_model.py:115: batch_size=750_000,\n/app/solution/two_tower_model.py:118: frame = batch.to_pandas()\n/app/solution/two_tower_model.py:303: batch_first=True,\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:369: inbatch_count: int,\n/app/solution/two_tower_model.py:374: \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n/app/solution/two_tower_model.py:375: batch_size = len(targets)\n/app/solution/two_tower_model.py:378: (batch_size, uniform_count),\n/app/solution/two_tower_model.py:382: inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n/app/solution/two_tower_model.py:384: if inbatch_count:\n/app/solution/two_tower_model.py:386: corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n/app/solution/two_tower_model.py:389: (batch_size, len(unique_targets)), generator=generator\n/app/solution/two_tower_model.py:396: inbatch = unique_targets[columns]\n/app/solution/two_tower_model.py:397: if corrected_count < inbatch_count:\n/app/solution/two_tower_model.py:400: (batch_size, inbatch_count - corrected_count),\n/app/solution/two_tower_model.py:404: inbatch = torch.cat([inbatch, filler], dim=1)\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:411: ] = item_correction[inbatch[:, :corrected_count]]\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:7:16 held-out validation targets; passing zero for either cap restores the\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:20:import argparse\n/app/solution/train_current_model.py:43: user_batch_size: int = 64\n/app/solution/train_current_model.py:51: epochs: int = 1\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:58: inbatch_correction_power: float = 0.5\n/app/solution/train_current_model.py:59: inbatch_correction_min: float = 0.25\n/app/solution/train_current_model.py:60: inbatch_correction_max: float = 4.0\n/app/solution/train_current_model.py:61: negative_filter_scope: str = \"full-training-history\"\n/app/solution/train_current_model.py:67: validation_fraction: float = 0.10\n/app/solution/train_current_model.py:69: max_validation_targets_per_user: int = 16\n/app/solution/train_current_model.py:87: packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n/app/solution/train_current_model.py:132: validation_counts: np.ndarray\n/app/solution/train_current_model.py:137: return int(self.training_counts.sum() + self.validation_counts.sum())\n/app/solution/train_current_model.py:161: epochs=config.epochs,\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:167: inbatch_correction_power=config.inbatch_correction_power,\n/app/solution/train_current_model.py:168: inbatch_correction_min=config.inbatch_correction_min,\n/app/solution/train_current_model.py:169: inbatch_correction_max=config.inbatch_correction_max,\n/app/solution/train_current_model.py:342: public: Path, stage: str, config: Config\n/app/solution/train_current_model.py:346: \"popularity_rank\"\n/app/solution/train_current_model.py:354: if stage == \"validation\":\n/app/solution/train_current_model.py:357: candidate_name = \"validation_eligible_candidates.npz\"\n/app/solution/train_current_model.py:358: state_name = \"validation_preference_state.parquet\"\n/app/solution/train_current_model.py:362: candidate_name = \"test_eligible_candidates.npz\"\n/app/solution/train_current_model.py:463: f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\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:474: \"stage\": stage,\n/app/solution/train_current_model.py:477: \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n/app/solution/train_current_model.py:482: path: Path, prepared: SequenceData, stage: str, config: Config\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: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:513: stage: str,\n/app/solution/train_current_model.py:518: \"validation_eligible_candidates.npz\"\n/app/solution/train_current_model.py:519: if stage == \"validation\"\n/app/solution/train_current_model.py:520: else \"test_eligible_candidates.npz\"\n/app/solution/train_current_model.py:524: prepared = load_cache(cache, stage, config)\n/app/solution/train_current_model.py:528: prepared, built_eligibility = prepare_data(public, stage, config)\n/app/solution/train_current_model.py:530: save_cache(cache, prepared, stage, config)\n/app/solution/train_current_model.py:540: \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n/app/solution/train_current_model.py:562: config.inbatch_correction_power,\n/app/solution/train_current_model.py:567: config.inbatch_correction_min,\n/app/solution/train_current_model.py:568: config.inbatch_correction_max,\n/app/solution/train_current_model.py:572: f\"inbatch correction: min={correction.min():.3f} \"\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:644: batch_size = len(targets_cpu)\n/app/solution/train_current_model.py:647: (batch_size, config.uniform_negatives),\n/app/solution/train_current_model.py:651: inbatch = torch.empty((batch_size, 0), dtype=torch.long)\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:660: (batch_size, len(unique_targets)), generator=generator\n/app/solution/train_current_model.py:671: inbatch = unique_targets[columns]\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:679: inbatch = torch.cat([inbatch, filler], dim=1)\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:686: ] = item_correction[inbatch[:, :corrected_count]].numpy()\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:707: # Rejected in-batch items become ordinary uniform draws.\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:869: validation_counts: np.ndarray,\n/app/solution/train_current_model.py:873: training_region_ends = total_targets - validation_counts\n/app/solution/train_current_model.py:878: or np.any(validation_counts < 0)\n/app/solution/train_current_model.py:880: raise RuntimeError(\"chronological target partition produced negative counts\")\n/app/solution/train_current_model.py:882: window_starts + training_counts + validation_counts, total_targets\n/app/solution/train_current_model.py:890: validation_fraction: float,\n/app/solution/train_current_model.py:892: max_validation_targets_per_user: int,\n/app/solution/train_current_model.py:896: validation = np.floor(totals * validation_fraction).astype(np.int64)\n/app/solution/train_current_model.py:897: validation[(totals >= 2) & (validation == 0)] = 1\n/app/solution/train_current_model.py:898: if max_validation_targets_per_user:\n/app/solution/train_current_model.py:899: validation = np.minimum(validation, max_validation_targets_per_user)\n/app/solution/train_current_model.py:901: history_training = totals - validation\n/app/solution/train_current_model.py:905: window_starts = retained_target_window_starts(totals, validation, training)\n/app/solution/train_current_model.py:909: validation_counts=validation,\n/app/solution/train_current_model.py:915: public: Path, stage: str, item_ids: np.ndarray\n/app/solution/train_current_model.py:918: prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n/app/solution/train_current_model.py:957: stage: str,\n/app/solution/train_current_model.py:978: public, stage, prepared.item_ids\n/app/solution/train_current_model.py:981: base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n/app/solution/train_current_model.py:991: first_validation_target = last_training_target + 1\n/app/solution/train_current_model.py:993: int(prepared.sequence_timestamps[first_validation_target])\n/app/solution/train_current_model.py:994: if first_validation_target < end\n/app/solution/train_current_model.py:1005: # eligible discovery outcomes at the corresponding\n/app/solution/train_current_model.py:1041: score_starts: np.ndarray,\n/app/solution/train_current_model.py:1046: \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n/app/solution/train_current_model.py:1047: batch_size = len(users)\n/app/solution/train_current_model.py:1049: input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n/app/solution/train_current_model.py:1050: event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n/app/solution/train_current_model.py:1051: activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n/app/solution/train_current_model.py:1052: state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n/app/solution/train_current_model.py:1053: targets = np.zeros((batch_size, steps), dtype=np.uint16)\n/app/solution/train_current_model.py:1054: weights = np.ones((batch_size, steps), dtype=np.float32)\n/app/solution/train_current_model.py:1055: scored = np.zeros((batch_size, steps), dtype=bool)\n/app/solution/train_current_model.py:1057: (batch_size, steps, config.bptt_steps),\n/app/solution/train_current_model.py:1061: processed = np.zeros(batch_size, dtype=np.int32)\n/app/solution/train_current_model.py:1085: score_from = min(count, max(int(score_starts[row]) - start, 0))\n/app/solution/train_current_model.py:1086: scored[row, score_from:count] = True\n/app/solution/train_current_model.py:1104: scored,\n/app/solution/train_current_model.py:1111:def score_target_microbatches(\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:1186:…4755 tokens truncated…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#!/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 return None\n\n event_features = np.zeros((len(items), 3), dtype=np.float16)\n event_features[:, 0] = organic.astype(np.float16)\n event_features[:, 1] = (\n np.minimum(completion, 100.0) / 100.0\n ).astype(np.float16)\n if len(items) > 1:\n gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n event_features[1:, 2] = np.exp(\n -gaps.astype(np.float64) / (60.0 * base.DAY)\n ).astype(np.float16)\n\n activity = causal_activity_features(\n timestamps, items, organic, completion\n )\n states = np.zeros(len(items), dtype=np.uint32)\n active_likes: dict[int, int] = {}\n active_dislikes: dict[int, int] = {}\n explicit_position = 0\n current_state = 0\n for row, timestamp in enumerate(timestamps):\n changed = False\n while (\n explicit_position < len(explicit.timestamps)\n and int(explicit.timestamps[explicit_position]) < int(timestamp)\n ):\n item = int(explicit.items[explicit_position])\n event_timestamp = int(explicit.timestamps[explicit_position])\n destination = (\n active_likes\n if int(explicit.channel[explicit_position]) == 0\n else active_dislikes\n )\n if int(explicit.active[explicit_position]) == 1:\n destination[item] = event_timestamp\n else:\n destination.pop(item, None)\n explicit_position += 1\n changed = True\n if changed:\n current_state = append_state_snapshot(\n active_likes,\n active_dislikes,\n n_items,\n liked_states,\n disliked_states,\n state_counts,\n )\n states[row] = np.uint32(current_state)\n return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n timestamps: np.ndarray,\n items: np.ndarray,\n event_features: np.ndarray,\n history_end: int,\n like_count: int,\n dislike_count: int,\n"} ## Step 8 · agent · 2026-08-31T19:24:52.733Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:24:56.707Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '520,900p' /app/solution/train_current_model.py && sed -n '900,1320p' /app/solution/train_current_model.py && sed -n '1320,1700p' /app/solution/train_current_model.py && du -sh /task/data && ls -lh /task/data /task/current_model\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":40000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:24:56.769Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 11630) Total output lines: 1 {"chunk_id":"0a1dcd","wall_time_seconds":0.000062242,"exit_code":0,"original_token_count":11211,"output":" else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n prepared: SequenceData,\n config: Config,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\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 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\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 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 padding_item = len(prepared.item_id…1630 tokens truncated…_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 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\n@torch.no_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 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\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,\n340M\t/task/data\n/task/current_model:\ntotal 96K\n-r--r--r-- 1 root root 79K Aug 31 19:19 train_current_model.py\n-r--r--r-- 1 root root 16K Aug 31 19:19 two_tower_model.py\n\n/task/data:\ntotal 340M\n-r--r--r-- 1 root root 342K Aug 31 19:19 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Aug 31 19:19 target_users.parquet\n-r--r--r-- 1 root root 7.4M Aug 31 19:19 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Aug 31 19:20 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Aug 31 19:19 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Aug 31 19:19 train_likes.parquet\n-r--r--r-- 1 root root 305M Aug 31 19:19 train_listens.parquet\n-r--r--r-- 1 root root 153K Aug 31 19:19 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Aug 31 19:19 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Aug 31 19:19 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Aug 31 19:19 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Aug 31 19:19 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Aug 31 19:19 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Aug 31 19:19 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Aug 31 19:19 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Aug 31 19:20 validation_preference_state.parquet\n"} ## Step 9 · agent · 2026-08-31T19:24:58.419Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:00.364Z { "code": "const r = await tools.exec_command({cmd:\"python /app/solution/train_current_model.py --help | sed -n '1,260p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":12000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:00.578Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"eae27c","wall_time_seconds":0.037940465,"exit_code":0,"original_token_count":43,"output":"Traceback (most recent call last):\n File \"/app/solution/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"} ## Step 10 · agent · 2026-08-31T19:25:02.628Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:05.624Z { "code": "const r = await tools.exec_command({cmd:\"which python; python --version; ls -la /app; ls -la /task | sed -n '1,120p'; sed -n '1450,1750p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":22000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:05.681Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"eaff31","wall_time_seconds":0.00000305,"exit_code":0,"original_token_count":2796,"output":"/usr/local/bin/python\nPython 3.12.13\ntotal 16\ndrwxr-xr-x 1 agent agent 4096 Aug 31 19:21 .\ndrwxr-xr-x 1 root root 4096 Aug 31 19:24 ..\ndrwxr-xr-x 1 agent agent 4096 Aug 31 19:24 solution\ntotal 40\ndr-xr-xr-x 1 root root 4096 Aug 31 19:21 .\ndrwxr-xr-x 1 root root 4096 Aug 31 19:24 ..\n-r--r--r-- 1 root root 0 Aug 31 19:19 .keep\ndr-xr-xr-x 1 root root 4096 Aug 31 19:19 current_model\ndr-xr-xr-x 1 root root 4096 Aug 31 19:20 data\n-r--r--r-- 1 root root 5774 Aug 31 19:19 evaluate_public.py\n-r--r--r-- 1 root root 8265 Aug 31 19:19 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Aug 31 19:19 requirements.txt\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\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"} ## Step 11 · agent · 2026-08-31T19:25:07.941Z ### Thinking unavailable — no text recorded The copied script is intact, but the default `python` shim is not the supplied scientific environment—it has no NumPy. I’m locating the prebuilt runtime now; I won’t install or modify packages. Once found, I’ll launch the baseline and capture both its checkpoint and public score for apples-to-apples experiments. ### exec · 2026-08-31T19:25:14.954Z { "code": "const r = await tools.exec_command({cmd:\"find / -maxdepth 4 -type f \\\\( -name 'python' -o -name 'python3' \\\\) 2>/dev/null | head -80; find / -maxdepth 5 -type d -name 'site-packages' 2>/dev/null | head -80; sed -n '1,200p' /task/requirements.txt; env | sort\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":16000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:15.105Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"4489b4","wall_time_seconds":0.000007531,"exit_code":0,"original_token_count":503,"output":"/opt/task-venv/lib/python3.12/site-packages\n/usr/local/lib/python3.12/site-packages\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\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_THREAD_ID=01a05947-d483-76b3-964f-f69c965010ae\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=71dcaf4e023d\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.147.0-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n"} ## Step 12 · agent · 2026-08-31T19:25:17.186Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:21.796Z { "code": "const r = await tools.exec_command({cmd:\"ls -la /opt/task-venv/bin | sed -n '1,100p'; /opt/task-venv/bin/python --version; /opt/task-venv/bin/python -c 'import numpy,pandas,torch,pyarrow; print(numpy.__version__,torch.__version__)'; /opt/task-venv/bin/python /app/solution/train_current_model.py --help | sed -n '1,260p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":18000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:29.685Z Script completed Wall time 7.9 seconds Output: {"chunk_id":"14ce1a","wall_time_seconds":7.710166245,"exit_code":0,"original_token_count":1445,"output":"total 124\ndr-xr-xr-x 1 root root 4096 Aug 31 19:21 .\ndr-xr-xr-x 1 root root 4096 Aug 31 19:21 ..\n-r--r--r-- 1 root root 9033 Aug 31 19:21 Activate.ps1\n-r--r--r-- 1 root root 2158 Aug 31 19:21 activate\n-r--r--r-- 1 root root 920 Aug 31 19:21 activate.csh\n-r--r--r-- 1 root root 2195 Aug 31 19:21 activate.fish\n-r-xr-xr-x 1 root root 302 Aug 31 19:21 chardetect\n-r-xr-xr-x 1 root root 304 Aug 31 19:21 dotenv\n-r-xr-xr-x 1 root root 308 Aug 31 19:21 f2py\n-r-xr-xr-x 1 root root 299 Aug 31 19:21 idna\n-r-xr-xr-x 1 root root 297 Aug 31 19:21 isympy\n-r-xr-xr-x 1 root root 305 Aug 31 19:21 jsonschema\n-r-xr-xr-x 1 root root 311 Aug 31 19:21 jupyter\n-r-xr-xr-x 1 root root 311 Aug 31 19:21 jupyter-migrate\n-r-xr-xr-x 1 root root 316 Aug 31 19:21 jupyter-troubleshoot\n-r-xr-xr-x 1 root root 344 Aug 31 19:21 jupyter-trust\n-r-xr-xr-x 1 root root 311 Aug 31 19:21 jupytext\n-r-xr-xr-x 1 root root 315 Aug 31 19:21 jupytext-config\n-r-xr-xr-x 1 root root 301 Aug 31 19:21 kaggle\n-r-xr-xr-x 1 root root 312 Aug 31 19:21 markdown-it\n-r-xr-xr-x 1 root root 325 Aug 31 19:21 normalizer\n-r-xr-xr-x 1 root root 308 Aug 31 19:21 numpy-config\n-r-xr-xr-x 1 root root 313 Aug 31 19:21 pip\n-r-xr-xr-x 1 root root 313 Aug 31 19:21 pip3\n-r-xr-xr-x 1 root root 313 Aug 31 19:21 pip3.12\nlrwxrwxrwx 1 root root 21 Aug 31 19:21 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root 6 Aug 31 19:21 python3 -> python\nlrwxrwxrwx 1 root root 6 Aug 31 19:21 python3.12 -> python\n-r-xr-xr-x 1 root root 307 Aug 31 19:21 slugify\n-r-xr-xr-x 1 root root 321 Aug 31 19:21 torchfrtrace\n-r-xr-xr-x 1 root root 312 Aug 31 19:21 torchrun\n-r-xr-xr-x 1 root root 299 Aug 31 19:21 tqdm\nPython 3.12.13\n2.2.6 2.8.0+cpu\nusage: train_current_model.py [-h] [--stage {validation,submission}] --output\n OUTPUT --public-dir PUBLIC_DIR\n [--prepared-cache PREPARED_CACHE]\n [--device {cpu,mps}] [--threads THREADS]\n [--bptt-steps BPTT_STEPS]\n [--user-batch-size USER_BATCH_SIZE]\n [--epochs EPOCHS]\n [--learning-rate LEARNING_RATE]\n [--loss-microbatch-size LOSS_MICROBATCH_SIZE]\n [--negative-filter-scope {full-training-history,last-100}]\n [--target-weighting {discovery-graded,organic-strong,uniform}]\n [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]\n [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]\n [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]\n [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]\n [--validation-fraction VALIDATION_FRACTION]\n [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]\n [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]\n [--early-stopping-patience EARLY_STOPPING_PATIENCE]\n [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]\n [--checkpoint-dir CHECKPOINT_DIR]\n [--resume-checkpoint RESUME_CHECKPOINT]\n [--seed SEED]\n [--ranking-batch-size RANKING_BATCH_SIZE]\n [--prepare-only]\n\nTrain the Yambda windowed two-tower baseline. Every catalog listen with at\nleast 50 percent completion is a sequence token. Within each selected\nchronological window, every adjacent target contributes a loss. The fast\ndefault retains each user's most recent 128 training targets and 16 held-out\nvalidation targets; passing zero for either cap restores the uncapped behavior\nfor that side of the split. A 100-step truncated-BPTT block computes all of\nits timestep losses and carries the detached GRU hidden state into the next\nblock. The model is a pure two-tower retriever: no user-ID embedding, no\noutput normalization, raw dot-product retrieval, sample-weighted binary cross\nentropy, and 24 uniform plus eight propensity-corrected unique in-batch\nnegatives. Every selected adjacent target retains a nonzero weight. Only files\nunder the supplied public directory are read.\n\noptions:\n -h, --help show this help message and exit\n --stage {validation,submission}\n --output OUTPUT\n --public-dir PUBLIC_DIR\n --prepared-cache PREPARED_CACHE\n --device {cpu,mps}\n --threads THREADS\n --bptt-steps BPTT_STEPS\n --user-batch-size USER_BATCH_SIZE\n --epochs EPOCHS\n --learning-rate LEARNING_RATE\n --loss-microbatch-size LOSS_MICROBATCH_SIZE\n --negative-filter-scope {full-training-history,last-100}\n --target-weighting {discovery-graded,organic-strong,uniform}\n --ordinary-target-weight ORDINARY_TARGET_WEIGHT\n --organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT\n --organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT\n --discovery-horizon-days DISCOVERY_HORIZON_DAYS\n --validation-fraction VALIDATION_FRACTION\n --max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER\n retain this many recent pre-validation targets per\n user; 0 is uncapped\n --max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER\n retain this many final validation targets per user; 0\n is uncapped\n --early-stopping-patience EARLY_STOPPING_PATIENCE\n --early-stopping-min-delta EARLY_STOPPING_MIN_DELTA\n --checkpoint-dir CHECKPOINT_DIR\n --resume-checkpoint RESUME_CHECKPOINT\n --seed SEED\n --ranking-batch-size RANKING_BATCH_SIZE\n --prepare-only\n"} ## Step 13 · agent · 2026-08-31T19:25:32.294Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:35.011Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:36.189Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"fe64e1","wall_time_seconds":1.001759911,"session_id":43690,"original_token_count":0,"output":""} ## Step 14 · agent · 2026-08-31T19:25:43.407Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:44.685Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:1000,max_output_tokens:4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:25:49.708Z Script completed Wall time 5.0 seconds Output: {"chunk_id":"609d6c","wall_time_seconds":5.001560021,"session_id":43690,"original_token_count":29,"output":"loaded explicit events: rows=720,270 users=8,220\r\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\r\n"} ## Step 15 · agent · 2026-08-31T19:25:51.431Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:52.771Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:26:03.792Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-08-31T19:26:06.488Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:26:07.253Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:26:22.795Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"638867","wall_time_seconds":30.001593719,"session_id":43690,"original_token_count":120,"output":"prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\r\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\r\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s\r\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s\r\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\r\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\r\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s\r\n"} ## Step 17 · agent · 2026-08-31T19:26:24.151Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:26:25.442Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:26:36.460Z Script running with cell ID 10 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:26:38.353Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:26:55.480Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"477279","wall_time_seconds":30.002087744,"session_id":43690,"original_token_count":146,"output":"prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\r\ntarget users without qualifying catalog sequence=7\r\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\n"} ## Step 18 · agent · 2026-08-31T19:26:56.889Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:26:58.173Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:27:09.227Z Script running with cell ID 11 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:27:11.235Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:27:28.239Z Script completed Wall time 17.0 seconds Output: {"chunk_id":"677555","wall_time_seconds":30.000920797,"session_id":43690,"original_token_count":0,"output":""} ## Step 19 · agent · 2026-08-31T19:27:29.977Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:27:31.311Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:27:42.361Z Script running with cell ID 12 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:27:44.607Z { "cell_id": "12", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:28:01.365Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"fd90ce","wall_time_seconds":30.001337759,"session_id":43690,"original_token_count":0,"output":""} ## Step 20 · agent · 2026-08-31T19:28:14.956Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:28:17.057Z { "code": "const r = await tools.exec_command({cmd:\"ps -eo pid,etime,%cpu,%mem,rss,cmd | rg 'train_current|PID'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:28:17.133Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"62dca4","wall_time_seconds":0.00000412,"exit_code":1,"original_token_count":7,"output":"/bin/sh: 1: ps: not found\n"} ## Step 21 · agent · 2026-08-31T19:28:18.508Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:28:19.847Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:28:30.865Z Script running with cell ID 14 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:28:33.717Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:28:49.868Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"da766f","wall_time_seconds":30.000601891,"session_id":43690,"original_token_count":0,"output":""} ## Step 22 · agent · 2026-08-31T19:28:51.739Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:28:53.037Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:29:04.062Z Script running with cell ID 15 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:29:06.409Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:29:23.067Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"a7c39b","wall_time_seconds":30.00160646,"session_id":43690,"original_token_count":0,"output":""} ## Step 23 · agent · 2026-08-31T19:29:24.909Z ### Thinking unavailable — no text recorded The baseline has prepared 20.0M chronological sequence tokens but trains on only the recent 1.05M targets, with 32 negatives per target. Its key inefficiency is now concrete: 24/32 negatives are uniformly sampled from all 30,000 tracks, so most are trivially irrelevant; the remaining eight come only from positives in the current user batch and are inverse-propensity corrected, which suppresses precisely the popular confusable items that dominate retrieval errors. I’m measuring the baseline before changing that balance. ## Step 24 · agent · 2026-08-31T19:29:47.621Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-08-31T19:29:53.903Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:55.213Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:30:06.236Z Script running with cell ID 16 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:30:08.425Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:30:25.245Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"5136dd","wall_time_seconds":30.000975593,"session_id":43690,"original_token_count":82,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.7s\r\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=211.9 validation_seconds=15.7\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\r\n"} ## Step 26 · agent · 2026-08-31T19:30:26.688Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:28.028Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:30:39.067Z Script running with cell ID 17 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:30:41.014Z { "cell_id": "17", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:30:58.073Z Script completed Wall time 17.1 seconds Output: {"chunk_id":"a1b2e7","wall_time_seconds":30.000970465,"session_id":43690,"original_token_count":0,"output":""} ## Step 27 · agent · 2026-08-31T19:30:59.446Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:00.747Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:31:11.771Z Script running with cell ID 18 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:31:14.136Z { "cell_id": "18", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:31:30.775Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"9fae2b","wall_time_seconds":30.000998181,"session_id":43690,"original_token_count":0,"output":""} ## Step 28 · agent · 2026-08-31T19:31:32.870Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:34.194Z { "code": "const r = await tools.write_stdin({session_id:43690,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:31:42.321Z Script completed Wall time 8.1 seconds Output: {"chunk_id":"d04afa","wall_time_seconds":8.063631504,"exit_code":0,"original_token_count":826,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/production_validation.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 362.915,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.5,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5398380465267117,\r\n \"positive_logit\": 0.7427799576763272,\r\n \"negative_logit\": -0.6222247048211599,\r\n \"pair_accuracy\": 0.7547733612041563,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 632551.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 211.89062101000002,\r\n \"validation_loss\": 0.49663621699855015,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 78769.25,\r\n \"validation_seconds\": 15.689767996\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.603016972541809,\r\n \"query_norm_std\": 0.7697497010231018,\r\n \"item_norm_mean\": 1.7570281028747559,\r\n \"item_norm_std\": 0.8279485702514648\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 29 · agent · 2026-08-31T19:31:43.914Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:46.081Z { "code": "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\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:31:49.444Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"4b5c95","wall_time_seconds":3.18478748,"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"} ## Step 30 · agent · 2026-08-31T19:31:57.006Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:00.454Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"def parse_args|ArgumentParser|Config\\\\(|uniform-neg|inbatch\\\" /app/solution/train_current_model.py | tail -50; sed -n '1780,2060p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":18000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:32:00.556Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"1bfe84","wall_time_seconds":0.000008221,"exit_code":0,"original_token_count":3231,"output":"57: inbatch_negatives: int = 8\n58: inbatch_correction_power: float = 0.5\n59: inbatch_correction_min: float = 0.25\n60: inbatch_correction_max: float = 4.0\n152: return base.TowerConfig(\n166: inbatch_negatives=config.inbatch_negatives,\n167: inbatch_correction_power=config.inbatch_correction_power,\n168: inbatch_correction_min=config.inbatch_correction_min,\n169: inbatch_correction_max=config.inbatch_correction_max,\n562: config.inbatch_correction_power,\n567: config.inbatch_correction_min,\n568: config.inbatch_correction_max,\n572: f\"inbatch correction: min={correction.min():.3f} \"\n651: inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n653: if config.inbatch_negatives:\n656: config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n671: inbatch = unique_targets[columns]\n672: if corrected_count < config.inbatch_negatives:\n675: (batch_size, config.inbatch_negatives - corrected_count),\n679: inbatch = torch.cat([inbatch, filler], dim=1)\n681: negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n686: ] = item_correction[inbatch[:, :corrected_count]].numpy()\n756: config.inbatch_negatives,\n851: total = config.uniform_negatives + config.inbatch_negatives\n857: if config.inbatch_negatives:\n860: result += config.inbatch_negatives * (\n1227: negative_count = config.uniform_negatives + config.inbatch_negatives\n1923: parser = argparse.ArgumentParser(description=__doc__)\n2028: config = Config(\n2137: \"24-uniform-plus-8-propensity-corrected-inbatch\"\n\n@torch.inference_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 ordered = np.argsort(lengths, kind=\"stable\")\n result = torch.zeros(\n len(sequence_rows), config.embedding_dim, dtype=torch.float32\n )\n for group_start in range(0, len(ordered), config.user_batch_size):\n output_rows = ordered[group_start : group_start + config.user_batch_size]\n rows = sequence_rows[output_rows]\n group_lengths = lengths[output_rows]\n hidden = torch.zeros(\n 1, len(output_rows), config.embedding_dim, device=device\n )\n for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n steps = min(\n config.bptt_steps,\n int(group_lengths.max(initial=0)) - start,\n )\n items = np.full(\n (len(output_rows), steps),\n len(prepared.item_ids),\n dtype=np.uint16,\n )\n features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n counts = np.zeros(len(output_rows), dtype=np.int32)\n for local_row, sequence_row in enumerate(rows):\n if sequence_row < 0:\n continue\n count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[sequence_row])\n selected = slice(offset + start, offset + start + count)\n items[local_row, :count] = prepared.sequence_items[selected]\n features[local_row, :count] = prepared.event_features[selected]\n counts[local_row] = count\n outputs, _ = event_sequence(\n model,\n torch.from_numpy(items.astype(np.int64)).to(device),\n torch.from_numpy(features.astype(np.float32)).to(device),\n hidden,\n )\n next_rows: list[torch.Tensor] = []\n for local_row, count in enumerate(counts):\n next_rows.append(\n outputs[local_row, count - 1]\n if count\n else hidden[0, local_row]\n )\n hidden = torch.stack(next_rows).unsqueeze(0)\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\n@torch.inference_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 )\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"} ## Step 31 · agent · 2026-08-31T19:32:03.428Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:19.094Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n@@\\n default=Config.loss_microbatch_size,\\n )\\n+ parser.add_argument(\\n+ \\\"--uniform-negatives\\\", type=int, default=Config.uniform_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-power\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_power,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-min\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_min,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-max\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_max,\\n+ )\\n@@\\n or args.max_validation_targets_per_user < 0\\n+ or args.uniform_negatives < 0\\n+ or args.inbatch_negatives < 0\\n+ or args.uniform_negatives + args.inbatch_negatives < 1\\n+ or args.inbatch_correction_power < 0\\n+ or args.inbatch_correction_min <= 0\\n+ or args.inbatch_correction_max < args.inbatch_correction_min\\n@@\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ uniform_negatives=args.uniform_negatives,\\n+ inbatch_negatives=args.inbatch_negatives,\\n+ inbatch_correction_power=args.inbatch_correction_power,\\n+ inbatch_correction_min=args.inbatch_correction_min,\\n+ inbatch_correction_max=args.inbatch_correction_max,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:32:19.147Z Script failed Wall time 0.0 seconds Output: Script error: Exit code: 1 Wall time: 0 seconds Output: Failed to write file /app/solution/train_current_model.py ## Step 32 · agent · 2026-08-31T19:32:21.058Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:24.446Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /app/solution/train_current_model.py /app/solution/two_tower_model.py; id; chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py; ls -l /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:32:24.504Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"3f29e9","wall_time_seconds":0.00000231,"exit_code":0,"original_token_count":73,"output":"-r--r--r-- 1 agent agent 80058 Aug 31 19:24 /app/solution/train_current_model.py\n-r--r--r-- 1 agent agent 15985 Aug 31 19:24 /app/solution/two_tower_model.py\nuid=1000(agent) gid=1000(agent) groups=1000(agent)\n-rw-r--r-- 1 agent agent 80058 Aug 31 19:24 /app/solution/train_current_model.py\n"} ## Step 33 · agent · 2026-08-31T19:32:25.950Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:41.741Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n@@\\n default=Config.loss_microbatch_size,\\n )\\n+ parser.add_argument(\\n+ \\\"--uniform-negatives\\\", type=int, default=Config.uniform_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-negatives\\\", type=int, default=Config.inbatch_negatives\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-power\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_power,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-min\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_min,\\n+ )\\n+ parser.add_argument(\\n+ \\\"--inbatch-correction-max\\\",\\n+ type=float,\\n+ default=Config.inbatch_correction_max,\\n+ )\\n@@\\n or args.max_validation_targets_per_user < 0\\n+ or args.uniform_negatives < 0\\n+ or args.inbatch_negatives < 0\\n+ or args.uniform_negatives + args.inbatch_negatives < 1\\n+ or args.inbatch_correction_power < 0\\n+ or args.inbatch_correction_min <= 0\\n+ or args.inbatch_correction_max < args.inbatch_correction_min\\n@@\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ uniform_negatives=args.uniform_negatives,\\n+ inbatch_negatives=args.inbatch_negatives,\\n+ inbatch_correction_power=args.inbatch_correction_power,\\n+ inbatch_correction_min=args.inbatch_correction_min,\\n+ inbatch_correction_max=args.inbatch_correction_max,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:32:41.788Z Script completed Wall time 0.0 seconds Output: {} ## Step 34 · agent · 2026-08-31T19:32:44.022Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:47.067Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '450,525p' /app/solution/train_current_model.py; /opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py /app/solution/two_tower_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:32:47.256Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"afd3f7","wall_time_seconds":0.00002466,"exit_code":0,"original_token_count":697,"output":" sequence_items=np.concatenate(item_parts),\n event_features=np.concatenate(event_parts),\n activity_features=np.concatenate(activity_parts),\n state_ids=np.concatenate(state_parts),\n liked_states=np.stack(liked_states),\n disliked_states=np.stack(disliked_states),\n state_counts=np.asarray(state_counts, dtype=np.int32),\n inference_user_rows=inference_sequence_rows,\n inference_liked_items=inference_likes,\n inference_disliked_items=inference_dislikes,\n inference_user_features=inference_user_features,\n )\n log(\n f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n f\"tokens={len(prepared.sequence_items):,} \"\n f\"next_item_targets={prepared.target_count:,} \"\n f\"states={len(prepared.liked_states):,}\"\n )\n return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n"} ## Step 35 · agent · 2026-08-31T19:32:49.578Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:52.825Z { "code": "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/cache_only.parquet --prepared-cache /app/solution/validation_prepared.npz --prepare-only\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:32:54.003Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"e5b820","wall_time_seconds":1.001642307,"session_id":78333,"original_token_count":0,"output":""} ## Step 36 · agent · 2026-08-31T19:32:55.950Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:57.252Z { "code": "const r = await tools.write_stdin({session_id:78333,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:33:08.273Z Script running with cell ID 27 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:33:10.267Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T19:33:27.277Z Script completed Wall time 17.0 seconds Output: {"chunk_id":"417619","wall_time_seconds":30.001045249,"session_id":78333,"original_token_count":97,"output":"loaded explicit events: rows=720,270 users=8,220\r\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\r\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\r\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\r\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s\r\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s\r\n"} ## Step 37 · agent · 2026-08-31T19:33:28.833Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:30.107Z { "code": "const r = await tools.write_stdin({session_id:78333,chars:\"\",yield_time_ms:30000,max_output_tokens:4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:33:41.128Z Script running with cell ID 28 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:33:43.012Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 4000 } ### Tool output · 2026-08-31T19:33:47.616Z Script completed Wall time 4.6 seconds Output: {"chunk_id":"c6ee8a","wall_time_seconds":17.484401144,"exit_code":0,"original_token_count":461,"output":"prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.1s\r\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.6s\r\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\r\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.6s\r\ntarget users without qualifying catalog sequence=7\r\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\r\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\r\n{\r\n \"stage\": \"validation\",\r\n \"sequence_users\": 9135,\r\n \"sequence_tokens\": 20056329,\r\n \"next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"target_users\": 8456,\r\n \"runtime_seconds\": 50.729,\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.5,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n }\r\n}\r\n"} ## Step 38 · agent · 2026-08-31T19:33:50.556Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:55.093Z { "code": "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/validation_inbatch24.parquet --prepared-cache /app/solution/validation_prepared.npz --uniform-negatives 8 --inbatch-negatives 24 --inbatch-correction-power 0 --checkpoint-dir /app/solution/ckpt_inbatch24\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:33:56.270Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"d71550","wall_time_seconds":1.001298358,"session_id":7769,"original_token_count":0,"output":""} ## Step 39 · agent · 2026-08-31T19:33:59.314Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:34:00.568Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:34:11.591Z Script running with cell ID 30 Wall time 11.0 seconds Output: ## Step 40 · agent · 2026-08-31T19:34:13.218Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:34:14.140Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:34:30.591Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"46be2f","wall_time_seconds":30.001018523,"session_id":7769,"original_token_count":104,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\n"} ## Step 41 · agent · 2026-08-31T19:34:32.175Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:34:33.471Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:34:44.493Z Script running with cell ID 31 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:34:46.516Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:35:03.503Z Script completed Wall time 17.0 seconds Output: {"chunk_id":"1c2e46","wall_time_seconds":30.00127021,"session_id":7769,"original_token_count":0,"output":""} ## Step 42 · agent · 2026-08-31T19:35:04.962Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:35:06.230Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:35:17.283Z Script running with cell ID 32 Wall time 11.0 seconds Output: ### wait · 2026-08-31T19:35:19.406Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:35:36.297Z Script completed Wall time 16.9 seconds Output: {"chunk_id":"a2b986","wall_time_seconds":30.001566404,"session_id":7769,"original_token_count":0,"output":""} ## Step 43 · agent · 2026-08-31T19:35:37.725Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:35:39.030Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:35:50.080Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T19:35:51.836Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:35:52.674Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:36:09.081Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"e2b12a","wall_time_seconds":30.00156919,"session_id":7769,"original_token_count":0,"output":""} ## Step 45 · agent · 2026-08-31T19:36:10.517Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:11.892Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:36:22.917Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T19:36:24.482Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:36:25.256Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:36:41.915Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"4ddf29","wall_time_seconds":30.000794995,"session_id":7769,"original_token_count":0,"output":""} ## Step 47 · agent · 2026-08-31T19:36:43.308Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:44.732Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:36:55.752Z Script running with cell ID 35 Wall time 11.0 seconds Output: ## Step 48 · agent · 2026-08-31T19:36:57.474Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:36:58.248Z { "cell_id": "35", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:37:14.761Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"74f3af","wall_time_seconds":30.001338122,"session_id":7769,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.579406 running_bce=0.602767 elapsed=170.4s\r\n"} ## Step 49 · agent · 2026-08-31T19:37:16.442Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:17.738Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:37:28.781Z Script running with cell ID 36 Wall time 11.0 seconds Output: ## Step 50 · agent · 2026-08-31T19:37:30.861Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:37:31.636Z { "cell_id": "36", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:37:47.785Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"54ee9c","wall_time_seconds":30.001008303,"session_id":7769,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/ckpt_inbatch24/epoch-01.pt\r\nepoch=1/1 train_loss=0.59968 validation_loss=0.54718 pair_accuracy=0.706 train_targets=1,052,953 validation_targets=130,103 train_seconds=182.0 validation_seconds=12.4\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.547179\r\n"} ## Step 51 · agent · 2026-08-31T19:37:49.770Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:51.069Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:38:02.112Z Script running with cell ID 37 Wall time 11.0 seconds Output: ## Step 52 · agent · 2026-08-31T19:38:03.775Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:38:04.494Z { "cell_id": "37", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:38:21.119Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"4d2b64","wall_time_seconds":30.001016404,"session_id":7769,"original_token_count":0,"output":""} ## Step 53 · agent · 2026-08-31T19:38:22.910Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:24.211Z { "code": "const r = await tools.write_stdin({session_id:7769,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:38:35.236Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 54 · agent · 2026-08-31T19:38:37.168Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:38:37.960Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:38:39.404Z Script completed Wall time 1.4 seconds Output: {"chunk_id":"74a902","wall_time_seconds":15.167652077,"exit_code":0,"original_token_count":826,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/validation_inbatch24.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 279.858,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 8,\r\n \"inbatch_negatives\": 24,\r\n \"inbatch_correction_power\": 0.0,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5996801647357424,\r\n \"positive_logit\": 0.37033814791565095,\r\n \"negative_logit\": -0.445143784681676,\r\n \"pair_accuracy\": 0.705832964529281,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 632551.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 182.022563686,\r\n \"validation_loss\": 0.5471792653076595,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 78769.25,\r\n \"validation_seconds\": 12.389283175000173\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.9200323820114136,\r\n \"query_norm_std\": 1.0379804372787476,\r\n \"item_norm_mean\": 1.600951910018921,\r\n \"item_norm_std\": 0.725330650806427\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 55 · agent · 2026-08-31T19:38:40.789Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:43.158Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_inbatch24.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:38:46.573Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"d744a8","wall_time_seconds":3.234195418,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/validation_inbatch24.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012552,\n \"quality_users\": 4263\n }\n}\n"} ## Step 56 · agent · 2026-08-31T19:38:58.613Z ### Thinking unavailable — no text recorded The first intervention is decisively worse: shifting from 24 uniform / 8 corrected in-batch negatives to 8 uniform / 24 uncorrected in-batch drops public NDCG@10 from `0.021280` to `0.012552`. That falsifies the “mostly popularity-confusable negatives” version of the hypothesis—the model over-suppresses popular items. I’m narrowing the change rather than carrying that regression forward: isolate the propensity correction at the original 24/8 mixture, and separately measure a popularity-only diagnostic to determine which direction the sampler’s item distribution should move. ### exec · 2026-08-31T19:39:11.265Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -c 'import numpy as np,pandas as pd; z=np.load(\\\"/task/data/validation_eligible_candidates.npz\\\"); u=z[\\\"uids\\\"]; items=z[\\\"item_ids\\\"]; m=np.unpackbits(z[\\\"packed_eligible\\\"],axis=1,count=len(items),bitorder=\\\"little\\\").astype(bool); rows=[]; [rows.extend((int(uid),int(items[j]),r) for r,j in enumerate(np.flatnonzero(m[i])[:100],1)) for i,uid in enumerate(u)]; pd.DataFrame(rows,columns=[\\\"uid\\\",\\\"item_id\\\",\\\"rank\\\"]).to_parquet(\\\"/app/solution/validation_popularity.parquet\\\",index=False)' && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_popularity.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:39:17.317Z Script completed Wall time 6.0 seconds Output: {"chunk_id":"a9c97b","wall_time_seconds":5.865939571,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/validation_popularity.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n"} ## Step 57 · agent · 2026-08-31T19:39:19.127Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:23.632Z { "code": "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/validation_uncorrected.parquet --prepared-cache /app/solution/validation_prepared.npz --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 0 --checkpoint-dir /app/solution/ckpt_uncorrected\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:39:24.816Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"9d74b4","wall_time_seconds":1.001783613,"session_id":40106,"original_token_count":0,"output":""} ## Step 58 · agent · 2026-08-31T19:39:26.958Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:28.346Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:39:39.373Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 59 · agent · 2026-08-31T19:39:41.102Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:39:42.122Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:39:58.379Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"a9fb67","wall_time_seconds":30.001705291,"session_id":40106,"original_token_count":104,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\n"} ## Step 60 · agent · 2026-08-31T19:40:00.034Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:01.389Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:40:12.437Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 61 · agent · 2026-08-31T19:40:13.952Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:40:14.745Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:40:31.447Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"b9c4ed","wall_time_seconds":30.000744518,"session_id":40106,"original_token_count":0,"output":""} ## Step 62 · agent · 2026-08-31T19:40:33.647Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:34.362Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:40:45.381Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 63 · agent · 2026-08-31T19:40:47.283Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:40:48.072Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:41:04.395Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"0b9f7b","wall_time_seconds":30.001242797,"session_id":40106,"original_token_count":0,"output":""} ## Step 64 · agent · 2026-08-31T19:41:06.026Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:41:07.362Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:41:18.385Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 65 · agent · 2026-08-31T19:41:20.638Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:21.438Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:41:37.400Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"d235a8","wall_time_seconds":30.002419278,"session_id":40106,"original_token_count":0,"output":""} ## Step 66 · agent · 2026-08-31T19:41:39.001Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:41:40.302Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:41:51.343Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 67 · agent · 2026-08-31T19:41:52.911Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:53.690Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:42:10.353Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"982c9e","wall_time_seconds":30.001101821,"session_id":40106,"original_token_count":0,"output":""} ## Step 68 · agent · 2026-08-31T19:42:11.845Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:42:13.191Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:42:24.216Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 69 · agent · 2026-08-31T19:42:25.654Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:26.429Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:42:43.230Z Script completed Wall time 16.8 seconds Output: {"chunk_id":"9b9f02","wall_time_seconds":30.001442057,"session_id":40106,"original_token_count":0,"output":""} ## Step 70 · agent · 2026-08-31T19:42:45.280Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:42:46.550Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:42:57.588Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 71 · agent · 2026-08-31T19:42:59.106Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:59.874Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:43:16.597Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"f3b21f","wall_time_seconds":30.001783106,"session_id":40106,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.562498 running_bce=0.562160 elapsed=197.4s\r\n"} ## Step 72 · agent · 2026-08-31T19:43:17.990Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:43:19.564Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:43:30.600Z Script running with cell ID 49 Wall time 11.0 seconds Output: ## Step 73 · agent · 2026-08-31T19:43:32.587Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:43:33.357Z { "cell_id": "49", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:43:49.608Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"79ae1f","wall_time_seconds":30.002093818,"session_id":40106,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/ckpt_uncorrected/epoch-01.pt\r\nepoch=1/1 train_loss=0.56012 validation_loss=0.51430 pair_accuracy=0.757 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=15.4\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.514304\r\n"} ## Step 74 · agent · 2026-08-31T19:43:50.995Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:43:52.273Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:44:03.320Z Script running with cell ID 50 Wall time 11.0 seconds Output: ## Step 75 · agent · 2026-08-31T19:44:04.980Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:44:05.755Z { "cell_id": "50", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:44:22.325Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"3b661f","wall_time_seconds":30.00013802,"session_id":40106,"original_token_count":0,"output":""} ## Step 76 · agent · 2026-08-31T19:44:24.144Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:25.485Z { "code": "const r = await tools.write_stdin({session_id:40106,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:44:36.519Z Script running with cell ID 51 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T19:44:39.710Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:44:40.528Z { "cell_id": "51", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:44:40.610Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"afd501","wall_time_seconds":13.84910884,"exit_code":0,"original_token_count":828,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/validation_uncorrected.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 311.293,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.0,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5601169318002907,\r\n \"positive_logit\": 0.5843293791440944,\r\n \"negative_logit\": -0.6334363934293068,\r\n \"pair_accuracy\": 0.7566698430509244,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 632551.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 210.59745993399997,\r\n \"validation_loss\": 0.5143042038258614,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 78769.25,\r\n \"validation_seconds\": 15.435132388000056\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.6756763458251953,\r\n \"query_norm_std\": 0.8129033446311951,\r\n \"item_norm_mean\": 1.824887752532959,\r\n \"item_norm_std\": 0.8556308746337891\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 78 · agent · 2026-08-31T19:44:42.215Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:44.470Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_uncorrected.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:44:47.835Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"7313dd","wall_time_seconds":3.188669001,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/validation_uncorrected.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018522,\n \"quality_users\": 4263\n }\n}\n"} ## Step 79 · agent · 2026-08-31T19:44:54.266Z ### Thinking unavailable — no text recorded The isolated result confirms that the correction is beneficial: removing it at the original 24/8 mix scores `0.018522`, below the `0.021280` baseline. Popularity-only is also poor (`0.010710`), so the target is not “recommend more popular music”; it is preserving personalized separation while preventing frequently observed items from dominating the negative gradient. I’m now testing a stronger propensity correction at the original negative mix—the smallest change supported by both ablations. ### exec · 2026-08-31T19:45:02.843Z { "code": "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/validation_correction1.parquet --prepared-cache /app/solution/validation_prepared.npz --uniform-negatives 24 --inbatch-negatives 8 --inbatch-correction-power 1.0 --checkpoint-dir /app/solution/ckpt_correction1\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:45:04.021Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"a9a92f","wall_time_seconds":1.00171199,"session_id":10621,"original_token_count":0,"output":""} ## Step 80 · agent · 2026-08-31T19:45:06.180Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:45:07.088Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:45:18.115Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T19:45:19.526Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:45:20.574Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:45:37.113Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"12c98d","wall_time_seconds":30.00128267,"session_id":10621,"original_token_count":104,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=0.250 mean_on_targets=0.971 max=4.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\n"} ## Step 82 · agent · 2026-08-31T19:45:38.550Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:45:40.083Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:45:51.137Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 83 · agent · 2026-08-31T19:45:53.029Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:45:53.779Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:46:10.137Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"b92381","wall_time_seconds":30.000536659,"session_id":10621,"original_token_count":0,"output":""} ## Step 84 · agent · 2026-08-31T19:46:11.512Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:12.818Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:46:23.841Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T19:46:25.400Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:46:26.193Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:46:42.854Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"4909f3","wall_time_seconds":30.000918918,"session_id":10621,"original_token_count":0,"output":""} ## Step 86 · agent · 2026-08-31T19:46:44.211Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:45.568Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:46:56.592Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T19:46:58.180Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:46:58.976Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:47:15.590Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"8b5866","wall_time_seconds":30.001371167,"session_id":10621,"original_token_count":0,"output":""} ## Step 88 · agent · 2026-08-31T19:47:17.088Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:18.409Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:47:29.439Z Script running with cell ID 58 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T19:47:31.386Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:47:32.172Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:47:48.459Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"f0299d","wall_time_seconds":30.001942565,"session_id":10621,"original_token_count":0,"output":""} ## Step 90 · agent · 2026-08-31T19:47:49.814Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:51.130Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:48:02.172Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 91 · agent · 2026-08-31T19:48:04.317Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:48:05.649Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:48:21.174Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"97e702","wall_time_seconds":30.00095095,"session_id":10621,"original_token_count":0,"output":""} ## Step 92 · agent · 2026-08-31T19:48:22.612Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:48:23.920Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:48:34.979Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 93 · agent · 2026-08-31T19:48:37.124Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:48:37.913Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:48:53.994Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"42de93","wall_time_seconds":30.000910663,"session_id":10621,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.536809 running_bce=0.530386 elapsed=197.5s\r\n"} ## Step 94 · agent · 2026-08-31T19:48:56.288Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:48:57.638Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:49:08.678Z Script running with cell ID 61 Wall time 11.0 seconds Output: ## Step 95 · agent · 2026-08-31T19:49:10.346Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:49:11.272Z { "cell_id": "61", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:49:27.692Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"e6dd9e","wall_time_seconds":30.000984505,"session_id":10621,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/ckpt_correction1/epoch-01.pt\r\nepoch=1/1 train_loss=0.52862 validation_loss=0.48561 pair_accuracy=0.753 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.6 validation_seconds=14.8\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.485609\r\n"} ## Step 96 · agent · 2026-08-31T19:49:29.227Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:49:30.546Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:49:41.579Z Script running with cell ID 62 Wall time 11.0 seconds Output: ## Step 97 · agent · 2026-08-31T19:49:43.072Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:49:43.851Z { "cell_id": "62", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:50:00.596Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"cd9cc5","wall_time_seconds":30.001304403,"session_id":10621,"original_token_count":0,"output":""} ## Step 98 · agent · 2026-08-31T19:50:02.004Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:50:03.320Z { "code": "const r = await tools.write_stdin({session_id:10621,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:50:14.363Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 99 · agent · 2026-08-31T19:50:16.272Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:50:17.050Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:50:18.087Z Script completed Wall time 1.0 seconds Output: {"chunk_id":"eacc59","wall_time_seconds":14.723834529,"exit_code":0,"original_token_count":828,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/validation_correction1.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 310.864,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 1.0,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5286242768457803,\r\n \"positive_logit\": 0.8037698270722188,\r\n \"negative_logit\": -0.607055753345073,\r\n \"pair_accuracy\": 0.7532777460152542,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 632551.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 210.64749365800003,\r\n \"validation_loss\": 0.4856087773830317,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 78769.25,\r\n \"validation_seconds\": 14.769704917999888\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.5412524938583374,\r\n \"query_norm_std\": 0.718069314956665,\r\n \"item_norm_mean\": 1.8196759223937988,\r\n \"item_norm_std\": 0.8474172949790955\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 100 · agent · 2026-08-31T19:50:20.651Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:50:23.027Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_correction1.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:50:26.388Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"6b749e","wall_time_seconds":3.18313121,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/validation_correction1.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020848,\n \"quality_users\": 4263\n }\n}\n"} ## Step 101 · agent · 2026-08-31T19:50:46.089Z ### Thinking unavailable — no text recorded ## Step 102 · agent · 2026-08-31T19:50:51.884Z ### Thinking unavailable — no text recorded A stronger correction improves the internal BCE but not the launch metric (`0.020848` versus `0.021280`), which is a useful warning: sampled BCE is not a reliable selector for top-10 discovery. Before spending another full fit, I’m checking whether the remaining error is catalog-prior calibration or personalization. I’ll use only cutoff-correct rankings for this diagnostic; validation outcomes remain confined to the evaluator. ## Step 103 · agent · 2026-08-31T19:51:05.171Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:51:17.139Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/blend_diagnostics.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build cutoff-correct rank-fusion diagnostics without reading outcomes.\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+\\n+import pandas as pd\\n+\\n+\\n+ROOT = Path(\\\"/app/solution\\\")\\n+model = pd.read_parquet(ROOT / \\\"production_validation.parquet\\\")\\n+prior = pd.read_parquet(ROOT / \\\"validation_popularity.parquet\\\")\\n+model_lists = model.sort_values([\\\"uid\\\", \\\"rank\\\"]).groupby(\\\"uid\\\").item_id.apply(list)\\n+prior_lists = prior.sort_values([\\\"uid\\\", \\\"rank\\\"]).groupby(\\\"uid\\\").item_id.apply(list)\\n+\\n+for count in (1, 2, 5, 10, 20):\\n+ rows = []\\n+ for uid, base_items in model_lists.items():\\n+ head = prior_lists.loc[uid][:count]\\n+ merged = list(dict.fromkeys(head + base_items))[:100]\\n+ rows.extend((int(uid), int(item), rank) for rank, item in enumerate(merged, 1))\\n+ pd.DataFrame(rows, columns=[\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]).to_parquet(\\n+ ROOT / f\\\"validation_pop_insert_{count}.parquet\\\", index=False\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:51:17.195Z Script completed Wall time 0.0 seconds Output: {} ## Step 104 · agent · 2026-08-31T19:51:18.735Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:51:22.530Z { "code": "let{output,...rest}=await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/blend_diagnostics.py && for k in 1 2 5 10 20; do /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_pop_insert_${k}.parquet; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":8000});text(rest);text(output)" } ### Tool output · 2026-08-31T19:51:33.550Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 105 · agent · 2026-08-31T19:51:35.247Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:51:36.052Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 8000 } ### Tool output · 2026-08-31T19:51:45.014Z Script completed Wall time 9.0 seconds Output: {"chunk_id":"ff69b7","wall_time_seconds":22.302076794,"exit_code":0,"original_token_count":176} { "model": "/app/solution/validation_pop_insert_1.parquet", "scores": { "quality_ndcg@10": 0.019042, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_insert_2.parquet", "scores": { "quality_ndcg@10": 0.01796, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_insert_5.parquet", "scores": { "quality_ndcg@10": 0.015483, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_insert_10.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } { "model": "/app/solution/validation_pop_insert_20.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ## Step 106 · agent · 2026-08-31T19:52:04.802Z ### Thinking unavailable — no text recorded ## Step 107 · agent · 2026-08-31T19:52:17.681Z ### Thinking unavailable — no text recorded ## Step 108 · agent · 2026-08-31T19:52:19.445Z ### Thinking unavailable — no text recorded The calibration diagnostic is unambiguous: inserting even one global-popularity item lowers NDCG, so the gain must come from better personalized negatives rather than a serving-time prior. I found a more specific mismatch in the production sampler: its “full-history” filter rejects only positive sequence items and active likes, while the platform candidate boundary excludes every prior encounter—including weak listens and explicit-negative/reversal events. The trainer therefore spends negative budget on items that can never enter that user’s serving candidate set. I’m implementing a candidate-boundary-aware rejection layer for target users, with the existing full-history filter as fallback for non-target training users. This changes only negative selection, not features, labels, cutoffs, or the two-tower scoring contract. ## Step 109 · agent · 2026-08-31T19:52:26.064Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:52:45.682Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n def build_known_positive_filter(\\n@@\\n return packed\\n+\\n+\\n+def add_serving_candidate_filter(\\n+ prepared: SequenceData,\\n+ eligibility: CandidateSet,\\n+ invalid_packed: np.ndarray,\\n+) -> np.ndarray:\\n+ \\\"\\\"\\\"Also reject items absent from each target user's serving candidate set.\\\"\\\"\\\"\\n+ result = invalid_packed.copy()\\n+ prepared_rows = pd.Index(prepared.user_ids).get_indexer(eligibility.uids)\\n+ if np.any(prepared_rows < 0):\\n+ raise RuntimeError(\\\"candidate user is absent from prepared training users\\\")\\n+ # Candidate artifacts store one bits for allowed items; the negative filter\\n+ # stores one bits for forbidden items. This excludes every prior encounter,\\n+ # not just qualifying positive listens, and matches indexed retrieval.\\n+ result[prepared_rows] |= np.bitwise_not(eligibility.packed)\\n+ log(\\n+ f\\\"serving-candidate negative filter: target_users={len(prepared_rows):,} \\\"\\n+ f\\\"bytes={result.nbytes:,}\\\"\\n+ )\\n+ return result\\n@@\\n def train_model(\\n public: Path,\\n stage: str,\\n prepared: SequenceData,\\n+ eligibility: CandidateSet,\\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+ known_positive_packed = None\\n+ if config.negative_filter_scope in (\\n+ \\\"full-training-history\\\",\\n+ \\\"serving-candidates\\\",\\n+ ):\\n+ known_positive_packed = build_known_positive_filter(\\n+ prepared, partition.history_training_counts\\n+ )\\n+ if config.negative_filter_scope == \\\"serving-candidates\\\":\\n+ known_positive_packed = add_serving_candidate_filter(\\n+ prepared, eligibility, known_positive_packed\\n+ )\\n@@\\n- choices=(\\\"full-training-history\\\", \\\"last-100\\\"),\\n+ choices=(\\\"serving-candidates\\\", \\\"full-training-history\\\", \\\"last-100\\\"),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:52:45.720Z Script completed Wall time 0.0 seconds Output: {} ## Step 110 · agent · 2026-08-31T19:52:48.484Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:52:50.787Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"train_model\\\\(|negative_filter_scope ==|negative_filter_scope in\\\" /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:52:50.855Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"da1070","wall_time_seconds":0.000004481,"exit_code":0,"original_token_count":64,"output":"757: if config.negative_filter_scope == \"full-training-history\":\n1542:def train_model(\n1588: if config.negative_filter_scope in (\n1595: if config.negative_filter_scope == \"serving-candidates\":\n2154: model, training_history = train_model(\n"} ## Step 111 · agent · 2026-08-31T19:52:52.589Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:52:55.276Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '740,770p' /app/solution/train_current_model.py; sed -n '2140,2170p' /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:52:55.332Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"8cffcc","wall_time_seconds":0.0000052,"exit_code":0,"original_token_count":482,"output":" 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 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"} ## Step 112 · agent · 2026-08-31T19:52:57.277Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:01.643Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- if config.negative_filter_scope == \\\"full-training-history\\\":\\n+ if config.negative_filter_scope in (\\n+ \\\"full-training-history\\\",\\n+ \\\"serving-candidates\\\",\\n+ ):\\n@@\\n model, training_history = train_model(\\n public,\\n args.stage,\\n prepared,\\n+ eligibility,\\n config,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:53:01.730Z Script completed Wall time 0.0 seconds Output: {} ## Step 113 · agent · 2026-08-31T19:53:03.601Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:06.412Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py && rg -n \\\"def train_model|train_model\\\\(\\\" /app/solution/train_current_model.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:53:06.597Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"d60d95","wall_time_seconds":0.000007291,"exit_code":0,"original_token_count":18,"output":"1545:def train_model(\n2157: model, training_history = train_model(\n"} ## Step 114 · agent · 2026-08-31T19:53:08.394Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:12.398Z { "code": "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/validation_serving_negatives.parquet --prepared-cache /app/solution/validation_prepared.npz --negative-filter-scope serving-candidates --checkpoint-dir /app/solution/ckpt_serving\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:53:13.576Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"632c8b","wall_time_seconds":1.001257034,"session_id":64695,"original_token_count":0,"output":""} ## Step 115 · agent · 2026-08-31T19:53:15.070Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:16.379Z { "code": "const r = await tools.write_stdin({session_id:64695,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:53:20.630Z Script completed Wall time 4.2 seconds Output: {"chunk_id":"9074ee","wall_time_seconds":4.226894914,"exit_code":1,"original_token_count":293,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\nTraceback (most recent call last):\r\n File \"/app/solution/train_current_model.py\", line 2229, in \r\n raise SystemExit(main())\r\n ^^^^^^\r\n File \"/app/solution/train_current_model.py\", line 2157, in main\r\n model, training_history = train_model(\r\n ^^^^^^^^^^^^\r\n File \"/app/solution/train_current_model.py\", line 1599, in train_model\r\n known_positive_packed = add_serving_candidate_filter(\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/app/solution/train_current_model.py\", line 618, in add_serving_candidate_filter\r\n raise RuntimeError(\"candidate user is absent from prepared training users\")\r\nRuntimeError: candidate user is absent from prepared training users\r\n"} ## Step 116 · agent · 2026-08-31T19:53:23.199Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:31.440Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n result = invalid_packed.copy()\\n prepared_rows = pd.Index(prepared.user_ids).get_indexer(eligibility.uids)\\n- if np.any(prepared_rows < 0):\\n- raise RuntimeError(\\\"candidate user is absent from prepared training users\\\")\\n+ matched = prepared_rows >= 0\\n@@\\n- result[prepared_rows] |= np.bitwise_not(eligibility.packed)\\n+ result[prepared_rows[matched]] |= np.bitwise_not(eligibility.packed[matched])\\n log(\\n- f\\\"serving-candidate negative filter: target_users={len(prepared_rows):,} \\\"\\n+ f\\\"serving-candidate negative filter: target_users={int(matched.sum()):,} \\\"\\n+ f\\\"without_sequence={int((~matched).sum()):,} \\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T19:53:31.486Z Script completed Wall time 0.0 seconds Output: {} ## Step 117 · agent · 2026-08-31T19:53:33.177Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:37.941Z { "code": "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 --output /app/solution/validation_serving_negatives.parquet --prepared-cache /app/solution/validation_prepared.npz --negative-filter-scope serving-candidates --checkpoint-dir /app/solution/ckpt_serving\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:53:39.117Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"f38736","wall_time_seconds":1.000849893,"session_id":11972,"original_token_count":0,"output":""} ## Step 118 · agent · 2026-08-31T19:53:40.611Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:53:41.921Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:53:52.946Z Script running with cell ID 76 Wall time 11.0 seconds Output: ## Step 119 · agent · 2026-08-31T19:53:54.628Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:53:55.406Z { "cell_id": "76", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:54:11.948Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"afdf25","wall_time_seconds":30.000906577,"session_id":11972,"original_token_count":127,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\r\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\nserving-candidate negative filter: target_users=8,449 without_sequence=7 bytes=34,256,250\r\n"} ## Step 120 · agent · 2026-08-31T19:54:13.849Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:54:15.241Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:54:26.265Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 121 · agent · 2026-08-31T19:54:28.938Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:54:29.753Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:54:45.274Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"87395f","wall_time_seconds":30.000889274,"session_id":11972,"original_token_count":0,"output":""} ## Step 122 · agent · 2026-08-31T19:54:47.589Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:54:48.894Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:54:59.914Z Script running with cell ID 78 Wall time 11.0 seconds Output: ## Step 123 · agent · 2026-08-31T19:55:01.520Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:55:02.276Z { "cell_id": "78", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:55:18.917Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"7b3b93","wall_time_seconds":30.00128726,"session_id":11972,"original_token_count":0,"output":""} ## Step 124 · agent · 2026-08-31T19:55:20.948Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:21.991Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:55:33.042Z Script running with cell ID 79 Wall time 11.0 seconds Output: ## Step 125 · agent · 2026-08-31T19:55:34.749Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:55:35.588Z { "cell_id": "79", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:55:52.054Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"58d400","wall_time_seconds":30.000898627,"session_id":11972,"original_token_count":0,"output":""} ## Step 126 · agent · 2026-08-31T19:55:53.785Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:55:55.124Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:56:06.173Z Script running with cell ID 80 Wall time 11.0 seconds Output: ## Step 127 · agent · 2026-08-31T19:56:07.884Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:56:08.666Z { "cell_id": "80", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:56:25.183Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"66b399","wall_time_seconds":30.001858289,"session_id":11972,"original_token_count":0,"output":""} ## Step 128 · agent · 2026-08-31T19:56:26.864Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:56:28.108Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:56:39.141Z Script running with cell ID 81 Wall time 11.0 seconds Output: ## Step 129 · agent · 2026-08-31T19:56:40.831Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:56:41.775Z { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:56:58.143Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"200597","wall_time_seconds":30.000234184,"session_id":11972,"original_token_count":0,"output":""} ## Step 130 · agent · 2026-08-31T19:57:00.108Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:57:02.439Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:57:13.459Z Script running with cell ID 82 Wall time 11.0 seconds Output: ## Step 131 · agent · 2026-08-31T19:57:15.048Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:57:15.845Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:57:32.466Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"70a8a5","wall_time_seconds":30.000845707,"session_id":11972,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.544488 running_bce=0.538094 elapsed=198.6s\r\n"} ## Step 132 · agent · 2026-08-31T19:57:34.796Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:57:36.095Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:57:47.138Z Script running with cell ID 83 Wall time 11.0 seconds Output: ## Step 133 · agent · 2026-08-31T19:57:49.180Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:57:49.960Z { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:58:06.146Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"8dd125","wall_time_seconds":30.001382977,"session_id":11972,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/ckpt_serving/epoch-01.pt\r\nepoch=1/1 train_loss=0.53628 validation_loss=0.49167 pair_accuracy=0.758 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.0 validation_seconds=15.6\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.491673\r\n"} ## Step 134 · agent · 2026-08-31T19:58:08.014Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:58:09.355Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:58:20.381Z Script running with cell ID 84 Wall time 11.0 seconds Output: ## Step 135 · agent · 2026-08-31T19:58:22.901Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:58:23.705Z { "cell_id": "84", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:58:39.386Z Script completed Wall time 15.7 seconds Output: {"chunk_id":"cacaaf","wall_time_seconds":30.00129974,"session_id":11972,"original_token_count":0,"output":""} ## Step 136 · agent · 2026-08-31T19:58:42.270Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:58:43.978Z { "code": "const r = await tools.write_stdin({session_id:11972,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:58:54.718Z Script completed Wall time 10.7 seconds Output: {"chunk_id":"fd1f5a","wall_time_seconds":10.693925953,"exit_code":0,"original_token_count":828,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/validation_serving_negatives.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 312.248,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"serving-candidates\",\r\n \"target_weighting\": \"organic-strong\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.5,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"serving-candidates\",\r\n \"target_weighting\": \"organic-strong\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5362803230956917,\r\n \"positive_logit\": 0.7728189552776412,\r\n \"negative_logit\": -0.6416620049714731,\r\n \"pair_accuracy\": 0.758185936361832,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 632551.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 211.95569359599995,\r\n \"validation_loss\": 0.4916729135144899,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 78769.25,\r\n \"validation_seconds\": 15.636210905000098\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.6311006546020508,\r\n \"query_norm_std\": 0.7599809169769287,\r\n \"item_norm_mean\": 1.7254749536514282,\r\n \"item_norm_std\": 0.8159294128417969\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 137 · agent · 2026-08-31T19:58:56.401Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:58:59.231Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_serving_negatives.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:59:02.610Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"5c8b3c","wall_time_seconds":3.201157986,"exit_code":0,"original_token_count":37,"output":"{\n \"model\": \"/app/solution/validation_serving_negatives.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020061,\n \"quality_users\": 4263\n }\n}\n"} ## Step 138 · agent · 2026-08-31T19:59:08.571Z ### Thinking unavailable — no text recorded The candidate-boundary filter is sound but does not improve ranking (`0.020061`), so I’m retaining it as an optional, documented experiment rather than making it the shipped default. The production sampler’s original 24/8 mix with 0.5 correction remains the best negative configuration measured. There is one objective change already supported by the implementation but not enabled in production: assign grade-3 weight to novel training encounters followed by an organic like within seven days, using only the fitting prefix. I’m testing that with the best negative sampler now. It directly aligns the positive side of the loss with the graded discovery metric while preserving all chronology and serving constraints. ## Step 139 · agent · 2026-08-31T19:59:14.210Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:59:18.236Z { "code": "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/validation_discovery_graded.parquet --prepared-cache /app/solution/validation_prepared.npz --target-weighting discovery-graded --checkpoint-dir /app/solution/ckpt_discovery\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":4000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T19:59:19.413Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"89351c","wall_time_seconds":1.001824447,"session_id":62962,"original_token_count":0,"output":""} ## Step 140 · agent · 2026-08-31T19:59:23.434Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:59:24.766Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T19:59:35.793Z Script running with cell ID 88 Wall time 11.0 seconds Output: ## Step 141 · agent · 2026-08-31T19:59:37.498Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:59:38.286Z { "cell_id": "88", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T19:59:54.806Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"ae0501","wall_time_seconds":30.001760037,"session_id":62962,"original_token_count":133,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\r\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\r\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\r\nloaded organic-like outcomes: rows=301,527 users=7,526\r\ngrade-3 first-encounter targets=46,417 horizon_days=7\r\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\r\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\r\n"} ## Step 142 · agent · 2026-08-31T19:59:56.553Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:59:58.152Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:00:09.179Z Script running with cell ID 89 Wall time 11.0 seconds Output: ## Step 143 · agent · 2026-08-31T20:00:11.845Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:00:12.716Z { "cell_id": "89", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:00:28.175Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"101803","wall_time_seconds":30.001606648,"session_id":62962,"original_token_count":0,"output":""} ## Step 144 · agent · 2026-08-31T20:00:29.887Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:00:31.633Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:00:42.679Z Script running with cell ID 90 Wall time 11.0 seconds Output: ## Step 145 · agent · 2026-08-31T20:00:45.362Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:00:46.147Z { "cell_id": "90", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:01:01.684Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"90ee50","wall_time_seconds":30.000989803,"session_id":62962,"original_token_count":0,"output":""} ## Step 146 · agent · 2026-08-31T20:01:03.577Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:01:04.974Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:01:15.994Z Script running with cell ID 91 Wall time 11.0 seconds Output: ## Step 147 · agent · 2026-08-31T20:01:17.743Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:01:18.520Z { "cell_id": "91", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:01:35.011Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"63956a","wall_time_seconds":30.000677363,"session_id":62962,"original_token_count":0,"output":""} ## Step 148 · agent · 2026-08-31T20:01:36.549Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:01:37.892Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:01:48.923Z Script running with cell ID 92 Wall time 11.0 seconds Output: ## Step 149 · agent · 2026-08-31T20:01:50.648Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:01:52.063Z { "cell_id": "92", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:02:07.931Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"4352bc","wall_time_seconds":30.000904276,"session_id":62962,"original_token_count":0,"output":""} ## Step 150 · agent · 2026-08-31T20:02:10.885Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:02:12.630Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:02:23.659Z Script running with cell ID 93 Wall time 11.0 seconds Output: ## Step 151 · agent · 2026-08-31T20:02:27.004Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:02:28.062Z { "cell_id": "93", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:02:42.680Z Script completed Wall time 14.6 seconds Output: {"chunk_id":"12b26a","wall_time_seconds":30.001678066,"session_id":62962,"original_token_count":0,"output":""} ## Step 152 · agent · 2026-08-31T20:02:45.098Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:02:46.439Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:02:57.464Z Script running with cell ID 94 Wall time 11.0 seconds Output: ## Step 153 · agent · 2026-08-31T20:02:59.262Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:03:01.360Z { "cell_id": "94", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:03:16.470Z Script completed Wall time 15.1 seconds Output: {"chunk_id":"1a2cc4","wall_time_seconds":30.001641349,"session_id":62962,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=197.3s\r\n"} ## Step 154 · agent · 2026-08-31T20:03:19.854Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:03:21.320Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:03:32.358Z Script running with cell ID 95 Wall time 11.0 seconds Output: ## Step 155 · agent · 2026-08-31T20:03:34.988Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:03:36.166Z { "cell_id": "95", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:03:51.360Z Script completed Wall time 15.2 seconds Output: {"chunk_id":"600600","wall_time_seconds":30.000916177,"session_id":62962,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/ckpt_discovery/epoch-01.pt\r\nepoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.3 validation_seconds=15.9\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.504831\r\n"} ## Step 156 · agent · 2026-08-31T20:03:53.238Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:03:55.239Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:04:06.272Z Script running with cell ID 96 Wall time 11.0 seconds Output: ## Step 157 · agent · 2026-08-31T20:04:08.281Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:04:09.248Z { "cell_id": "96", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:04:25.280Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"50be0f","wall_time_seconds":30.001006708,"session_id":62962,"original_token_count":0,"output":""} ## Step 158 · agent · 2026-08-31T20:04:28.847Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:04:31.752Z { "code": "const r = await tools.write_stdin({session_id:62962,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:04:39.305Z Script completed Wall time 7.5 seconds Output: {"chunk_id":"ce3818","wall_time_seconds":7.506730835,"exit_code":0,"original_token_count":830,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"validation\",\r\n \"output\": \"/app/solution/validation_discovery_graded.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 316.697,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"discovery-graded\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.5,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"discovery-graded\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20056329,\r\n \"all_next_item_targets\": 20047194,\r\n \"selected_training_targets\": 1052953,\r\n \"selected_validation_targets\": 130103,\r\n \"discarded_old_targets\": 18864138,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5421154205142457,\r\n \"positive_logit\": 0.7774311398915007,\r\n \"negative_logit\": -0.6407561970787692,\r\n \"pair_accuracy\": 0.7602795720701684,\r\n \"targets\": 1052953.0,\r\n \"target_weight_sum\": 348780.0,\r\n \"batches\": 266.0,\r\n \"seconds\": 210.31310302200018,\r\n \"validation_loss\": 0.5048305721403443,\r\n \"validation_targets\": 130103.0,\r\n \"validation_target_weight_sum\": 42129.5,\r\n \"validation_seconds\": 15.93714405899982\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.7521849870681763,\r\n \"query_norm_std\": 0.8638167381286621,\r\n \"item_norm_mean\": 1.8543914556503296,\r\n \"item_norm_std\": 0.8459439277648926\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 159 · agent · 2026-08-31T20:04:42.214Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:04:45.718Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_discovery_graded.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:04:49.092Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"027bc7","wall_time_seconds":3.195912411,"exit_code":0,"original_token_count":37,"output":"{\n \"model\": \"/app/solution/validation_discovery_graded.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023522,\n \"quality_users\": 4263\n }\n}\n"} ## Step 160 · agent · 2026-08-31T20:05:07.639Z ### Thinking unavailable — no text recorded The aligned objective clears the baseline: public NDCG@10 rises from `0.021280` to `0.023522` (+10.5%). The winning configuration keeps the production negative sampler—validated by the negative ablations—and changes target weighting so novel tracks followed by an organic like within the fitting prefix’s seven-day horizon receive grade-3 emphasis. I’m freezing that measured winner and refitting it on `train_* + val_*` now. This uses validation events only as observed history, switches to the test eligibility/preference snapshots, and writes the first valid `/app/solution/submission.parquet` before any optional tuning. ### exec · 2026-08-31T20:05:16.747Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- target_weighting: str = \\\"organic-strong\\\"\\n+ # Public selection: this preserves every adjacent target but emphasizes a\\n+ # novel encounter followed by an organic like inside the fitting prefix.\\n+ target_weighting: str = \\\"discovery-graded\\\"\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T20:05:16.870Z Script completed Wall time 0.0 seconds Output: {} ## Step 161 · agent · 2026-08-31T20:05:21.360Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:05:28.671Z { "code": "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 && /opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --output /app/solution/submission.parquet --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/ckpt_submission\",\"workdir\":\"/app\",\"yield_time_ms\":1000,\"max_output_tokens\":5000,\"tty\":true});\ntext(r);\n" } ### Tool output · 2026-08-31T20:05:29.850Z Script completed Wall time 1.2 seconds Output: {"chunk_id":"b4d6db","wall_time_seconds":1.001087569,"session_id":41230,"original_token_count":0,"output":""} ## Step 162 · agent · 2026-08-31T20:05:33.145Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:05:35.209Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:05:46.239Z Script running with cell ID 101 Wall time 11.0 seconds Output: ## Step 163 · agent · 2026-08-31T20:05:52.223Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:05:53.609Z { "cell_id": "101", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:06:05.243Z Script completed Wall time 11.6 seconds Output: {"chunk_id":"b8bf89","wall_time_seconds":30.000893064,"session_id":41230,"original_token_count":97,"output":"loaded explicit events: rows=746,504 users=8,307\r\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\r\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.3s\r\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.7s\r\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s\r\nprepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.4s\r\n"} ## Step 164 · agent · 2026-08-31T20:06:08.344Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:06:09.982Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:06:21.002Z Script running with cell ID 102 Wall time 11.0 seconds Output: ## Step 165 · agent · 2026-08-31T20:06:23.203Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:06:24.779Z { "cell_id": "102", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:06:40.015Z Script completed Wall time 15.2 seconds Output: {"chunk_id":"0cc73e","wall_time_seconds":30.001557834,"session_id":41230,"original_token_count":246,"output":"prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s\r\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s\r\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.6s\r\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.6s\r\ntarget users without qualifying catalog sequence=6\r\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\r\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\r\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\r\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\r\nloaded organic-like outcomes: rows=314,586 users=7,613\r\ngrade-3 first-encounter targets=47,773 horizon_days=7\r\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\r\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\r\n"} ## Step 166 · agent · 2026-08-31T20:06:42.424Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:06:44.327Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:06:55.383Z Script running with cell ID 103 Wall time 11.0 seconds Output: ## Step 167 · agent · 2026-08-31T20:06:59.284Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:07:00.336Z { "cell_id": "103", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:07:14.392Z Script completed Wall time 14.1 seconds Output: {"chunk_id":"5d9639","wall_time_seconds":30.000997521,"session_id":41230,"original_token_count":0,"output":""} ## Step 168 · agent · 2026-08-31T20:07:16.760Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:07:18.268Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:07:29.322Z Script running with cell ID 104 Wall time 11.0 seconds Output: ## Step 169 · agent · 2026-08-31T20:07:31.928Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:07:32.941Z { "cell_id": "104", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:07:48.332Z Script completed Wall time 15.4 seconds Output: {"chunk_id":"da91b0","wall_time_seconds":30.00098197,"session_id":41230,"original_token_count":0,"output":""} ## Step 170 · agent · 2026-08-31T20:07:50.170Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:07:51.648Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:08:02.679Z Script running with cell ID 105 Wall time 11.0 seconds Output: ## Step 171 · agent · 2026-08-31T20:08:04.799Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:08:05.889Z { "cell_id": "105", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:08:21.683Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"0bb03a","wall_time_seconds":30.001007871,"session_id":41230,"original_token_count":0,"output":""} ## Step 172 · agent · 2026-08-31T20:08:24.370Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:08:26.208Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:08:37.253Z Script running with cell ID 106 Wall time 11.0 seconds Output: ## Step 173 · agent · 2026-08-31T20:08:42.231Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:08:43.206Z { "cell_id": "106", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:08:56.252Z Script completed Wall time 13.0 seconds Output: {"chunk_id":"7ef23a","wall_time_seconds":30.000952888,"session_id":41230,"original_token_count":0,"output":""} ## Step 174 · agent · 2026-08-31T20:08:58.200Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:08:59.537Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:09:10.596Z Script running with cell ID 107 Wall time 11.0 seconds Output: ## Step 175 · agent · 2026-08-31T20:09:12.308Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:09:13.134Z { "cell_id": "107", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:09:29.600Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"7ed105","wall_time_seconds":30.000892169,"session_id":41230,"original_token_count":0,"output":""} ## Step 176 · agent · 2026-08-31T20:09:31.132Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:09:32.735Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:09:43.753Z Script running with cell ID 108 Wall time 11.0 seconds Output: ## Step 177 · agent · 2026-08-31T20:09:46.593Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:09:47.410Z { "cell_id": "108", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:10:02.767Z Script completed Wall time 15.4 seconds Output: {"chunk_id":"45efb0","wall_time_seconds":30.000673476,"session_id":41230,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.455749 running_bce=0.544431 elapsed=200.6s\r\n"} ## Step 178 · agent · 2026-08-31T20:10:04.422Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:10:05.776Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:10:16.824Z Script running with cell ID 109 Wall time 11.0 seconds Output: ## Step 179 · agent · 2026-08-31T20:10:18.593Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:10:19.400Z { "cell_id": "109", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:10:35.834Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"5c0440","wall_time_seconds":30.00104806,"session_id":41230,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/ckpt_submission/epoch-01.pt\r\nepoch=1/1 train_loss=0.54085 validation_loss=0.51262 pair_accuracy=0.762 train_targets=1,067,606 validation_targets=131,997 train_seconds=215.6 validation_seconds=16.4\r\nrestored best validation-BCE checkpoint: epoch=1 loss=0.512617\r\n"} ## Step 180 · agent · 2026-08-31T20:10:37.520Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:10:38.914Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:10:49.954Z Script running with cell ID 110 Wall time 11.0 seconds Output: ## Step 181 · agent · 2026-08-31T20:10:51.737Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:10:52.520Z { "cell_id": "110", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:11:08.960Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"1766b4","wall_time_seconds":30.001503407,"session_id":41230,"original_token_count":0,"output":""} ## Step 182 · agent · 2026-08-31T20:11:10.665Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:11:12.170Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:11:23.191Z Script running with cell ID 111 Wall time 11.0 seconds Output: ## Step 183 · agent · 2026-08-31T20:11:25.402Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T20:11:26.201Z { "cell_id": "111", "yield_time_ms": 30000, "max_tokens": 6000 } ### Tool output · 2026-08-31T20:11:42.202Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"850d30","wall_time_seconds":30.001532302,"session_id":41230,"original_token_count":0,"output":""} ## Step 184 · agent · 2026-08-31T20:11:44.072Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:11:45.410Z { "code": "const r = await tools.write_stdin({session_id:41230,chars:\"\",yield_time_ms:30000,max_output_tokens:6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:11:52.881Z Script completed Wall time 7.5 seconds Output: {"chunk_id":"ebb781","wall_time_seconds":7.429485069,"exit_code":0,"original_token_count":826,"output":"ranked users=1,024/8,456\r\nranked users=2,048/8,456\r\nranked users=3,072/8,456\r\nranked users=4,096/8,456\r\nranked users=5,120/8,456\r\nranked users=6,144/8,456\r\nranked users=7,168/8,456\r\nranked users=8,192/8,456\r\nranked users=8,456/8,456\r\n{\r\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\r\n \"stage\": \"submission\",\r\n \"output\": \"/app/solution/submission.parquet\",\r\n \"rows\": 845600,\r\n \"users\": 8456,\r\n \"runtime_seconds\": 379.678,\r\n \"public_inputs_only\": true,\r\n \"architecture\": {\r\n \"family\": \"pure-windowed-sequence-two-tower\",\r\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\r\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\r\n \"bptt_hidden_state\": \"carried-and-detached\",\r\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\r\n \"loss_execution\": \"gradient-accumulated-microbatches\",\r\n \"user_id_embedding\": false,\r\n \"user_mlp\": \"582-512-256-192\",\r\n \"item_mlp\": \"196-512-256-192\",\r\n \"normalization\": \"none\",\r\n \"score\": \"raw-dot-product\",\r\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\r\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\r\n \"false_negative_filter\": \"full-training-history\",\r\n \"target_weighting\": \"discovery-graded\"\r\n },\r\n \"config\": {\r\n \"bptt_steps\": 100,\r\n \"user_batch_size\": 64,\r\n \"embedding_dim\": 192,\r\n \"hidden_dim\": 192,\r\n \"user_hidden_1\": 512,\r\n \"user_hidden_2\": 256,\r\n \"item_hidden_1\": 512,\r\n \"item_hidden_2\": 256,\r\n \"dropout\": 0.05,\r\n \"epochs\": 1,\r\n \"learning_rate\": 0.001,\r\n \"weight_decay\": 1e-05,\r\n \"gradient_clip\": 2.0,\r\n \"loss_microbatch_size\": 65536,\r\n \"uniform_negatives\": 24,\r\n \"inbatch_negatives\": 8,\r\n \"inbatch_correction_power\": 0.5,\r\n \"inbatch_correction_min\": 0.25,\r\n \"inbatch_correction_max\": 4.0,\r\n \"negative_filter_scope\": \"full-training-history\",\r\n \"target_weighting\": \"discovery-graded\",\r\n \"ordinary_target_weight\": 0.25,\r\n \"organic_strong_target_weight\": 1.0,\r\n \"organic_like_target_weight\": 3.0,\r\n \"discovery_horizon_days\": 7,\r\n \"validation_fraction\": 0.1,\r\n \"max_train_targets_per_user\": 128,\r\n \"max_validation_targets_per_user\": 16,\r\n \"early_stopping_patience\": 1,\r\n \"early_stopping_min_delta\": 0.0001,\r\n \"seed\": 29\r\n },\r\n \"sequence_tokens\": 20712971,\r\n \"all_next_item_targets\": 20703818,\r\n \"selected_training_targets\": 1067606,\r\n \"selected_validation_targets\": 131997,\r\n \"discarded_old_targets\": 19504215,\r\n \"training_history\": [\r\n {\r\n \"epoch\": 1.0,\r\n \"loss\": 0.5408499578108406,\r\n \"positive_logit\": 0.7880318129205779,\r\n \"negative_logit\": -0.6562818357083422,\r\n \"pair_accuracy\": 0.7620188592514467,\r\n \"targets\": 1067606.0,\r\n \"target_weight_sum\": 353472.75,\r\n \"batches\": 270.0,\r\n \"seconds\": 215.5706840769999,\r\n \"validation_loss\": 0.5126167207212325,\r\n \"validation_targets\": 131997.0,\r\n \"validation_target_weight_sum\": 43182.0,\r\n \"validation_seconds\": 16.389232716999686\r\n }\r\n ],\r\n \"embedding_diagnostics\": {\r\n \"query_norm_mean\": 1.4391510486602783,\r\n \"query_norm_std\": 0.6248915791511536,\r\n \"item_norm_mean\": 1.8983012437820435,\r\n \"item_norm_std\": 0.8495252728462219\r\n },\r\n \"torch_version\": \"2.8.0+cpu\",\r\n \"device\": \"cpu\"\r\n}\r\n"} ## Step 185 · agent · 2026-08-31T20:12:13.057Z ### Thinking unavailable — no text recorded ## Step 186 · agent · 2026-08-31T20:12:15.863Z ### Thinking unavailable — no text recorded The refit is complete and `/app/solution/submission.parquet` now exists. It was trained on 20.7M `train+val` sequence tokens, selected 1.07M recent training targets with an internal chronological suffix, used 47,773 fitting-prefix grade-3 examples, and ranked only the test candidate snapshot. I’m doing the final structural verifier now—exact user set, 100 distinct eligible items per user, complete ranks 1–100, integer/finite fields—then I’ll write the concise experiment record and leave the measured winner as the default command. ## Step 187 · agent · 2026-08-31T20:12:21.082Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:13:04.374Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/validate_submission.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Strict local structural validation for the hidden-test ranking.\\\"\\\"\\\"\\n+\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+ROOT = Path(\\\"/app/solution\\\")\\n+DATA = Path(\\\"/task/data\\\")\\n+ranking = pd.read_parquet(ROOT / \\\"submission.parquet\\\")\\n+required = [\\\"uid\\\", \\\"item_id\\\", \\\"rank\\\"]\\n+assert list(ranking.columns) == required\\n+assert len(ranking) == 845_600\\n+for column in required:\\n+ values = ranking[column].to_numpy()\\n+ assert np.issubdtype(values.dtype, np.integer)\\n+ assert np.isfinite(values).all()\\n+\\n+targets = pd.read_parquet(DATA / \\\"target_users.parquet\\\", columns=[\\\"uid\\\"])\\n+assert set(ranking.uid.unique()) == set(targets.uid.to_numpy())\\n+counts = ranking.groupby(\\\"uid\\\", sort=False).agg(\\n+ rows=(\\\"rank\\\", \\\"size\\\"),\\n+ items=(\\\"item_id\\\", \\\"nunique\\\"),\\n+ ranks=(\\\"rank\\\", \\\"nunique\\\"),\\n+ rank_min=(\\\"rank\\\", \\\"min\\\"),\\n+ rank_max=(\\\"rank\\\", \\\"max\\\"),\\n+)\\n+assert (counts.rows == 100).all()\\n+assert (counts[\\\"items\\\"] == 100).all()\\n+assert (counts.ranks == 100).all()\\n+assert (counts.rank_min == 1).all() and (counts.rank_max == 100).all()\\n+\\n+with np.load(DATA / \\\"test_eligible_candidates.npz\\\", allow_pickle=False) as artifact:\\n+ uids = artifact[\\\"uids\\\"]\\n+ items = artifact[\\\"item_ids\\\"]\\n+ packed = artifact[\\\"packed_eligible\\\"]\\n+item_columns = pd.Series(np.arange(len(items)), index=items)\\n+uid_rows = pd.Series(np.arange(len(uids)), index=uids)\\n+ordered = ranking.sort_values([\\\"uid\\\", \\\"rank\\\"], kind=\\\"stable\\\")\\n+rows = uid_rows.loc[ordered.uid].to_numpy(dtype=np.int64)\\n+columns = item_columns.loc[ordered.item_id].to_numpy(dtype=np.int64)\\n+allowed = (packed[rows, columns >> 3] >> (columns & 7)) & 1\\n+assert np.all(allowed == 1)\\n+print(\\\"submission valid: 8,456 users, 845,600 rows, all items eligible\\\")\\n*** Add File: /app/solution/README.md\\n+# Improved music-discovery two-tower\\n+\\n+## Result\\n+\\n+The shipped model improves public graded NDCG@10 from **0.021280** to\\n+**0.023522** (+10.5%). The final `submission.parquet` is a one-epoch refit on\\n+`train_* + val_*`; validation events are observed history only in this refit.\\n+\\n+## Diagnosis and experiments\\n+\\n+The production objective uses 24 catalog-uniform negatives and eight\\n+unique-target in-batch negatives. Its full-history rejection filter prevents\\n+known positive false negatives, while a square-root inverse-propensity weight\\n+keeps frequent in-batch targets from dominating. Public ablations showed that\\n+this balance is important:\\n+\\n+| Validation configuration | NDCG@10 |\\n+| --- | ---: |\\n+| Production: 24 uniform + 8 corrected in-batch | 0.021280 |\\n+| 24 uniform + 8 uncorrected in-batch | 0.018522 |\\n+| 8 uniform + 24 uncorrected in-batch | 0.012552 |\\n+| Stronger in-batch correction (power 1.0) | 0.020848 |\\n+| Serving-candidate-aware rejection filter | 0.020061 |\\n+| **Production negatives + discovery-graded targets** | **0.023522** |\\n+\\n+The negative experiments falsified the initial idea that many more popular\\n+in-batch negatives would help: they suppress popular candidates too strongly.\\n+The retained sampler is the measured best. The winning objective change keeps\\n+every adjacent next-item target, but raises a novel encounter to weight 3 when\\n+an organic like follows within seven days inside the fitting prefix. Strong\\n+organic 80%-complete listens retain weight 1 and ordinary/repeated targets\\n+retain weight 0.25. This aligns training with the grade-3 discovery gain without\\n+using validation outcomes during validation ranking.\\n+\\n+The model remains the same indexed two-tower: item vectors are computed once,\\n+the user tower has no user-ID table, and serving uses one batchable dot product.\\n+\\n+## Reproduce the final artifact\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/train_current_model.py \\\\\\n+ --stage submission \\\\\\n+ --public-dir /task \\\\\\n+ --output /app/solution/submission.parquet\\n+\\n+/opt/task-venv/bin/python /app/solution/validate_submission.py\\n+```\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-08-31T20:13:04.521Z Script completed Wall time 0.0 seconds Output: {} ## Step 188 · agent · 2026-08-31T20:13:08.381Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:13:15.710Z { "code": "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 /app/solution/validate_submission.py && /opt/task-venv/bin/python /app/solution/validate_submission.py && test -f /app/solution/submission.parquet && test ! -L /app/solution/submission.parquet && ls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md && sha256sum /app/solution/submission.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":6000});\ntext(r);\n" } ### Tool output · 2026-08-31T20:13:17.711Z Script completed Wall time 2.0 seconds Output: {"chunk_id":"aa4e98","wall_time_seconds":1.8227373839999998,"exit_code":0,"original_token_count":116,"output":"submission valid: 8,456 users, 845,600 rows, all items eligible\n-rw-r--r-- 1 agent agent 2.1K Aug 31 20:13 /app/solution/README.md\n-rw-r--r-- 1 agent agent 1.2M Aug 31 20:11 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 81K Aug 31 20:05 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Aug 31 19:24 /app/solution/two_tower_model.py\n3dba5c1ca2ff07ff48094553d88c2dc3ef79e53edbc22a798b9fc04848f69e0d /app/solution/submission.parquet\n"} ## Step 189 · agent · 2026-08-31T20:13:22.636Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T20:13:28.555Z { "code": "const p = await tools.update_plan({explanation:\"Measured the baseline and four negative-sampling ablations, selected the discovery-graded objective with the best production negative sampler, refit on train+validation, and strictly validated the final hidden-test ranking.\",plan:[\n {step:\"Copy and inspect the production implementation and data shapes\",status:\"completed\"},\n {step:\"Reproduce the public baseline and diagnose negative sampling\",status:\"completed\"},\n {step:\"Implement and measure focused objective improvements\",status:\"completed\"},\n {step:\"Refit the best approach on train+validation and validate submission\",status:\"completed\"},\n {step:\"Write the experiment README and final handoff\",status:\"completed\"}\n]});\ntext(p);\n" } ### Tool output · 2026-08-31T20:13:28.620Z Script completed Wall time 0.0 seconds Output: {} ## Step 190 · agent · 2026-08-31T20:13:31.907Z ### Thinking unavailable — no text recorded Completed and validated the improved two-tower artifact. - Submission: [submission.parquet](/app/solution/submission.parquet) - Implementation: [train_current_model.py](/app/solution/train_current_model.py) - Model primitives: [two_tower_model.py](/app/solution/two_tower_model.py) - Experiment record: [README.md](/app/solution/README.md) Public NDCG@10 improved from **0.021280** to **0.023522** (+10.5%). The final model was refit on `train_* + val_*` using discovery-graded target weighting and the best validated production negative sampler. Structural validation passed: 8,456 exact users, 100 distinct eligible items and ranks 1–100 per user, totaling 845,600 rows.