yambda-discovery-ranking-two-tower-case-1 / run-001-gpt-5-6-sol-high-2026-08-31T11-53-19 Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-08-31T19:01:42.599Z 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 · 2026-08-31T19:01:44.504Z ## 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 · 2026-08-31T19:01:44.504Z 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 · 2026-08-31T19:01:44.504Z 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 · 2026-08-31T19:01:44.505Z 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": "01a05932-e438-7e03-b8aa-3bfe1c35d983", "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 · 2026-08-31T19:01:44.513Z # 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 · 2026-08-31T19:01:48.022Z ### Thinking unavailable — no text recorded I’ll reproduce the production score first, then inspect the objective and data statistics to target negative sampling. I’ll keep a valid best artifact under `/app/solution/` while iterating, and only refit on `train + val` after selecting the improvement on the public window. ### exec · 2026-08-31T19:01:57.956Z { "code": "const p = await tools.update_plan({plan:[\n {step:\"Reproduce baseline and inspect training/evaluation pipeline\",status:\"in_progress\"},\n {step:\"Diagnose negative sampling and implement focused variants\",status:\"pending\"},\n {step:\"Run public backtests and select the best valid model\",status:\"pending\"},\n {step:\"Refit on train + validation and generate hidden-test submission\",status:\"pending\"},\n {step:\"Validate artifact schema/eligibility and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"pwd && rg --files /task/current_model /task | sed -n '1,160p' && du -sh /task/data && sed -n '1,260p' /task/current_model/train_current_model.py && sed -n '1,300p' /task/current_model/two_tower_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);" } ### Tool output · 2026-08-31T19:01:58.031Z Script completed Wall time 0.1 seconds Output: /app /task/ranking_evaluation.py /task/requirements.txt /task/current_model/two_tower_model.py /task/current_model/train_current_model.py /task/evaluate_public.py /task/current_model/two_tower_model.py /task/current_model/train_current_model.py /task/data/validation_preference_state.parquet /task/data/test_eligible_candidates.npz /task/data/val_dislikes.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/validation_eligible_candidates.npz /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_undislikes.parquet /task/data/val_undislikes.parquet /task/data/val_listens.parquet /task/data/val_likes.parquet /task/data/train_unlikes.parquet /task/data/candidate_catalog.parquet /task/data/test_preference_state.parquet /task/data/target_users.parquet 340M /task/data #!/usr/bin/env python3 """Train the Yambda windowed two-tower baseline. Every catalog listen with at least 50 percent completion is a sequence token. Within each selected chronological window, every adjacent target contributes a loss. The fast default retains each user's most recent 128 training targets and 16 held-out validation targets; passing zero for either cap restores the uncapped behavior for that side of the split. A 100-step truncated-BPTT block computes all of its timestep losses and carries the detached GRU hidden state into the next block. The model is a pure two-tower retriever: no user-ID embedding, no output normalization, raw dot-product retrieval, sample-weighted binary cross entropy, and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every selected adjacent target retains a nonzero weight. Only files under the supplied public directory are read. """ from __future__ import annotations import argparse import json import random import sys import time from dataclasses import asdict, dataclass from pathlib import Path import numpy as np import pandas as pd import torch from torch import nn from torch.nn import functional as F import two_tower_model as base CACHE_VERSION = 2 @dataclass(frozen=True) class Config: bptt_steps: int = 100 user_batch_size: int = 64 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 loss_microbatch_size: int = 65_536 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 negative_filter_scope: str = "full-training-history" target_weighting: str = "organic-strong" ordinary_target_weight: float = 0.25 organic_strong_target_weight: float = 1.0 organic_like_target_weight: float = 3.0 discovery_horizon_days: int = 7 validation_fraction: float = 0.10 max_train_targets_per_user: int = 128 max_validation_targets_per_user: int = 16 early_stopping_patience: int = 1 early_stopping_min_delta: float = 1.0e-4 seed: int = 29 @dataclass(frozen=True) class CandidateSet: uids: np.ndarray item_ids: np.ndarray packed: np.ndarray @classmethod def read(cls, path: Path) -> "CandidateSet": with np.load(path, allow_pickle=False) as artifact: result = cls( uids=artifact["uids"].astype(np.int64, copy=True), item_ids=artifact["item_ids"].astype(np.int64, copy=True), packed=artifact["packed_eligible"].astype(np.uint8, copy=True), ) expected = (len(result.uids), (len(result.item_ids) + 7) // 8) if result.packed.shape != expected: raise ValueError( f"invalid candidate artifact shape {result.packed.shape}; " f"expected {expected}" ) return result @dataclass(frozen=True) class SequenceData: item_ids: np.ndarray item_features: np.ndarray user_ids: np.ndarray offsets: np.ndarray sequence_timestamps: np.ndarray sequence_items: np.ndarray event_features: np.ndarray activity_features: np.ndarray state_ids: np.ndarray liked_states: np.ndarray disliked_states: np.ndarray state_counts: np.ndarray inference_user_rows: np.ndarray inference_liked_items: np.ndarray inference_disliked_items: np.ndarray inference_user_features: np.ndarray @property def lengths(self) -> np.ndarray: return np.diff(self.offsets) @property def target_count(self) -> int: return int(np.maximum(self.lengths - 1, 0).sum()) @dataclass(frozen=True) class TargetPartition: """A contiguous recent target window and its chronological BCE split.""" window_starts: np.ndarray training_counts: np.ndarray validation_counts: np.ndarray history_training_counts: np.ndarray @property def selected_count(self) -> int: return int(self.training_counts.sum() + self.validation_counts.sum()) def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def seed_everything(seed: int) -> None: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def model_config(config: Config) -> base.TowerConfig: """Map the dense trainer configuration onto the unchanged tower.""" return base.TowerConfig( sequence_length=config.bptt_steps, embedding_dim=config.embedding_dim, hidden_dim=config.hidden_dim, user_hidden_1=config.user_hidden_1, user_hidden_2=config.user_hidden_2, item_hidden_1=config.item_hidden_1, item_hidden_2=config.item_hidden_2, dropout=config.dropout, epochs=config.epochs, learning_rate=config.learning_rate, weight_decay=config.weight_decay, gradient_clip=config.gradient_clip, uniform_negatives=config.uniform_negatives, inbatch_negatives=config.inbatch_negatives, inbatch_correction_power=config.inbatch_correction_power, inbatch_correction_min=config.inbatch_correction_min, inbatch_correction_max=config.inbatch_correction_max, seed=config.seed, ) def append_state_snapshot( active_likes: dict[int, int], active_dislikes: dict[int, int], n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> int: liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items)) disliked_states.append( base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items) ) state_counts.append((len(active_likes), len(active_dislikes))) return len(liked_states) - 1 def causal_activity_features( timestamps: np.ndarray, items: np.ndarray, organic: np.ndarray, completion: np.ndarray, ) -> np.ndarray: """Compute the first four reviewed user features at every event cutoff.""" length = len(items) result = np.zeros((length, 4), dtype=np.float16) if not length: return result left_rows = np.searchsorted( timestamps, timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY, side="left", ) rows = np.arange(length, dtype=np.int64) counts = rows - left_rows + 1 organic_sum = np.cumsum(organic.astype(np.float64)) completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64)) before_organic = np.where( left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0 ) before_completion = np.where( left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0 ) unique_counts = np.empty(length, dtype=np.int32) frequencies: dict[int, int] = {} left = 0 for right, raw_item in enumerate(items): item = int(raw_item) frequencies[item] = frequencies.get(item, 0) + 1 required_left = int(left_rows[right]) while left < required_left: old_item = int(items[left]) remaining = frequencies[old_item] - 1 if remaining: frequencies[old_item] = remaining else: del frequencies[old_item] left += 1 unique_counts[right] = len(frequencies) result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16) result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16) result[:, 2] = ( (organic_sum - before_organic) / np.maximum(counts, 1) ).astype(np.float16) result[:, 3] = ( (completion_sum - before_completion) / np.maximum(counts, 1) / 100.0 ).astype(np.float16) return result def build_sequence_parts( listens: base.ListenEvents, explicit: base.ExplicitEvents, n_items: int, liked_states: list[np.ndarray], disliked_states: list[np.ndarray], state_counts: list[tuple[int, int]], ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None: qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False) items = listens.items[qualifying].astype(np.uint16, copy=False) organic = listens.organic[qualifying].astype(np.float32, copy=False) completion = listens.completion_pct[qualifying].astype(np.float32, copy=False) if not len(items): """Shared public-data primitives for the Yambda two-tower baseline.""" from __future__ import annotations import sys from dataclasses import dataclass from pathlib import Path from typing import Iterator import numpy as np import pandas as pd import pyarrow.parquet as pq import torch from torch import nn DAY = 86_400 TRAIN_END = 24_786_800 VALIDATION_END = 25_393_400 LISTEN_POSITIVE_THRESHOLD = 50 PROFILE_LOOKBACK_DAYS = 120 LIKE_ITEMS = 48 DISLIKE_ITEMS = 24 TOP_N = 100 @dataclass(frozen=True) class TowerConfig: sequence_length: int = 100 embedding_dim: int = 192 hidden_dim: int = 192 user_hidden_1: int = 512 user_hidden_2: int = 256 item_hidden_1: int = 512 item_hidden_2: int = 256 dropout: float = 0.05 epochs: int = 1 learning_rate: float = 1.0e-3 weight_decay: float = 1.0e-5 gradient_clip: float = 2.0 uniform_negatives: int = 24 inbatch_negatives: int = 8 inbatch_correction_power: float = 0.5 inbatch_correction_min: float = 0.25 inbatch_correction_max: float = 4.0 seed: int = 29 @dataclass(frozen=True) class ListenEvents: timestamps: np.ndarray items: np.ndarray organic: np.ndarray completion_pct: np.ndarray @dataclass(frozen=True) class ExplicitEvents: timestamps: np.ndarray items: np.ndarray channel: np.ndarray active: np.ndarray organic_like: np.ndarray def log(message: str) -> None: print(message, file=sys.stderr, flush=True) def build_item_features(catalog: pd.DataFrame) -> np.ndarray: """Return four standardized features built only from the public catalog.""" positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False) organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False) popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False) features = np.column_stack( [ np.log1p(positives), np.log1p(organic), organic / np.maximum(positives, 1.0), 1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1), ] ) means = features.mean(axis=0, keepdims=True) scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6) standardized = ((features - means) / scales).astype(np.float32) if not np.isfinite(standardized).all(): raise RuntimeError("catalog features contain non-finite values") return standardized def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents: if len(parts) == 1: return parts[0] timestamps = np.concatenate([part.timestamps for part in parts]) items = np.concatenate([part.items for part in parts]) organic = np.concatenate([part.organic for part in parts]) completion = np.concatenate([part.completion_pct for part in parts]) order = np.argsort(timestamps, kind="stable") return ListenEvents( timestamps=timestamps[order], items=items[order], organic=organic[order], completion_pct=completion[order], ) def iter_listen_users( path: Path, item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Stream one uid-sorted public listen file one user at a time.""" parquet = pq.ParquetFile(path) pending_uid: int | None = None pending: list[ListenEvents] = [] previous_uid = -1 for batch in parquet.iter_batches( batch_size=750_000, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ): frame = batch.to_pandas() mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 if not np.any(keep): continue uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep] timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep] items = mapped[keep].astype(np.int32, copy=False) organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep] completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep] if np.any(uids[1:] < uids[:-1]): raise RuntimeError(f"{path.name} is not sorted by uid") boundaries = np.r_[ 0, np.flatnonzero(uids[1:] != uids[:-1]) + 1, len(uids), ] for start, end in zip(boundaries[:-1], boundaries[1:]): uid = int(uids[start]) part = ListenEvents( timestamps[start:end].copy(), items[start:end].copy(), organic[start:end].copy(), completion[start:end].copy(), ) if pending_uid is None: pending_uid = uid elif uid != pending_uid: if pending_uid < previous_uid: raise RuntimeError(f"{path.name} user order regressed") yield pending_uid, _combine_listen_chunks(pending) previous_uid = pending_uid pending_uid = uid pending = [] pending.append(part) if pending_uid is not None: yield pending_uid, _combine_listen_chunks(pending) def merge_listen_streams( paths: list[Path], item_index: pd.Index ) -> Iterator[tuple[int, ListenEvents]]: """Merge uid-sorted public listen files without materializing them.""" streams = [iter(iter_listen_users(path, item_index)) for path in paths] heads: list[tuple[int, ListenEvents] | None] = [ next(stream, None) for stream in streams ] while any(head is not None for head in heads): uid = min(head[0] for head in heads if head is not None) parts: list[ListenEvents] = [] for position, head in enumerate(heads): if head is not None and head[0] == uid: parts.append(head[1]) heads[position] = next(streams[position], None) yield uid, _combine_listen_chunks(parts) def load_explicit_events( data: Path, prefixes: tuple[str, ...], item_index: pd.Index, ) -> dict[int, ExplicitEvents]: """Load public explicit actions as chronological state changes.""" specifications = ( ("likes", 0, 1, 0), ("unlikes", 0, 0, 1), ("dislikes", 1, 1, 0), ("undislikes", 1, 0, 1), ) parts: list[pd.DataFrame] = [] for family, channel, active, tie_priority in specifications: for prefix in prefixes: frame = pd.read_parquet( data / f"{prefix}_{family}.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) mapped = item_index.get_indexer( frame.item_id.to_numpy(dtype=np.int64, copy=False) ) keep = mapped >= 0 if not np.any(keep): continue retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy() retained["item"] = mapped[keep].astype(np.int32, copy=False) retained["channel"] = np.int8(channel) retained["active"] = np.int8(active) retained["tie_priority"] = np.int8(tie_priority) retained["organic_like"] = np.int8( family == "likes" ) * retained.is_organic.astype(np.int8) parts.append(retained.drop(columns="is_organic")) if not parts: return {} events = pd.concat(parts, ignore_index=True) events = events.sort_values( ["uid", "timestamp", "tie_priority"], kind="mergesort" ).reset_index(drop=True) result: dict[int, ExplicitEvents] = {} for uid, rows in events.groupby("uid", sort=False): result[int(uid)] = ExplicitEvents( timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True), items=rows.item.to_numpy(dtype=np.int32, copy=True), channel=rows.channel.to_numpy(dtype=np.int8, copy=True), active=rows.active.to_numpy(dtype=np.int8, copy=True), organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True), ) log(f"loaded explicit events: rows={len(events):,} users={len(result):,}") return result def empty_explicit_events() -> ExplicitEvents: return ExplicitEvents( timestamps=np.empty(0, dtype=np.int64), items=np.empty(0, dtype=np.int32), channel=np.empty(0, dtype=np.int8), active=np.empty(0, dtype=np.int8), organic_like=np.empty(0, dtype=np.int8), ) def pack_active_items( active: dict[int, int], width: int, padding_item: int ) -> np.ndarray: packed = np.full(width, padding_item, dtype=np.uint16) if not active: return packed ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width] packed[: len(ordered)] = np.fromiter( (item for item, _ in ordered), dtype=np.uint16, count=len(ordered) ) return packed def load_inference_state( path: Path, item_index: pd.Index, uids: np.ndarray, n_items: int, ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """Pack the task-provided active explicit state.""" state = pd.read_parquet(path, columns=["uid", "item_id", "state"]) mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64)) keep = mapped >= 0 state = state.loc[keep, ["uid", "state"]].copy() state["item"] = mapped[keep].astype(np.int32, copy=False) user_rows = pd.Index(uids) liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16) disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16) like_counts = np.zeros(len(uids), dtype=np.int32) dislike_counts = np.zeros(len(uids), dtype=np.int32) for value, width, destination, counts in ( ("liked", LIKE_ITEMS, liked, like_counts), ("disliked", DISLIKE_ITEMS, disliked, dislike_counts), ): selected = state[state.state == value] for uid, rows in selected.groupby("uid", sort=False): row = int(user_rows.get_indexer([int(uid)])[0]) if row < 0: continue items = np.sort(rows.item.unique()) counts[row] = len(items) retained = items[:width] destination[row, : len(retained)] = retained.astype(np.uint16) return liked, disliked, like_counts, dislike_counts class SequentialTwoTower(nn.Module): """Order-sensitive user tower and independent catalog-aware item tower.""" def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None: super().__init__() n_items, n_item_features = item_features.shape dim = config.embedding_dim self.n_items = n_items self.context_embedding = nn.Embedding( n_items + 1, dim, padding_idx=n_items ) self.event_projection = nn.Sequential( nn.Linear(dim + 3, config.hidden_dim), nn.GELU(), ) self.sequence_encoder = nn.GRU( input_size=config.hidden_dim, ## Step 8 · agent · 2026-08-31T19:02:01.021Z ### Thinking unavailable — no text recorded The baseline confirms the suspected shape: positives are adjacent ≥50%-completion listens, while negatives are a fixed mix of 24 uniform catalog samples and 8 corrected in-batch samples. I’m now measuring the baseline and tracing exactly how those negatives are filtered and weighted, since the discovery metric rewards a much narrower organic ≥80%/like target. ### exec · 2026-08-31T19:02:08.103Z { "code": "const 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 && python /task/current_model/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:02:08.321Z Script completed Wall time 0.2 seconds Output: {"chunk_id":"45541b","wall_time_seconds":0.039557918,"exit_code":1,"original_token_count":45,"output":"Traceback (most recent call last):\n File \"/task/current_model/train_current_model.py\", line 28, in \n import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"} ## Step 9 · agent · 2026-08-31T19:02:10.365Z ### Thinking unavailable — no text recorded The shell’s default `python` is a minimal interpreter without the supplied packages, so the reproduction failed before touching any data or artifacts. I’m locating the task’s packaged Python environment and will rerun with that interpreter. ### exec · 2026-08-31T19:02:17.129Z { "code": "const r = await tools.exec_command({cmd:\"which -a python python3 || true\\npython --version || true\\npython3 --version || true\\nls -la /opt /usr/local/bin | sed -n '1,180p'\\nfind / -maxdepth 4 -type f \\\\( -name python -o -name python3 \\\\) 2>/dev/null | sed -n '1,120p'\\nsed -n '1,220p' /task/requirements.txt\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:20000});\ntext(r.output);" } ### Tool output · 2026-08-31T19:02:17.291Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 Python 3.12.13 /opt: total 16 drwxr-xr-x 1 root root 4096 Aug 31 18:58 . drwxr-xr-x 1 root root 4096 Aug 31 19:01 .. drwxr-xr-x 4 root root 4096 Aug 31 18:58 codex-home dr-xr-xr-x 1 root root 4096 Aug 31 18:58 task-venv /usr/local/bin: total 48 drwxr-xr-x 1 root root 4096 Aug 31 18:58 . drwxr-xr-x 1 root root 4096 Aug 3 00:00 .. lrwxrwxrwx 1 root root 9 Aug 5 01:13 2to3 -> 2to3-3.12 -rwxr-xr-x 1 root root 102 Aug 5 01:13 2to3-3.12 lrwxrwxrwx 1 root root 53 Aug 31 18:58 codex -> /opt/codex-home/packages/standalone/current/bin/codex lrwxrwxrwx 1 root root 5 Aug 5 01:14 idle -> idle3 lrwxrwxrwx 1 root root 8 Aug 5 01:13 idle3 -> idle3.12 -rwxr-xr-x 1 root root 100 Aug 5 01:13 idle3.12 lrwxrwxrwx 1 root root 4 Aug 5 01:14 pip -> pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3 -rwxr-xr-x 1 root root 230 Aug 5 01:13 pip3.12 lrwxrwxrwx 1 root root 6 Aug 5 01:14 pydoc -> pydoc3 lrwxrwxrwx 1 root root 9 Aug 5 01:13 pydoc3 -> pydoc3.12 -rwxr-xr-x 1 root root 85 Aug 5 01:13 pydoc3.12 lrwxrwxrwx 1 root root 7 Aug 5 01:14 python -> python3 lrwxrwxrwx 1 root root 14 Aug 5 01:14 python-config -> python3-config lrwxrwxrwx 1 root root 10 Aug 5 01:13 python3 -> python3.12 lrwxrwxrwx 1 root root 17 Aug 5 01:13 python3-config -> python3.12-config -rwxr-xr-x 1 root root 14480 Aug 5 01:13 python3.12 -rwxr-xr-x 1 root root 3018 Aug 5 01:13 python3.12-config # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ## Step 10 · agent · 2026-08-31T19:02:19.565Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:22.876Z { "code": "const r = await tools.exec_command({cmd:\"find /opt/task-venv -maxdepth 3 -type f -o -type l | sed -n '1,160p'\\nls -la /opt/task-venv /opt/task-venv/bin /opt/task-venv/lib || true\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:10000});\ntext(r.output);" } ### Tool output · 2026-08-31T19:02:22.971Z Script completed Wall time 0.0 seconds Output: /opt/task-venv/pyvenv.cfg /opt/task-venv/.lock /opt/task-venv/bin/tqdm /opt/task-venv/bin/f2py /opt/task-venv/bin/torchrun /opt/task-venv/bin/Activate.ps1 /opt/task-venv/bin/activate.fish /opt/task-venv/bin/markdown-it /opt/task-venv/bin/pip3 /opt/task-venv/bin/jupyter /opt/task-venv/bin/dotenv /opt/task-venv/bin/jupytext-config /opt/task-venv/bin/python /opt/task-venv/bin/activate.csh /opt/task-venv/bin/python3 /opt/task-venv/bin/jupytext /opt/task-venv/bin/isympy /opt/task-venv/bin/pip3.12 /opt/task-venv/bin/jupyter-trust /opt/task-venv/bin/kaggle /opt/task-venv/bin/numpy-config /opt/task-venv/bin/idna /opt/task-venv/bin/torchfrtrace /opt/task-venv/bin/jsonschema /opt/task-venv/bin/normalizer /opt/task-venv/bin/jupyter-migrate /opt/task-venv/bin/python3.12 /opt/task-venv/bin/pip /opt/task-venv/bin/jupyter-troubleshoot /opt/task-venv/bin/chardetect /opt/task-venv/bin/slugify /opt/task-venv/bin/activate /opt/task-venv/lib64 /opt/task-venv: total 32 dr-xr-xr-x 1 root root 4096 Aug 31 18:58 . drwxr-xr-x 1 root root 4096 Aug 31 18:58 .. -r--r--r-- 1 root root 0 Aug 31 18:58 .lock dr-xr-xr-x 1 root root 4096 Aug 31 18:58 bin dr-xr-xr-x 1 root root 4096 Aug 31 18:58 etc dr-xr-xr-x 1 root root 4096 Aug 31 18:58 include dr-xr-xr-x 1 root root 4096 Aug 31 18:58 lib lrwxrwxrwx 1 root root 3 Aug 31 18:58 lib64 -> lib -r--r--r-- 1 root root 171 Aug 31 18:58 pyvenv.cfg dr-xr-xr-x 1 root root 4096 Aug 31 18:58 share /opt/task-venv/bin: total 124 dr-xr-xr-x 1 root root 4096 Aug 31 18:58 . dr-xr-xr-x 1 root root 4096 Aug 31 18:58 .. -r--r--r-- 1 root root 9033 Aug 31 18:58 Activate.ps1 -r--r--r-- 1 root root 2158 Aug 31 18:58 activate -r--r--r-- 1 root root 920 Aug 31 18:58 activate.csh -r--r--r-- 1 root root 2195 Aug 31 18:58 activate.fish -r-xr-xr-x 1 root root 302 Aug 31 18:58 chardetect -r-xr-xr-x 1 root root 304 Aug 31 18:58 dotenv -r-xr-xr-x 1 root root 308 Aug 31 18:58 f2py -r-xr-xr-x 1 root root 299 Aug 31 18:58 idna -r-xr-xr-x 1 root root 297 Aug 31 18:58 isympy -r-xr-xr-x 1 root root 305 Aug 31 18:58 jsonschema -r-xr-xr-x 1 root root 311 Aug 31 18:58 jupyter -r-xr-xr-x 1 root root 311 Aug 31 18:58 jupyter-migrate -r-xr-xr-x 1 root root 316 Aug 31 18:58 jupyter-troubleshoot -r-xr-xr-x 1 root root 344 Aug 31 18:58 jupyter-trust -r-xr-xr-x 1 root root 311 Aug 31 18:58 jupytext -r-xr-xr-x 1 root root 315 Aug 31 18:58 jupytext-config -r-xr-xr-x 1 root root 301 Aug 31 18:58 kaggle -r-xr-xr-x 1 root root 312 Aug 31 18:58 markdown-it -r-xr-xr-x 1 root root 325 Aug 31 18:58 normalizer -r-xr-xr-x 1 root root 308 Aug 31 18:58 numpy-config -r-xr-xr-x 1 root root 313 Aug 31 18:58 pip -r-xr-xr-x 1 root root 313 Aug 31 18:58 pip3 -r-xr-xr-x 1 root root 313 Aug 31 18:58 pip3.12 lrwxrwxrwx 1 root root 21 Aug 31 18:58 python -> /usr/local/bin/python lrwxrwxrwx 1 root root 6 Aug 31 18:58 python3 -> python lrwxrwxrwx 1 root root 6 Aug 31 18:58 python3.12 -> python -r-xr-xr-x 1 root root 307 Aug 31 18:58 slugify -r-xr-xr-x 1 root root 321 Aug 31 18:58 torchfrtrace -r-xr-xr-x 1 root root 312 Aug 31 18:58 torchrun -r-xr-xr-x 1 root root 299 Aug 31 18:58 tqdm /opt/task-venv/lib: total 12 dr-xr-xr-x 1 root root 4096 Aug 31 18:58 . dr-xr-xr-x 1 root root 4096 Aug 31 18:58 .. dr-xr-xr-x 1 root root 4096 Aug 31 18:58 python3.12 ## Step 11 · agent · 2026-08-31T19:02:26.366Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:02:30.302Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -c 'import sys,numpy,torch,pandas; print(sys.prefix, numpy.__version__, torch.__version__, pandas.__version__)'\\n/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:30000,max_output_tokens:20000});\ntext(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:02:41.322Z Script running with cell ID 5 Wall time 11.0 seconds Output: ## Step 12 · agent · 2026-08-31T19:02:43.376Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:02:44.155Z { "cell_id": "5", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:03:00.483Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"7a235a","wall_time_seconds":30.001703563,"session_id":34385,"original_token_count":71,"output":"/opt/task-venv 2.2.6 2.8.0+cpu 2.3.3\nloaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.9s\n"} ## Step 13 · agent · 2026-08-31T19:03:04.924Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:06.379Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:03:17.404Z Script running with cell ID 6 Wall time 11.0 seconds Output: ## Step 14 · agent · 2026-08-31T19:03:19.517Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:20.290Z { "cell_id": "6", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:03:36.407Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"5cf952","wall_time_seconds":30.001188643,"session_id":34385,"original_token_count":229,"output":"prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.3s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.1s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.8s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.2s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.5s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.2s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 15 · agent · 2026-08-31T19:03:39.259Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:03:40.664Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:03:51.700Z Script running with cell ID 7 Wall time 11.0 seconds Output: ## Step 16 · agent · 2026-08-31T19:03:53.724Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:03:54.499Z { "cell_id": "7", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:04:10.714Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"f14292","wall_time_seconds":30.001669427,"session_id":34385,"original_token_count":0,"output":""} ## Step 17 · agent · 2026-08-31T19:04:12.935Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:04:14.421Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:04:25.444Z Script running with cell ID 8 Wall time 11.0 seconds Output: ## Step 18 · agent · 2026-08-31T19:04:26.959Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:04:27.723Z { "cell_id": "8", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:04:44.457Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"e52be2","wall_time_seconds":30.001226317,"session_id":34385,"original_token_count":0,"output":""} ## Step 19 · agent · 2026-08-31T19:04:46.432Z ### Thinking unavailable — no text recorded Data preparation selected about 1.18M recent targets from 20M possible transitions. The current “hard” in-batch negatives are popularity-corrected, but they are still essentially accidental batch co-occurrences—not high-scoring confusions for each user—and the uniform pool spends most of its budget on easy tail items. Training is underway; meanwhile I’m extracting the loss code so the first variant can replace easy negatives without changing either tower. ### exec · 2026-08-31T19:04:51.409Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:05:02.435Z Script running with cell ID 9 Wall time 11.0 seconds Output: ## Step 20 · agent · 2026-08-31T19:05:04.132Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:04.912Z { "cell_id": "9", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:05:21.456Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"50d510","wall_time_seconds":30.00095638,"session_id":34385,"original_token_count":0,"output":""} ## Step 21 · agent · 2026-08-31T19:05:23.187Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:24.573Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:05:35.611Z Script running with cell ID 10 Wall time 11.0 seconds Output: ## Step 22 · agent · 2026-08-31T19:05:37.300Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:05:38.060Z { "cell_id": "10", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:05:54.621Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"8e6e46","wall_time_seconds":30.00190771,"session_id":34385,"original_token_count":0,"output":""} ## Step 23 · agent · 2026-08-31T19:05:56.475Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:05:57.888Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:06:08.921Z Script running with cell ID 11 Wall time 11.0 seconds Output: ## Step 24 · agent · 2026-08-31T19:06:10.623Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:11.381Z { "cell_id": "11", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:06:27.940Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"b4842c","wall_time_seconds":30.002003233,"session_id":34385,"original_token_count":0,"output":""} ## Step 25 · agent · 2026-08-31T19:06:30.398Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:34.602Z { "code": "const r = await tools.exec_command({cmd:\"rg -n \\\"negative|loss|train_epoch|sample|rank|ArgumentParser|add_argument|stage\\\" /app/solution/train_current_model.py | sed -n '1,240p'\\nsed -n '650,1080p' /app/solution/train_current_model.py\\nsed -n '300,700p' /app/solution/two_tower_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000}); text(r.output);" } ### Tool output · 2026-08-31T19:06:34.686Z Script completed Wall time 0.0 seconds Output: 6:loss. The fast default retains each user's most recent 128 training targets and 9:computes all of its timestep losses and carries the detached GRU hidden state 13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy, 14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every 55: loss_microbatch_size: int = 65_536 56: uniform_negatives: int = 24 57: inbatch_negatives: int = 8 61: negative_filter_scope: str = "full-training-history" 165: uniform_negatives=config.uniform_negatives, 166: inbatch_negatives=config.inbatch_negatives, 342: public: Path, stage: str, config: Config 346: "popularity_rank" 354: if stage == "validation": 463: f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} " 471:def cache_metadata(stage: str, config: Config) -> dict[str, object]: 474: "stage": stage, 477: "event_time_feature": "exp-negative-gap-over-60-days", 482: path: Path, prepared: SequenceData, stage: str, config: Config 487: metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)), 493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData: 496: expected = cache_metadata(stage, config) 513: stage: str, 519: if stage == "validation" 524: prepared = load_cache(cache, stage, config) 528: prepared, built_eligibility = prepare_data(public, stage, config) 530: save_cache(cache, prepared, stage, config) 603: f"full-history false-negative filter: users={len(prepared.user_ids):,} " 609:def invalid_full_history_negatives( 610: negatives: np.ndarray, 616: """Return row-level invalid negatives using the complete training prefix.""" 617: byte_columns = negatives >> 3 618: bit_columns = negatives & 7 621: invalid |= negatives == targets[:, None] 622: invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) 623: for column in range(1, negatives.shape[1]): 625: negatives[:, column, None] == negatives[:, :column] 630:def draw_full_history_filtered_negatives( 641: """Draw mixed negatives and reject all known public-training positives.""" 647: (batch_size, config.uniform_negatives), 653: if config.inbatch_negatives: 656: config.inbatch_negatives, max(len(unique_targets) - 1, 0) 672: if corrected_count < config.inbatch_negatives: 675: (batch_size, config.inbatch_negatives - corrected_count), 681: negatives = torch.cat([uniform, inbatch], dim=1).numpy() 682: correction = np.ones_like(negatives, dtype=np.float32) 685: :, config.uniform_negatives : config.uniform_negatives + corrected_count 688: invalid = invalid_full_history_negatives( 689: negatives, 697: torch.from_numpy(negatives).to(device), 706: negatives[invalid] = replacements 709: invalid = invalid_full_history_negatives( 710: negatives, 717: raise RuntimeError("full-history negative resampling failed") 719: torch.from_numpy(negatives).to(device), 724:def draw_training_negatives( 736: if config.negative_filter_scope == "full-training-history": 738: raise RuntimeError("full-history negative filter was not built") 739: return draw_full_history_filtered_negatives( 750: return base.draw_filtered_mixed_negatives( 755: config.uniform_negatives, 756: config.inbatch_negatives, 816: negatives: torch.Tensor, 818: all_items = torch.cat([positives, negatives.reshape(-1)]) 822: negative_vectors = encoded[inverse[len(positives) :]].reshape( 823: len(positives), negatives.shape[1], -1 825: return positive_vectors, negative_vectors 845:def corrected_negative_loss( 851: total = config.uniform_negatives + config.inbatch_negatives 853: if config.uniform_negatives: 854: result += config.uniform_negatives * raw[ 855: :, : config.uniform_negatives 857: if config.inbatch_negatives: 858: selected = slice(config.uniform_negatives, total) 860: result += config.inbatch_negatives * ( 880: raise RuntimeError("chronological target partition produced negative counts") 915: public: Path, stage: str, item_ids: np.ndarray 918: prefixes = ("train",) if stage == "validation" else ("train", "val") 957: stage: str, 978: public, stage, prepared.item_ids 981: base.TRAIN_END if stage == "validation" else base.VALIDATION_END 1119: negatives: torch.Tensor, 1120: negative_weights: torch.Tensor, 1121: sample_weights: np.ndarray, 1127: total_weight = float(sample_weights.sum()) 1129: raise RuntimeError("target block has no positive sample weight") 1132: "loss_sum": 0.0, 1134: "negative_sum": 0.0, 1138: for begin in range(0, target_count, config.loss_microbatch_size): 1139: end = min(begin + config.loss_microbatch_size, target_count) 1162: positive_vectors, negative_vectors = candidate_vectors( 1165: negatives[begin:end], 1168: negative_logits = torch.einsum( 1169: "bd,bkd->bk", query_vectors, negative_vectors 1171: positive_loss = F.softplus(-positive_logits) 1172: negative_loss = corrected_negative_loss( 1173: negative_logits, negative_weights[begin:end], config 1176: sample_weights[begin:end].astype(np.float32, copy=False) 1178: loss_sum = ( 1179: selected_weights * (positive_loss + negative_loss) / 2.0 1181: if not bool(torch.isfinite(loss_sum)): 1182: raise RuntimeError("dense next-item training produced non-finite loss") 1184: (loss_sum / total_weight).backward() 1188: statistics["loss_sum"] += float(loss_sum.detach().cpu()) 1190: statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) 1192: (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() 1227: negative_count = config.uniform_negatives + config.inbatch_negatives 1228: total_loss = 0.0 1232: total_negative = 0.0 1293: negatives, negative_weights = draw_training_negatives( 1314: negatives, 1315: negative_weights, 1327: total_loss += statistics["loss_sum"] 1331: total_negative += statistics["negative_sum"] 1337: batch_bce = statistics["loss_sum"] / max( 1340: running_bce = total_loss / max(total_weight, 1.0e-12) 1351: "loss": total_loss / max(total_weight, 1.0e-12), 1353: "negative_logit": total_negative / max(total_targets * negative_count, 1), 1355: / max(total_targets * negative_count, 1), 1388: total_loss = 0.0 1449: negatives, negative_weights = draw_training_negatives( 1469: negatives, 1470: negative_weights, 1475: total_loss += statistics["loss_sum"] 1481: "validation_loss": total_loss / max(total_weight, 1.0e-12), 1523: stage: str, 1560: stage, 1567: if config.negative_filter_scope == "full-training-history" 1571: best_loss = float("inf") 1601: "negative_filter_scope", "last-100" 1629: stored_filter_scope != config.negative_filter_scope 1657: prior.pop("validation_loss", None) 1660: f"{stored_filter_scope}->{config.negative_filter_scope}, " 1666: if "validation_loss" not in prior: 1682: best_loss = float(prior["validation_loss"]) 1687: f"loss={best_loss:.6f}" 1731: diagnostics["validation_loss"] 1732: < best_loss - config.early_stopping_min_delta 1735: best_loss = diagnostics["validation_loss"] 1754: f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " 1755: f"validation_loss={diagnostics['validation_loss']:.5f} " 1766: f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" 1776: f"loss={best_loss:.6f}" 1861:def rank_candidates( 1867: ranking_batch_size: int, 1882: for start in range(0, len(eligibility.uids), ranking_batch_size): 1883: end = min(start + ranking_batch_size, len(eligibility.uids)) 1896: (uid, int(prepared.item_ids[column]), rank) 1897: for rank, column in enumerate(columns, start=1) 1900: log(f"ranked users={end:,}/{len(eligibility.uids):,}") 1909: return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics 1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None: 1923: parser = argparse.ArgumentParser(description=__doc__) 1924: parser.add_argument( 1925: "--stage", choices=("validation", "submission"), default="validation" 1927: parser.add_argument("--output", type=Path, required=True) 1928: parser.add_argument("--public-dir", type=Path, required=True) 1929: parser.add_argument("--prepared-cache", type=Path) 1930: parser.add_argument("--device", choices=("cpu", "mps"), default="cpu") 1931: parser.add_argument("--threads", type=int, default=8) 1932: parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps) 1933: parser.add_argument( 1936: parser.add_argument("--epochs", type=int, default=Config.epochs) 1937: parser.add_argument("--learning-rate", type=float, default=Config.learning_rate) 1938: parser.add_argument( 1939: "--loss-microbatch-size", 1941: default=Config.loss_microbatch_size, 1943: parser.add_argument( 1944: "--negative-filter-scope", 1946: default=Config.negative_filter_scope, 1948: parser.add_argument( 1953: parser.add_argument( 1958: parser.add_argument( 1963: parser.add_argument( 1968: parser.add_argument( 1973: parser.add_argument( 1978: parser.add_argument( 1984: parser.add_argument( 1990: parser.add_argument( 1995: parser.add_argument( 2000: parser.add_argument("--checkpoint-dir", type=Path) 2001: parser.add_argument("--resume-checkpoint", type=Path) 2002: parser.add_argument("--seed", type=int, default=Config.seed) 2003: parser.add_argument("--ranking-batch-size", type=int, default=64) 2004: parser.add_argument("--prepare-only", action="store_true") 2012: or args.loss_microbatch_size < 1 2033: loss_microbatch_size=args.loss_microbatch_size, 2034: negative_filter_scope=args.negative_filter_scope, 2057: args.stage, 2071: "stage": args.stage, 2095: args.stage, 2102: ranking, embedding_diagnostics = rank_candidates( 2108: args.ranking_batch_size, 2110: write_ranking(ranking, args.output.resolve()) 2115: "stage": args.stage, 2117: "rows": len(ranking), 2118: "users": int(ranking.uid.nunique()), 2126: "sequence_encoder": "one-layer-GRU-dense-timestep-loss", 2129: "loss_execution": "gradient-accumulated-microbatches", 2135: "objective": "sample-weighted-BCE-at-every-timestep", 2136: "negative_sampling": ( 2139: "false_negative_filter": config.negative_filter_scope, ) inbatch = torch.empty((batch_size, 0), dtype=torch.long) corrected_count = 0 if config.inbatch_negatives: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min( config.inbatch_negatives, max(len(unique_targets) - 1, 0) ) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq( torch.from_numpy(targets_cpu)[:, None] ), -1.0, ) columns = torch.topk( priorities, k=corrected_count, dim=1 ).indices inbatch = unique_targets[columns] if corrected_count < config.inbatch_negatives: filler = torch.randint( n_items, (batch_size, config.inbatch_negatives - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives = torch.cat([uniform, inbatch], dim=1).numpy() correction = np.ones_like(negatives, dtype=np.float32) if corrected_count: correction[ :, config.uniform_negatives : config.uniform_negatives + corrected_count ] = item_correction[inbatch[:, :corrected_count]].numpy() for _ in range(32): invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if not np.any(invalid): return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) replacements = torch.randint( n_items, (int(invalid.sum()),), generator=generator, device="cpu", ).numpy() negatives[invalid] = replacements # Rejected in-batch items become ordinary uniform draws. correction[invalid] = 1.0 invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if np.any(invalid): raise RuntimeError("full-history negative resampling failed") return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) def draw_training_negatives( positive_items: torch.Tensor, known_history: torch.Tensor, liked_items: torch.Tensor, user_rows: np.ndarray, known_positive_packed: np.ndarray | None, prepared: SequenceData, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: if config.negative_filter_scope == "full-training-history": if known_positive_packed is None: raise RuntimeError("full-history negative filter was not built") return draw_full_history_filtered_negatives( positive_items, user_rows, liked_items, known_positive_packed, len(prepared.item_ids), config, item_correction, generator, device, ) return base.draw_filtered_mixed_negatives( positive_items, known_history, liked_items, len(prepared.item_ids), config.uniform_negatives, config.inbatch_negatives, item_correction, generator, device, ) def event_sequence( model: base.SequentialTwoTower, items: torch.Tensor, features: torch.Tensor, hidden: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: inputs = torch.cat([model.context_embedding(items), features], dim=-1) projected = model.event_projection(inputs) return model.sequence_encoder(projected, hidden) def pool_explicit_states( model: base.SequentialTwoTower, prepared: SequenceData, state_ids: np.ndarray, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: unique_states, inverse = np.unique(state_ids, return_inverse=True) liked_items = torch.from_numpy( prepared.liked_states[unique_states].astype(np.int64) ).to(device) disliked_items = torch.from_numpy( prepared.disliked_states[unique_states].astype(np.int64) ).to(device) liked_vectors = model._mean_context_channel(liked_items) disliked_vectors = model._mean_context_channel(disliked_items) inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device) counts = prepared.state_counts[state_ids] return ( liked_vectors[inverse_rows], disliked_vectors[inverse_rows], torch.from_numpy(counts.astype(np.float32)).to(device), ) def user_queries( model: base.SequentialTwoTower, sequence_vectors: torch.Tensor, liked_vectors: torch.Tensor, disliked_vectors: torch.Tensor, user_features: torch.Tensor, ) -> torch.Tensor: return model.user_tower( torch.cat( [sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1, ) ) def candidate_vectors( model: base.SequentialTwoTower, positives: torch.Tensor, negatives: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: all_items = torch.cat([positives, negatives.reshape(-1)]) unique_items, inverse = torch.unique(all_items, return_inverse=True) encoded = model.encode_items(unique_items) positive_vectors = encoded[inverse[: len(positives)]] negative_vectors = encoded[inverse[len(positives) :]].reshape( len(positives), negatives.shape[1], -1 ) return positive_vectors, negative_vectors def history_windows( sequence: np.ndarray, start: int, count: int, width: int, padding_item: int, ) -> np.ndarray: base_start = max(0, start - width + 1) segment = sequence[base_start : start + count] padded = np.concatenate( [np.full(width - 1, padding_item, dtype=np.uint16), segment] ) windows = np.lib.stride_tricks.sliding_window_view(padded, width) offset = start - base_start return windows[offset : offset + count].copy() def corrected_negative_loss( logits: torch.Tensor, correction: torch.Tensor, config: Config, ) -> torch.Tensor: raw = F.softplus(logits) total = config.uniform_negatives + config.inbatch_negatives result = torch.zeros(len(logits), device=logits.device) if config.uniform_negatives: result += config.uniform_negatives * raw[ :, : config.uniform_negatives ].mean(dim=1) if config.inbatch_negatives: selected = slice(config.uniform_negatives, total) weights = correction[:, selected] result += config.inbatch_negatives * ( (raw[:, selected] * weights).sum(dim=1) / weights.sum(dim=1).clamp_min(1.0e-6) ) return result / total def retained_target_window_starts( total_targets: np.ndarray, validation_counts: np.ndarray, training_counts: np.ndarray, ) -> np.ndarray: """Return the discarded prefix before the retained chronological suffix.""" training_region_ends = total_targets - validation_counts window_starts = training_region_ends - training_counts if ( np.any(window_starts < 0) or np.any(training_counts < 0) or np.any(validation_counts < 0) ): raise RuntimeError("chronological target partition produced negative counts") if not np.array_equal( window_starts + training_counts + validation_counts, total_targets ): raise RuntimeError("chronological target window is not a final contiguous suffix") return window_starts def chronological_target_partition( prepared: SequenceData, validation_fraction: float, max_train_targets_per_user: int, max_validation_targets_per_user: int, ) -> TargetPartition: """Select a recent contiguous window with a final out-of-time BCE suffix.""" totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64) validation = np.floor(totals * validation_fraction).astype(np.int64) validation[(totals >= 2) & (validation == 0)] = 1 if max_validation_targets_per_user: validation = np.minimum(validation, max_validation_targets_per_user) history_training = totals - validation training = history_training.copy() if max_train_targets_per_user: training = np.minimum(training, max_train_targets_per_user) window_starts = retained_target_window_starts(totals, validation, training) return TargetPartition( window_starts=window_starts, training_counts=training, validation_counts=validation, history_training_counts=history_training, ) def load_organic_like_outcomes( public: Path, stage: str, item_ids: np.ndarray ) -> dict[int, dict[int, np.ndarray]]: """Load mapped organic-like timestamps from public fitting history.""" prefixes = ("train",) if stage == "validation" else ("train", "val") frames = [ pd.read_parquet( public / "data" / f"{prefix}_likes.parquet", columns=["uid", "timestamp", "item_id", "is_organic"], ) for prefix in prefixes ] likes = pd.concat(frames, ignore_index=True) likes = likes[likes.is_organic == 1].copy() mapped = pd.Index(item_ids).get_indexer( likes.item_id.to_numpy(dtype=np.int64) ) likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy() likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False) likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable") nested: dict[int, dict[int, list[int]]] = {} for raw_uid, raw_timestamp, raw_item in likes.itertuples( index=False, name=None ): nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append( int(raw_timestamp) ) result = { uid: { item: np.asarray(timestamps, dtype=np.int64) for item, timestamps in items.items() } for uid, items in nested.items() } log( f"loaded organic-like outcomes: rows={len(likes):,} " f"users={len(result):,}" ) return result def build_target_weights( public: Path, stage: str, prepared: SequenceData, training_counts: np.ndarray, config: Config, ) -> np.ndarray: """Build nonzero per-token weights without dropping adjacent targets.""" if config.target_weighting == "uniform": weights = np.ones(len(prepared.sequence_items), dtype=np.float32) else: weights = np.full( len(prepared.sequence_items), config.ordinary_target_weight, dtype=np.float32, ) organic_strong = ( (prepared.event_features[:, 0] > 0.5) & (prepared.event_features[:, 1] >= 0.8) ) weights[organic_strong] = config.organic_strong_target_weight if config.target_weighting == "discovery-graded": like_outcomes = load_organic_like_outcomes( public, stage, prepared.item_ids ) history_end = ( base.TRAIN_END if stage == "validation" else base.VALIDATION_END ) horizon = config.discovery_horizon_days * base.DAY grade_three = 0 for user_row, uid in enumerate(prepared.user_ids): start = int(prepared.offsets[user_row]) end = int(prepared.offsets[user_row + 1]) if end - start < 2: continue last_training_target = start + int(training_counts[user_row]) first_validation_target = last_training_target + 1 split_timestamp = ( int(prepared.sequence_timestamps[first_validation_target]) if first_validation_target < end else history_end ) user_likes = like_outcomes.get(int(uid), {}) seen = {int(prepared.sequence_items[start])} for position in range(start + 1, end): item = int(prepared.sequence_items[position]) novel = item not in seen seen.add(item) if not novel: # Repeats remain training examples, but they cannot be # eligible discovery outcomes at the corresponding # cutoff and therefore keep only the ordinary weight. weights[position] = config.ordinary_target_weight continue timestamps = user_likes.get(item) if timestamps is None: continue timestamp = int(prepared.sequence_timestamps[position]) upper = min( timestamp + horizon, split_timestamp if position <= last_training_target else history_end, ) outcome = int(np.searchsorted(timestamps, timestamp, side="left")) if outcome < len(timestamps) and int(timestamps[outcome]) < upper: weights[position] = config.organic_like_target_weight grade_three += 1 log( f"grade-3 first-encounter targets={grade_three:,} " f"horizon_days={config.discovery_horizon_days}" ) log( f"target weighting={config.target_weighting} " f"min={weights.min():.3f} mean={weights.mean():.3f} " f"max={weights.max():.3f}" ) return weights def build_dense_block( prepared: SequenceData, target_weights: np.ndarray, users: np.ndarray, window_starts: np.ndarray, process_counts: np.ndarray, score_starts: np.ndarray, start: int, steps: int, config: Config, ) -> tuple[np.ndarray, ...]: """Build one causal sequence block and mark the target rows to score.""" batch_size = len(users) padding_item = len(prepared.item_ids) input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16) event_features = np.zeros((batch_size, steps, 3), dtype=np.float16) activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16) state_ids = np.zeros((batch_size, steps), dtype=np.uint32) targets = np.zeros((batch_size, steps), dtype=np.uint16) weights = np.ones((batch_size, steps), dtype=np.float32) scored = np.zeros((batch_size, steps), dtype=bool) histories = np.full( (batch_size, steps, config.bptt_steps), padding_item, dtype=np.uint16, ) processed = np.zeros(batch_size, dtype=np.int32) lengths = prepared.lengths for row, user_row in enumerate(users): count = min(steps, max(int(process_counts[row]) - start, 0)) if not count: continue offset = int(prepared.offsets[user_row]) window_start = int(window_starts[row]) absolute_start = window_start + start positions = slice( offset + absolute_start, offset + absolute_start + count, ) target_positions = slice( offset + absolute_start + 1, offset + absolute_start + count + 1, ) input_items[row, :count] = prepared.sequence_items[positions] event_features[row, :count] = prepared.event_features[positions] input_size=config.hidden_dim, hidden_size=dim, num_layers=1, batch_first=True, ) self.user_tower = nn.Sequential( nn.Linear(dim * 3 + 6, config.user_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_1, config.user_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.user_hidden_2, dim), ) self.candidate_embedding = nn.Embedding(n_items, dim) self.register_buffer( "item_features", torch.from_numpy(item_features), persistent=True ) self.item_tower = nn.Sequential( nn.Linear(dim + n_item_features, config.item_hidden_1), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_1, config.item_hidden_2), nn.GELU(), nn.Dropout(config.dropout), nn.Linear(config.item_hidden_2, dim), ) nn.init.normal_(self.context_embedding.weight, std=0.02) nn.init.normal_(self.candidate_embedding.weight, std=0.02) with torch.no_grad(): self.context_embedding.weight[n_items].zero_() def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor: mask = items.ne(self.n_items) vectors = self.context_embedding(items) return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum( dim=1, keepdim=True ).clamp_min(1) def encode_items(self, items: torch.Tensor) -> torch.Tensor: inputs = torch.cat( [self.candidate_embedding(items), self.item_features[items]], dim=1 ) return self.item_tower(inputs) def invalid_negative_mask( negatives: torch.Tensor, targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, ) -> torch.Tensor: """Identify targets, known positives, active likes, and row duplicates.""" invalid = negatives.eq(targets[:, None]) invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2) invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= negatives[:, column, None].eq( negatives[:, :column] ).any(dim=1) return invalid def draw_filtered_mixed_negatives( targets: torch.Tensor, context_items: torch.Tensor, liked_items: torch.Tensor, n_items: int, uniform_count: int, inbatch_count: int, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw uniform and unique-target in-batch negatives.""" batch_size = len(targets) uniform = torch.randint( n_items, (batch_size, uniform_count), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu") corrected_count = 0 if inbatch_count: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0)) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq(targets.detach().cpu()[:, None]), -1.0, ) columns = torch.topk(priorities, k=corrected_count, dim=1).indices inbatch = unique_targets[columns] if corrected_count < inbatch_count: filler = torch.randint( n_items, (batch_size, inbatch_count - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives_cpu = torch.cat([uniform, inbatch], dim=1) correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32) if corrected_count: correction_cpu[ :, uniform_count : uniform_count + corrected_count ] = item_correction[inbatch[:, :corrected_count]] negatives = negatives_cpu.to(device) correction = correction_cpu.to(device) for _ in range(32): invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if not bool(invalid.any()): return negatives, correction replacements = torch.randint( n_items, (int(invalid.sum().detach().cpu()),), generator=generator, device="cpu", ).to(device) negatives[invalid] = replacements correction[invalid] = 1.0 invalid = invalid_negative_mask( negatives, targets, context_items, liked_items ) if bool(invalid.any()): raise RuntimeError("mixed negative resampling failed false-negative filter") return negatives, correction ## Step 26 · agent · 2026-08-31T19:06:39.834Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:06:41.232Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:06:52.254Z Script running with cell ID 13 Wall time 11.0 seconds Output: ## Step 27 · agent · 2026-08-31T19:06:53.770Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:06:54.554Z { "cell_id": "13", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:07:11.269Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"40d2e6","wall_time_seconds":30.001070634,"session_id":34385,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.2s\n"} ## Step 28 · agent · 2026-08-31T19:07:12.650Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:14.021Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:07:25.074Z Script running with cell ID 14 Wall time 11.0 seconds Output: ## Step 29 · agent · 2026-08-31T19:07:29.428Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:07:30.211Z { "cell_id": "14", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:07:44.089Z Script completed Wall time 13.9 seconds Output: {"chunk_id":"ddecf6","wall_time_seconds":30.0017543,"session_id":34385,"original_token_count":58,"output":"epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"} ## Step 30 · agent · 2026-08-31T19:07:46.192Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:07:47.594Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:07:58.635Z Script running with cell ID 15 Wall time 11.0 seconds Output: ## Step 31 · agent · 2026-08-31T19:08:00.448Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:01.244Z { "cell_id": "15", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:08:17.642Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"af05c0","wall_time_seconds":30.001933297,"session_id":34385,"original_token_count":0,"output":""} ## Step 32 · agent · 2026-08-31T19:08:19.389Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:20.780Z { "code": "const r = await tools.write_stdin({session_id:34385,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:08:31.823Z Script running with cell ID 16 Wall time 11.0 seconds Output: ## Step 33 · agent · 2026-08-31T19:08:33.604Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:08:34.390Z { "cell_id": "16", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:08:39.970Z Script completed Wall time 5.6 seconds Output: {"chunk_id":"a6e217","wall_time_seconds":19.13857311,"exit_code":0,"original_token_count":802,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/production_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 361.405,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5398380488240146,\n \"positive_logit\": 0.7427799616596575,\n \"negative_logit\": -0.6222247061344452,\n \"pair_accuracy\": 0.754773301847281,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 212.262626608,\n \"validation_loss\": 0.4966362179911506,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 15.493451819000029\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.6030172109603882,\n \"query_norm_std\": 0.7697497606277466,\n \"item_norm_mean\": 1.7570278644561768,\n \"item_norm_std\": 0.8279486298561096\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 34 · agent · 2026-08-31T19:08:41.437Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:08:43.586Z { "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:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:08:46.952Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"0ced7c","wall_time_seconds":3.184462268,"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 35 · agent · 2026-08-31T19:08:53.657Z ### Thinking unavailable — no text recorded Baseline public NDCG@10 is **0.02128** (4,263 scored users). The main mismatch is now concrete: 75.5% sampled-pair accuracy and good BCE coexist with weak top-10 retrieval because most uniform negatives are trivial. I’m implementing a bounded candidate-pool sampler that draws a larger mix of catalog/popularity candidates, scores them with the current towers, and trains only on the hardest valid subset; full-history false-negative filtering remains intact. ### exec · 2026-08-31T19:09:01.726Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '560,770p' /app/solution/train_current_model.py\\nsed -n '1080,1515p' /app/solution/train_current_model.py\\nsed -n '1515,1825p' /app/solution/train_current_model.py\\nsed -n '1920,2160p' /app/solution/train_current_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:40000}); text(r.output);" } ### Tool output · 2026-08-31T19:09:01.789Z Script completed Wall time 0.1 seconds Output: Warning: truncated output (original token count: 11193) Total output lines: 1199 raw = np.power( (1.0 / len(counts)) / probabilities, config.inbatch_correction_power, ) normalization = float(np.sum(counts * raw) / max(total, 1.0)) correction = np.clip( raw / max(normalization, 1.0e-12), config.inbatch_correction_min, config.inbatch_correction_max, ).astype(np.float32) mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0)) log( f"inbatch correction: min={correction.min():.3f} " f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}" ) return torch.from_numpy(correction) def build_known_positive_filter( prepared: SequenceData, training_counts: np.ndarray ) -> np.ndarray: """Pack each user's complete public-training positive-item set.""" width = (len(prepared.item_ids) + 7) // 8 packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8) retained = 0 for user_row, target_count in enumerate(training_counts): if not target_count: continue offset = int(prepared.offsets[user_row]) # T adjacent targets consume sequence positions 0 through T. The # chronological BCE suffix starts at T + 1 and is intentionally absent. items = np.unique( prepared.sequence_items[ offset : offset + int(target_count) + 1 ].astype(np.int64) ) byte_columns = items >> 3 bit_values = np.left_shift( np.uint8(1), (items & 7).astype(np.uint8) ) np.bitwise_or.at(packed[user_row], byte_columns, bit_values) retained += len(items) log( f"full-history false-negative filter: users={len(prepared.user_ids):,} " f"unique_user_items={retained:,} bytes={packed.nbytes:,}" ) return packed def invalid_full_history_negatives( negatives: np.ndarray, targets: np.ndarray, user_rows: np.ndarray, known_positive_packed: np.ndarray, liked_items: np.ndarray, ) -> np.ndarray: """Return row-level invalid negatives using the complete training prefix.""" byte_columns = negatives >> 3 bit_columns = negatives & 7 known = known_positive_packed[user_rows[:, None], byte_columns] invalid = ((known >> bit_columns) & 1).astype(bool) invalid |= negatives == targets[:, None] invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2) for column in range(1, negatives.shape[1]): invalid[:, column] |= ( negatives[:, column, None] == negatives[:, :column] ).any(axis=1) return invalid def draw_full_history_filtered_negatives( targets: torch.Tensor, user_rows: np.ndarray, liked_items: torch.Tensor, known_positive_packed: np.ndarray, n_items: int, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: """Draw mixed negatives and reject all known public-training positives.""" targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False) liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False) batch_size = len(targets_cpu) uniform = torch.randint( n_items, (batch_size, config.uniform_negatives), generator=generator, device="cpu", ) inbatch = torch.empty((batch_size, 0), dtype=torch.long) corrected_count = 0 if config.inbatch_negatives: unique_targets = torch.unique(targets.detach().cpu()) corrected_count = min( config.inbatch_negatives, max(len(unique_targets) - 1, 0) ) if corrected_count: priorities = torch.rand( (batch_size, len(unique_targets)), generator=generator ) priorities.masked_fill_( unique_targets[None, :].eq( torch.from_numpy(targets_cpu)[:, None] ), -1.0, ) columns = torch.topk( priorities, k=corrected_count, dim=1 ).indices inbatch = unique_targets[columns] if corrected_count < config.inbatch_negatives: filler = torch.randint( n_items, (batch_size, config.inbatch_negatives - corrected_count), generator=generator, device="cpu", ) inbatch = torch.cat([inbatch, filler], dim=1) negatives = torch.cat([uniform, inbatch], dim=1).numpy() correction = np.ones_like(negatives, dtype=np.float32) if corrected_count: correction[ :, config.uniform_negatives : config.uniform_negatives + corrected_count ] = item_correction[inbatch[:, :corrected_count]].numpy() for _ in range(32): invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if not np.any(invalid): return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) replacements = torch.randint( n_items, (int(invalid.sum()),), generator=generator, device="cpu", ).numpy() negatives[invalid] = replacements # Rejected in-batch items become ordinary uniform draws. correction[invalid] = 1.0 invalid = invalid_full_history_negatives( negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu, ) if np.any(invalid): raise RuntimeError("full-history negative resampling failed") return ( torch.from_numpy(negatives).to(device), torch.from_numpy(correction).to(device), ) def draw_training_negatives( positive_items: torch.Tensor, known_history: torch.Tensor, liked_items: torch.Tensor, user_rows: np.ndarray, known_positive_packed: np.ndarray | None, prepared: SequenceData, config: Config, item_correction: torch.Tensor, generator: torch.Generator, device: torch.device, ) -> tuple[torch.Tensor, torch.Tensor]: if config.negative_filter_scope == "full-training-history": if known_positive_packed is None: raise RuntimeError("full-history negative filter was not built") return draw_full_history_filtered_negatives( positive_items, user_rows, liked_items, known_positive_packed, len(prepared.item_ids), config, item_correction, generator, device, ) return base.draw_filtered_mixed_negatives( positive_items, known_history, liked_items, len(prepared.item_ids), config.uniform_negatives, config.inbatch_negatives, item_correction, generator, device, ) def event_sequence( model: base.SequentialTwoTower, items: torch.Tensor, features: torch.Tensor, hidden: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: inputs = torch.cat([model.context_embedding(items), features], dim=-1) projected = model.event_projection(inputs) event_features[row, :count] = prepared.event_features[positions] activity_features[row, :count] = prepared.activity_features[positions] state_ids[row, :count] = prepared.state_ids[positions] targets[row, :count] = prepared.sequence_items[target_positions] weights[row, :count] = target_weights[target_positions] score_from = min(count, max(int(score_starts[row]) - start, 0)) scored[row, score_from:count] = True processed[row] = count user_sequence = prepared.sequence_items[ offset : offset + int(lengths[user_row]) ] histories[row, :count] = history_windows( user_sequence, absolute_start, count, config.bptt_steps, padding_item, ) return ( input_items, event_features, activity_features, state_ids, targets, scored, histories, processed, weights, ) def score_target_microbatches( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, sequence_vectors: torch.Tensor, flat_states: np.ndarray, flat_activity: np.ndarray, positive_items: torch.Tensor, negatives: torch.Tensor, negative_weights: torch.Tensor, sample_weights: np.ndarray, device: torch.device, backward: bool, ) -> tuple[dict[str, float], torch.Tensor | None]: """Compute one logical block's exact BCE in bounded-memory slices.""" target_count = len(positive_items) total_weight = float(sample_weights.sum()) if total_weight <= 0: raise RuntimeError("target block has no positive sample weight") sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None statistics = { "loss_sum": 0.0, "positive_sum": 0.0, "negative_sum": 0.0, "pair_correct": 0.0, "weight_sum": 0.0, } for begin in range(0, target_count, config.loss_microbatch_size): end = min(begin + config.loss_microbatch_size, target_count) selected_states = flat_states[begin:end] if backward: sequence_input = ( sequence_vectors[begin:end].detach().requires_grad_(True) ) else: sequence_input = sequence_vectors[begin:end] liked_vectors, disliked_vectors, _ = pool_explicit_states( model, prepared, selected_states, device ) counts = prepared.state_counts[selected_states] numeric = np.zeros((end - begin, 6), dtype=np.float32) numeric[:, :4] = flat_activity[begin:end] numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0 numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0 query_vectors = user_queries( model, sequence_input, liked_vectors, disliked_vectors, torch.from_numpy(numeric).to(device), ) positive_vectors, negative_vectors = candidate_vectors( model, positive_items[begin:end], negatives[begin:end], ) positive_logits = (query_vectors * positive_vectors).sum(dim=1) negative_logits = torch.einsum( "bd,bkd->bk", query_vectors, negative_vectors ) positive_loss = F.softplus(-positive_logits) negative_loss = corrected_negative_loss( negative_logits, negative_weights[begin:end], config ) selected_weights = torch.from_numpy( sample_weights[begin:end].astype(np.float32, copy=False) ).to(device) loss_sum = ( selected_weights * (positive_loss + negative_loss) / 2.0 ).sum() if not bool(torch.isfinite(loss_sum)): raise RuntimeError("dense next-item training produced non-finite loss") if backward: (loss_sum / total_weight).backward() if sequence_input.grad is None: raise RuntimeError("sequence microbatch did not produce a gradient") sequence_gradient[begin:end] = sequence_input.grad statistics["loss_sum"] += float(loss_sum.detach().cpu()) statistics["positive_sum"] += float(positive_logits.detach().sum().cpu()) statistics["negative_sum"] += float(negative_logits.detach().sum().cpu()) statistics["pair_correct"] += float( (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu() ) statistics["weight_sum"] += float(selected_weights.sum().detach().cpu()) return statistics, sequence_gradient def release_accelerator_cache(device: torch.device) -> None: if device.type == "mps": torch.mps.empty_cache() def run_training_epoch( model: base.SequentialTwoTower, optimizer: torch.optim.Optimizer, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, target_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, generator: torch.Generator, device: torch.device, epoch: int, ) -> dict[str, float]: model.train() train_users = np.flatnonzero(target_counts > 0) sorted_users = train_users[ np.argsort(target_counts[train_users], kind="stable") ] groups = [ sorted_users[start : start + config.user_batch_size] for start in range(0, len(sorted_users), config.user_batch_size) ] group_order = torch.randperm(len(groups), generator=generator).numpy() negative_count = config.uniform_negatives + config.inbatch_negatives total_loss = 0.0 total_targets = 0 total_weight = 0.0 total_positive = 0.0 total_negative = 0.0 total_pair_correct = 0.0 batches = 0 started = time.monotonic() for group_number in group_order: users = groups[int(group_number)] group_counts = target_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_counts.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_counts, np.zeros(len(users), dtype=np.int64), start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).to(device), torch.from_numpy(block[1].astype(np.float32)).to(device), hidden, ) hidden = torch.stack( [ sequence_outputs[row, count - 1] if count else hidden[0, row] for row, count in enumerate(block[7]) ] ).unsqueeze(0).detach() flat_scored = block[5].reshape(-1) if not np.any(flat_scored): continue mask = torch.from_numpy(flat_scored).to(device) sequence_vectors = sequence_outputs.reshape( -1, config.embedding_dim )[mask] flat_states = block[3].reshape(-1)[flat_scored] positive_items = torch.from_numpy( block[4].reshape(-1)[flat_scored].astype(np.int64) ).to(device) known_history = torch.from_numpy( block[6].reshape(-1, config.bptt_steps)[flat_scored].astype( np.int64 ) ).to(device) liked_items = torch.from_numpy( prepared.liked_states[flat_states].astype(np.int64) ).to(device) flat_user_rows = np.broadcast_to( users[:, None], block[5].shape ).reshape(-1)[flat_scored] negatives, negative_weights = draw_training_negatives( positive_items, known_history, liked_items, flat_user_rows, known_positive_packed, prepared, config, item_correction, generator, device, ) optimizer.zero_grad(set_to_none=True) statistics, sequence_gradient = score_target_microbatches( model, prepared, config, sequence_vectors, flat_states, block[2].reshape(-1, 4)[flat_scored].astype(np.float32), positive_items, negatives, negative_weights, block[8].reshape(-1)[flat_scored], device, backward=True, ) if sequence_gradient is None: raise RuntimeError("training block omitted its sequence gradient") sequence_vectors.backward(sequence_gradient) nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip) optimizer.step() count = len(positive_items) total_loss += statistics["loss_sum"] total_targets += count total_weight += statistics["weight_sum"] total_positive += statistics["positive_sum"] total_negative += statistics["negative_sum"] total_pair_correct += statistics["pair_correct"] batches += 1 if batches % 25 == 0: release_accelerator_cache(device) if batches % 250 == 0: batch_bce = statistics["loss_sum"] / max( statistics["weight_sum"], 1.0e-12 ) running_bce = total_loss / max(total_weight, 1.0e-12) log( f"epoch={epoch}/{config.epochs} batches={batches:,} " f"targets={total_targets:,} " f"batch_bce={batch_bce:.6f} " f"running_bce={running_bce:.6f} " f"elapsed={time.monotonic() - started:.1f}s" ) return { "epoch": float(epoch), "loss": total_loss / max(total_weight, 1.0e-12), "positive_logit": total_positive / max(total_targets, 1), "negative_logit": total_negative / max(total_targets * negative_count, 1), "pair_accuracy": total_pair_correct / max(total_targets * negative_count, 1), "targets": float(total_targets), "target_weight_sum": total_weight, "batches": float(batches), "seconds": time.monotonic() - started, } @torch.no_grad() def evaluate_validation_bce( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, target_weights: np.ndarray, window_starts: np.ndarray, training_counts: np.ndarray, validation_counts: np.ndarray, item_correction: torch.Tensor, known_positive_packed: np.ndarray | None, device: torch.device, ) -> dict[str, float]: """Measure deterministic BCE on each user's chronological target suffix.""" model.eval() totals = training_counts + validation_counts users_with_validation = np.flatnonzero(validation_counts > 0) ordered = users_with_validation[ np.argsort(totals[users_with_validation], kind="stable") ] groups = [ ordered[start : start + config.user_batch_size] for start in range(0, len(ordered), config.user_batch_size) ] generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000) total_loss = 0.0 total_targets = 0 total_weight = 0.0 started = time.monotonic() for group_number, users in enumerate(groups, start=1): group_totals = totals[users] group_starts = training_counts[users] hidden = torch.zeros( 1, len(users), config.embedding_dim, device=device ) max_targets = int(group_totals.max()) for start in range(0, max_targets, config.bptt_steps): steps = min(config.bptt_steps, max_targets - start) block = build_dense_block( prepared, target_weights, users, window_starts[users], group_totals, group_starts, start, steps, config, ) sequence_outputs, _ = event_sequence( model, torch.from_numpy(block[0].astype(np.int64)).t…1193 tokens truncated…ed(config.seed + 10_000) item_correction = build_item_correction( prepared, config, partition.window_starts, training_counts, ) target_weights = build_target_weights( public, stage, prepared, partition.history_training_counts, config, ) known_positive_packed = ( build_known_positive_filter(prepared, partition.history_training_counts) if config.negative_filter_scope == "full-training-history" else None ) history: list[dict[str, float]] = [] best_loss = float("inf") best_epoch = 0 best_state: dict[str, torch.Tensor] | None = None epochs_without_improvement = 0 start_epoch = 0 if resume_checkpoint is not None: payload = torch.load( resume_checkpoint, map_location=device, weights_only=False ) stored_config = payload.get("config", {}) architecture_fields = ( "bptt_steps", "embedding_dim", "hidden_dim", "user_hidden_1", "user_hidden_2", "item_hidden_1", "item_hidden_2", ) mismatches = { name: (stored_config.get(name), getattr(config, name)) for name in architecture_fields if stored_config.get(name) != getattr(config, name) } if mismatches: raise RuntimeError( f"resume checkpoint architecture differs: {mismatches}" ) stored_filter_scope = stored_config.get( "negative_filter_scope", "last-100" ) stored_target_weighting = stored_config.get( "target_weighting", "uniform" ) stored_weight_values = ( stored_config.get("ordinary_target_weight", 1.0), stored_config.get("organic_strong_target_weight", 1.0), stored_config.get("organic_like_target_weight", 3.0), stored_config.get("discovery_horizon_days", 7), ) current_weight_values = ( config.ordinary_target_weight, config.organic_strong_target_weight, config.organic_like_target_weight, config.discovery_horizon_days, ) stored_window_values = ( stored_config.get("validation_fraction", 0.10), stored_config.get("max_train_targets_per_user", 0), stored_config.get("max_validation_targets_per_user", 0), ) current_window_values = ( config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) objective_changed = ( stored_filter_scope != config.negative_filter_scope or stored_target_weighting != config.target_weighting or stored_weight_values != current_weight_values or stored_window_values != current_window_values ) model.load_state_dict(payload["model_state"]) if "optimizer_state" in payload and not objective_changed: optimizer.load_state_dict(payload["optimizer_state"]) for group in optimizer.param_groups: group["lr"] = config.learning_rate group["weight_decay"] = config.weight_decay log(f"restored optimizer state from {resume_checkpoint}") else: log( f"using a fresh AdamW optimizer after loading {resume_checkpoint}" ) if "generator_state" in payload and not objective_changed: generator.set_state(payload["generator_state"]) start_epoch = int(payload["epoch"]) if start_epoch >= config.epochs: raise RuntimeError( f"resume epoch {start_epoch} must be below --epochs {config.epochs}" ) prior = { str(name): float(value) for name, value in payload.get("diagnostics", {}).items() } if objective_changed: prior.pop("validation_loss", None) log( "BCE objective changed: filter " f"{stored_filter_scope}->{config.negative_filter_scope}, " "target weighting " f"{stored_target_weighting}->{config.target_weighting}, " f"target window {stored_window_values}->{current_window_values}; " "recomputing the resume checkpoint's validation BCE" ) if "validation_loss" not in prior: prior.update( evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) ) history.append(prior) best_loss = float(prior["validation_loss"]) best_epoch = start_epoch best_state = snapshot_model(model) log( f"resumed validation-BCE training: epoch={start_epoch} " f"loss={best_loss:.6f}" ) for epoch in range(start_epoch + 1, config.epochs + 1): diagnostics = run_training_epoch( model, optimizer, prepared, config, target_weights, partition.window_starts, training_counts, item_correction, known_positive_packed, generator, device, epoch, ) validation = evaluate_validation_bce( model, prepared, config, target_weights, partition.window_starts, training_counts, validation_counts, item_correction, known_positive_packed, device, ) diagnostics.update(validation) history.append(diagnostics) current_state = snapshot_model(model) if checkpoint_dir is not None: save_epoch_checkpoint( checkpoint_dir, epoch, current_state, config, diagnostics, optimizer, generator, ) improved = ( diagnostics["validation_loss"] < best_loss - config.early_stopping_min_delta ) if improved: best_loss = diagnostics["validation_loss"] best_epoch = epoch best_state = current_state epochs_without_improvement = 0 if checkpoint_dir is not None: torch.save( { "epoch": epoch, "config": asdict(config), "diagnostics": diagnostics, "model_state": current_state, "optimizer_state": optimizer.state_dict(), "generator_state": generator.get_state(), }, checkpoint_dir / "best.pt", ) else: epochs_without_improvement += 1 log( f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} " f"validation_loss={diagnostics['validation_loss']:.5f} " f"pair_accuracy={diagnostics['pair_accuracy']:.3f} " f"train_targets={int(diagnostics['targets']):,} " f"validation_targets={int(diagnostics['validation_targets']):,} " f"train_seconds={diagnostics['seconds']:.1f} " f"validation_seconds={diagnostics['validation_seconds']:.1f}" ) release_accelerator_cache(device) if epochs_without_improvement >= config.early_stopping_patience: log( f"early stopping after epoch={epoch}; " f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}" ) break if best_state is None: raise RuntimeError("training did not produce a valid checkpoint") model.load_state_dict(best_state) model.to(device).eval() log( f"restored best validation-BCE checkpoint: epoch={best_epoch} " f"loss={best_loss:.6f}" ) return model, history @torch.inference_mode() def inference_queries( model: base.SequentialTwoTower, prepared: SequenceData, config: Config, device: torch.device, ) -> torch.Tensor: sequence_rows = prepared.inference_user_rows lengths = np.where( sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0 ) ordered = np.argsort(lengths, kind="stable") result = torch.zeros( len(sequence_rows), config.embedding_dim, dtype=torch.float32 ) for group_start in range(0, len(ordered), config.user_batch_size): output_rows = ordered[group_start : group_start + config.user_batch_size] rows = sequence_rows[output_rows] group_lengths = lengths[output_rows] hidden = torch.zeros( 1, len(output_rows), config.embedding_dim, device=device ) for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps): steps = min( config.bptt_steps, int(group_lengths.max(initial=0)) - start, ) items = np.full( (len(output_rows), steps), len(prepared.item_ids), dtype=np.uint16, ) features = np.zeros((len(output_rows), steps, 3), dtype=np.float16) counts = np.zeros(len(output_rows), dtype=np.int32) for local_row, sequence_row in enumerate(rows): if sequence_row < 0: continue count = min(steps, max(int(group_lengths[local_row]) - start, 0)) if not count: continue offset = int(prepared.offsets[sequence_row]) selected = slice(offset + start, offset + start + count) items[local_row, :count] = prepared.sequence_items[selected] features[local_row, :count] = prepared.event_features[selected] counts[local_row] = count def main() -> int: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument( "--stage", choices=("validation", "submission"), default="validation" ) parser.add_argument("--output", type=Path, required=True) parser.add_argument("--public-dir", type=Path, required=True) parser.add_argument("--prepared-cache", type=Path) parser.add_argument("--device", choices=("cpu", "mps"), default="cpu") parser.add_argument("--threads", type=int, default=8) parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps) parser.add_argument( "--user-batch-size", type=int, default=Config.user_batch_size ) parser.add_argument("--epochs", type=int, default=Config.epochs) parser.add_argument("--learning-rate", type=float, default=Config.learning_rate) parser.add_argument( "--loss-microbatch-size", type=int, default=Config.loss_microbatch_size, ) parser.add_argument( "--negative-filter-scope", choices=("full-training-history", "last-100"), default=Config.negative_filter_scope, ) parser.add_argument( "--target-weighting", choices=("discovery-graded", "organic-strong", "uniform"), default=Config.target_weighting, ) parser.add_argument( "--ordinary-target-weight", type=float, default=Config.ordinary_target_weight, ) parser.add_argument( "--organic-strong-target-weight", type=float, default=Config.organic_strong_target_weight, ) parser.add_argument( "--organic-like-target-weight", type=float, default=Config.organic_like_target_weight, ) parser.add_argument( "--discovery-horizon-days", type=int, default=Config.discovery_horizon_days, ) parser.add_argument( "--validation-fraction", type=float, default=Config.validation_fraction, ) parser.add_argument( "--max-train-targets-per-user", type=int, default=Config.max_train_targets_per_user, help="retain this many recent pre-validation targets per user; 0 is uncapped", ) parser.add_argument( "--max-validation-targets-per-user", type=int, default=Config.max_validation_targets_per_user, help="retain this many final validation targets per user; 0 is uncapped", ) parser.add_argument( "--early-stopping-patience", type=int, default=Config.early_stopping_patience, ) parser.add_argument( "--early-stopping-min-delta", type=float, default=Config.early_stopping_min_delta, ) parser.add_argument("--checkpoint-dir", type=Path) parser.add_argument("--resume-checkpoint", type=Path) parser.add_argument("--seed", type=int, default=Config.seed) parser.add_argument("--ranking-batch-size", type=int, default=64) parser.add_argument("--prepare-only", action="store_true") args = parser.parse_args() if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1: parser.error("threads, BPTT steps, and user batch size must be positive") if ( args.epochs < 1 or args.learning_rate <= 0 or args.loss_microbatch_size < 1 or args.early_stopping_patience < 1 or args.early_stopping_min_delta < 0 or args.ordinary_target_weight <= 0 or args.organic_strong_target_weight <= 0 or args.organic_like_target_weight <= 0 or args.discovery_horizon_days < 1 or args.max_train_targets_per_user < 0 or args.max_validation_targets_per_user < 0 ): parser.error("training counts, learning rate, and patience are invalid") if not 0.0 < args.validation_fraction < 1.0: parser.error("validation fraction must be strictly between zero and one") public = args.public_dir.resolve() if not (public / "data" / "candidate_catalog.parquet").is_file(): parser.error(f"public task data not found under {public}") config = Config( bptt_steps=args.bptt_steps, user_batch_size=args.user_batch_size, epochs=args.epochs, learning_rate=args.learning_rate, loss_microbatch_size=args.loss_microbatch_size, negative_filter_scope=args.negative_filter_scope, target_weighting=args.target_weighting, ordinary_target_weight=args.ordinary_target_weight, organic_strong_target_weight=args.organic_strong_target_weight, organic_like_target_weight=args.organic_like_target_weight, discovery_horizon_days=args.discovery_horizon_days, validation_fraction=args.validation_fraction, max_train_targets_per_user=args.max_train_targets_per_user, max_validation_targets_per_user=args.max_validation_targets_per_user, early_stopping_patience=args.early_stopping_patience, early_stopping_min_delta=args.early_stopping_min_delta, seed=args.seed, ) torch.set_num_threads(args.threads) torch.set_num_interop_threads(1) seed_everything(config.seed) device = torch.device(args.device) if device.type == "mps" and not torch.backends.mps.is_available(): parser.error("MPS was requested but is unavailable") started = time.monotonic() prepared, eligibility = load_or_prepare( public, args.stage, config, args.prepared_cache.resolve() if args.prepared_cache else None, ) target_partition = chronological_target_partition( prepared, config.validation_fraction, config.max_train_targets_per_user, config.max_validation_targets_per_user, ) if args.prepare_only: print( json.dumps( { "stage": args.stage, "sequence_users": len(prepared.user_ids), "sequence_tokens": len(prepared.sequence_items), "next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int( target_partition.window_starts.sum() ), "target_users": len(eligibility.uids), "runtime_seconds": round(time.monotonic() - started, 3), "config": asdict(config), }, indent=2, ) ) return 0 model, training_history = train_model( public, args.stage, prepared, config, device, args.checkpoint_dir.resolve() if args.checkpoint_dir else None, args.resume_checkpoint.resolve() if args.resume_checkpoint else None, ) ranking, embedding_diagnostics = rank_candidates( model, prepared, eligibility, config, device, args.ranking_batch_size, ) write_ranking(ranking, args.output.resolve()) print( json.dumps( { "baseline": "yambda-windowed-sequence-two-tower-base", "stage": args.stage, "output": str(args.output.resolve()), "rows": len(ranking), "users": int(ranking.uid.nunique()), "runtime_seconds": round(time.monotonic() - started, 3), "public_inputs_only": True, "architecture": { "family": "pure-windowed-sequence-two-tower", "sequence_training": ( "every-adjacent-target-within-recent-chronological-window" ), "sequence_encoder": "one-layer-GRU-dense-timestep-loss", "bptt_hidden_state": "carried-and-detached", "model_selection": "chronological-validation-BCE-early-stopping", "loss_execution": "gradient-accumulated-microbatches", "user_id_embedding": False, "user_mlp": "582-512-256-192", "item_mlp": "196-512-256-192", "normalization": "none", "score": "raw-dot-product", "objective": "sample-weighted-BCE-at-every-timestep", "negative_sampling": ( "24-uniform-plus-8-propensity-corrected-inbatch" ), "false_negative_filter": config.negative_filter_scope, "target_weighting": config.target_weighting, }, "config": asdict(config), "sequence_tokens": len(prepared.sequence_items), "all_next_item_targets": prepared.target_count, "selected_training_targets": int( target_partition.training_counts.sum() ), "selected_validation_targets": int( target_partition.validation_counts.sum() ), "discarded_old_targets": int(target_partition.window_starts.sum()), "training_history": training_history, "embedding_diagnostics": embedding_diagnostics, "torch_version": torch.__version__, "device": str(device), }, indent=2, ) ) return 0 ## Step 36 · agent · 2026-08-31T19:09:22.439Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-08-31T19:09:41.082Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-08-31T19:09:59.724Z ### Thinking unavailable — no text recorded ## Step 39 · agent · 2026-08-31T19:10:11.281Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:42.042Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n uniform_negatives: int = 24\\n inbatch_negatives: int = 8\\n+ # Draw the production 32 negatives plus extra popularity-skewed candidates,\\n+ # then backpropagate only through the highest-scoring 32. A value of 32\\n+ # exactly reproduces the production sampler.\\n+ hard_negative_pool_size: int = 48\\n@@\\n def draw_full_history_filtered_negatives(\\n@@\\n- uniform = torch.randint(\\n+ retained_count = config.uniform_negatives + config.inbatch_negatives\\n+ pool_count = max(retained_count, config.hard_negative_pool_size)\\n+ extra_count = pool_count - retained_count\\n+ uniform = torch.randint(\\n n_items,\\n (batch_size, config.uniform_negatives),\\n@@\\n- negatives = torch.cat([uniform, inbatch], dim=1).numpy()\\n+ # The clipped inverse propensity correction is already available on CPU.\\n+ # Its reciprocal is a stable, deliberately flattened popularity proposal:\\n+ # it concentrates the added pool on competitive head items without letting\\n+ # a handful of tracks consume the entire negative budget.\\n+ extra = torch.empty((batch_size, 0), dtype=torch.long)\\n+ if extra_count:\\n+ proposal = item_correction.detach().cpu().clamp_min(1.0e-6).reciprocal()\\n+ extra = torch.multinomial(\\n+ proposal,\\n+ batch_size * extra_count,\\n+ replacement=True,\\n+ generator=generator,\\n+ ).reshape(batch_size, extra_count)\\n+\\n+ negatives = torch.cat([uniform, extra, inbatch], dim=1).numpy()\\n correction = np.ones_like(negatives, dtype=np.float32)\\n- if corrected_count:\\n+ if corrected_count and not extra_count:\\n correction[\\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\\n@@\\n def score_target_microbatches(\\n@@\\n- negative_logits = torch.einsum(\\n+ negative_logits = torch.einsum(\\n \\\"bd,bkd->bk\\\", query_vectors, negative_vectors\\n )\\n+ selected_negative_weights = negative_weights[begin:end]\\n+ retained_count = config.uniform_negatives + config.inbatch_negatives\\n+ if negative_logits.shape[1] > retained_count:\\n+ # Stop-gradient selection is intentional: the selected logits still\\n+ # carry gradients through both towers, while easy pool members do\\n+ # not dilute a loss whose serving boundary is top-of-list retrieval.\\n+ hard_columns = torch.topk(\\n+ negative_logits.detach(), k=retained_count, dim=1\\n+ ).indices\\n+ negative_logits = negative_logits.gather(1, hard_columns)\\n+ selected_negative_weights = selected_negative_weights.gather(\\n+ 1, hard_columns\\n+ )\\n positive_loss = F.softplus(-positive_logits)\\n- negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n- )\\n+ if negatives.shape[1] > retained_count:\\n+ # The hard pool mixes proposal distributions and is purposefully a\\n+ # ranking-oriented objective, so propensity correction is not valid\\n+ # after top-k selection. Each retained confusion receives equal mass.\\n+ negative_loss = F.softplus(negative_logits).mean(dim=1)\\n+ else:\\n+ negative_loss = corrected_negative_loss(\\n+ negative_logits, selected_negative_weights, config\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:10:42.103Z 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 40 · agent · 2026-08-31T19:10:44.830Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:10:47.172Z { "code": "const r = await tools.exec_command({cmd:\"ls -l /app/solution && chmod u+w /app/solution/train_current_model.py /app/solution/two_tower_model.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:2000}); text(r.output);" } ### Tool output · 2026-08-31T19:10:47.253Z Script completed Wall time 0.0 seconds Output: total 1312 -rw-r--r-- 1 agent agent 1244231 Aug 31 19:08 production_validation.parquet -r--r--r-- 1 agent agent 80058 Aug 31 19:02 train_current_model.py -r--r--r-- 1 agent agent 15985 Aug 31 19:02 two_tower_model.py ## Step 41 · agent · 2026-08-31T19:10:49.240Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:19.328Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n uniform_negatives: int = 24\\n inbatch_negatives: int = 8\\n+ # Draw the production 32 negatives plus extra popularity-skewed candidates,\\n+ # then backpropagate only through the highest-scoring 32. A value of 32\\n+ # exactly reproduces the production sampler.\\n+ hard_negative_pool_size: int = 48\\n@@\\n def draw_full_history_filtered_negatives(\\n@@\\n- uniform = torch.randint(\\n+ retained_count = config.uniform_negatives + config.inbatch_negatives\\n+ pool_count = max(retained_count, config.hard_negative_pool_size)\\n+ extra_count = pool_count - retained_count\\n+ uniform = torch.randint(\\n n_items,\\n (batch_size, config.uniform_negatives),\\n@@\\n- negatives = torch.cat([uniform, inbatch], dim=1).numpy()\\n+ # The clipped inverse propensity correction is already available on CPU.\\n+ # Its reciprocal is a stable, deliberately flattened popularity proposal:\\n+ # it concentrates the added pool on competitive head items without letting\\n+ # a handful of tracks consume the entire negative budget.\\n+ extra = torch.empty((batch_size, 0), dtype=torch.long)\\n+ if extra_count:\\n+ proposal = item_correction.detach().cpu().clamp_min(1.0e-6).reciprocal()\\n+ extra = torch.multinomial(\\n+ proposal,\\n+ batch_size * extra_count,\\n+ replacement=True,\\n+ generator=generator,\\n+ ).reshape(batch_size, extra_count)\\n+\\n+ negatives = torch.cat([uniform, extra, inbatch], dim=1).numpy()\\n correction = np.ones_like(negatives, dtype=np.float32)\\n- if corrected_count:\\n+ if corrected_count and not extra_count:\\n correction[\\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\\n@@\\n- negative_logits = torch.einsum(\\n+ negative_logits = torch.einsum(\\n \\\"bd,bkd->bk\\\", query_vectors, negative_vectors\\n )\\n+ selected_negative_weights = negative_weights[begin:end]\\n+ retained_count = config.uniform_negatives + config.inbatch_negatives\\n+ if negative_logits.shape[1] > retained_count:\\n+ # Stop-gradient selection is intentional: selected logits still\\n+ # carry gradients through both towers, while easy pool members do\\n+ # not dilute a loss aimed at top-of-list retrieval.\\n+ hard_columns = torch.topk(\\n+ negative_logits.detach(), k=retained_count, dim=1\\n+ ).indices\\n+ negative_logits = negative_logits.gather(1, hard_columns)\\n+ selected_negative_weights = selected_negative_weights.gather(\\n+ 1, hard_columns\\n+ )\\n positive_loss = F.softplus(-positive_logits)\\n- negative_loss = corrected_negative_loss(\\n- negative_logits, negative_weights[begin:end], config\\n- )\\n+ if negatives.shape[1] > retained_count:\\n+ # Top-k selection changes the proposal distribution, so the\\n+ # production in-batch propensity correction is no longer valid.\\n+ # Equal mass on retained confusions is the ranking objective.\\n+ negative_loss = F.softplus(negative_logits).mean(dim=1)\\n+ else:\\n+ negative_loss = corrected_negative_loss(\\n+ negative_logits, selected_negative_weights, config\\n+ )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:11:19.372Z Script completed Wall time 0.0 seconds Output: {} ## Step 42 · agent · 2026-08-31T19:11:25.210Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:34.754Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--loss-microbatch-size\\\",\\n type=int,\\n default=Config.loss_microbatch_size,\\n )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-pool-size\\\",\\n+ type=int,\\n+ default=Config.hard_negative_pool_size,\\n+ help=\\\"sample this many candidates and retain the hardest 32; 32 disables mining\\\",\\n+ )\\n@@\\n or args.learning_rate <= 0\\n or args.loss_microbatch_size < 1\\n+ or args.hard_negative_pool_size\\n+ < Config.uniform_negatives + Config.inbatch_negatives\\n@@\\n learning_rate=args.learning_rate,\\n loss_microbatch_size=args.loss_microbatch_size,\\n+ hard_negative_pool_size=args.hard_negative_pool_size,\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n+ \\\"sampled-hard-pool-with-full-history-filter\\\"\\n ),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:11:34.801Z Script completed Wall time 0.0 seconds Output: {} ## Step 43 · agent · 2026-08-31T19:11:38.504Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:11:43.172Z { "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\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/hard48_checkpoints --output /app/solution/hard48_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:11:54.198Z Script running with cell ID 23 Wall time 11.0 seconds Output: ## Step 44 · agent · 2026-08-31T19:11:56.684Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:11:57.458Z { "cell_id": "23", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:12:13.359Z Script completed Wall time 15.9 seconds Output: {"chunk_id":"0d035a","wall_time_seconds":30.00175434,"session_id":32809,"original_token_count":79,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s\n"} ## Step 45 · agent · 2026-08-31T19:12:14.922Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:12:16.306Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:12:27.323Z Script running with cell ID 24 Wall time 11.0 seconds Output: ## Step 46 · agent · 2026-08-31T19:12:28.808Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:12:29.589Z { "cell_id": "24", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:12:46.327Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"8fb073","wall_time_seconds":30.001364928,"session_id":32809,"original_token_count":232,"output":"prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 47 · agent · 2026-08-31T19:12:56.801Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:12:58.181Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:13:09.221Z Script running with cell ID 25 Wall time 11.0 seconds Output: ## Step 48 · agent · 2026-08-31T19:13:11.167Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:13:11.945Z { "cell_id": "25", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:13:28.227Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"e7bdc1","wall_time_seconds":30.001154015,"session_id":32809,"original_token_count":0,"output":""} ## Step 49 · agent · 2026-08-31T19:13:29.813Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:13:31.163Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:13:42.184Z Script running with cell ID 26 Wall time 11.0 seconds Output: ## Step 50 · agent · 2026-08-31T19:13:43.818Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:13:44.596Z { "cell_id": "26", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:14:01.188Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"00aad3","wall_time_seconds":30.00101027,"session_id":32809,"original_token_count":0,"output":""} ## Step 51 · agent · 2026-08-31T19:14:02.709Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:14:04.079Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:14:15.121Z Script running with cell ID 27 Wall time 11.0 seconds Output: ## Step 52 · agent · 2026-08-31T19:14:16.629Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:14:17.413Z { "cell_id": "27", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:14:34.122Z Script completed Wall time 16.7 seconds Output: {"chunk_id":"c2ef1f","wall_time_seconds":30.000477147,"session_id":32809,"original_token_count":0,"output":""} ## Step 53 · agent · 2026-08-31T19:14:35.668Z ### Thinking unavailable — no text recorded The hard-pool variant is training cleanly with the same chronological split and full-history exclusion. Its extra 16 pool candidates are popularity-skewed, while the original 24 uniform and 8 in-batch candidates remain; only the top-scoring 32 of 48 contribute loss. This keeps the per-example gradient budget fixed and adds roughly 50% scoring work rather than a full-catalog mining pass. ### exec · 2026-08-31T19:14:40.479Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:14:51.500Z Script running with cell ID 28 Wall time 11.0 seconds Output: ## Step 54 · agent · 2026-08-31T19:14:53.316Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:14:54.121Z { "cell_id": "28", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:15:10.516Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"7f2749","wall_time_seconds":30.001228637,"session_id":32809,"original_token_count":0,"output":""} ## Step 55 · agent · 2026-08-31T19:15:12.012Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:15:13.405Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:15:24.422Z Script running with cell ID 29 Wall time 11.0 seconds Output: ## Step 56 · agent · 2026-08-31T19:15:26.166Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:15:26.955Z { "cell_id": "29", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:15:43.442Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"7dc8d4","wall_time_seconds":30.00195128,"session_id":32809,"original_token_count":0,"output":""} ## Step 57 · agent · 2026-08-31T19:15:45.306Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:15:46.659Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:15:57.682Z Script running with cell ID 30 Wall time 11.0 seconds Output: ## Step 58 · agent · 2026-08-31T19:16:00.311Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:16:01.143Z { "cell_id": "30", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:16:16.689Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"086ae1","wall_time_seconds":30.000921178,"session_id":32809,"original_token_count":0,"output":""} ## Step 59 · agent · 2026-08-31T19:16:19.081Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:16:21.285Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:16:32.335Z Script running with cell ID 31 Wall time 11.0 seconds Output: ## Step 60 · agent · 2026-08-31T19:16:34.057Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:16:34.837Z { "cell_id": "31", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:16:51.345Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"179d03","wall_time_seconds":30.000548437,"session_id":32809,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.649828 running_bce=0.632990 elapsed=230.3s\n"} ## Step 61 · agent · 2026-08-31T19:16:54.132Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:16:55.530Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:17:06.566Z Script running with cell ID 32 Wall time 11.0 seconds Output: ## Step 62 · agent · 2026-08-31T19:17:08.244Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:17:09.053Z { "cell_id": "32", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:17:25.575Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"7edf3a","wall_time_seconds":30.001458772,"session_id":32809,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/hard48_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.63205 validation_loss=0.60409 pair_accuracy=0.609 train_targets=1,052,953 validation_targets=130,103 train_seconds=245.6 validation_seconds=20.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604093\n"} ## Step 63 · agent · 2026-08-31T19:17:27.104Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:17:28.492Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:17:39.531Z Script running with cell ID 33 Wall time 11.0 seconds Output: ## Step 64 · agent · 2026-08-31T19:17:41.656Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:17:42.515Z { "cell_id": "33", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:17:58.536Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"7711bc","wall_time_seconds":30.001882205,"session_id":32809,"original_token_count":0,"output":""} ## Step 65 · agent · 2026-08-31T19:18:00.129Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:18:01.531Z { "code": "const r = await tools.write_stdin({session_id:32809,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:18:12.555Z Script running with cell ID 34 Wall time 11.0 seconds Output: ## Step 66 · agent · 2026-08-31T19:18:14.377Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:18:15.149Z { "cell_id": "34", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:18:29.213Z Script completed Wall time 14.1 seconds Output: {"chunk_id":"7126ec","wall_time_seconds":27.649319675,"exit_code":0,"original_token_count":809,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/hard48_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 401.579,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"sampled-hard-pool-with-full-history-filter\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"hard_negative_pool_size\": 48,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.6320480839785293,\n \"positive_logit\": 0.2749283446064721,\n \"negative_logit\": -0.21619851451581898,\n \"pair_accuracy\": 0.6088086612127986,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 245.624033005,\n \"validation_loss\": 0.6040925726377234,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 20.57991937099996\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.5106288194656372,\n \"query_norm_std\": 1.0394723415374756,\n \"item_norm_mean\": 1.2617090940475464,\n \"item_norm_std\": 0.25714924931526184\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 67 · agent · 2026-08-31T19:18:30.702Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:18:32.907Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/hard48_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:18:36.290Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"e3d8b5","wall_time_seconds":3.203490952,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/hard48_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019374,\n \"quality_users\": 4263\n }\n}\n"} ## Step 68 · agent · 2026-08-31T19:18:55.928Z ### Thinking unavailable — no text recorded ## Step 69 · agent · 2026-08-31T19:19:00.628Z ### Thinking unavailable — no text recorded The first hard-mining attempt scored **0.019374**, below the 0.02128 baseline, so it is rejected and the production ranking remains the saved best. The diagnostic is useful: replacing the entire negative loss with mined examples over-concentrated training and reduced item-vector norm diversity. I’m adjusting to a mixed objective—retain the full production loss for coverage and add a smaller hardest-confusion term—then I’ll backtest that isolated change. ### exec · 2026-08-31T19:19:34.218Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n hard_negative_pool_size: int = 48\\n+ hard_negative_count: int = 8\\n+ hard_negative_weight: float = 0.25\\n@@\\n- if corrected_count and not extra_count:\\n- correction[\\n- :, config.uniform_negatives : config.uniform_negatives + corrected_count\\n- ] = item_correction[inbatch[:, :corrected_count]].numpy()\\n+ if corrected_count:\\n+ inbatch_start = config.uniform_negatives + extra_count\\n+ correction[:, inbatch_start : inbatch_start + corrected_count] = (\\n+ item_correction[inbatch[:, :corrected_count]].numpy()\\n+ )\\n@@\\n- selected_negative_weights = negative_weights[begin:end]\\n+ selected_negative_weights = negative_weights[begin:end]\\n retained_count = config.uniform_negatives + config.inbatch_negatives\\n if negative_logits.shape[1] > retained_count:\\n- # Stop-gradient selection is intentional: selected logits still\\n- # carry gradients through both towers, while easy pool members do\\n- # not dilute a loss aimed at top-of-list retrieval.\\n- hard_columns = torch.topk(\\n- negative_logits.detach(), k=retained_count, dim=1\\n+ # Preserve the production proposal loss for broad catalog coverage,\\n+ # then add a bounded mined term. This avoids the representation\\n+ # collapse observed when all easy negatives were discarded.\\n+ extra_count = negative_logits.shape[1] - retained_count\\n+ base_logits = torch.cat(\\n+ [\\n+ negative_logits[:, : config.uniform_negatives],\\n+ negative_logits[:, config.uniform_negatives + extra_count :],\\n+ ],\\n+ dim=1,\\n+ )\\n+ base_weights = torch.cat(\\n+ [\\n+ selected_negative_weights[:, : config.uniform_negatives],\\n+ selected_negative_weights[\\n+ :, config.uniform_negatives + extra_count :\\n+ ],\\n+ ],\\n+ dim=1,\\n+ )\\n+ hard_columns = torch.topk(\\n+ negative_logits.detach(), k=config.hard_negative_count, dim=1\\n ).indices\\n- negative_logits = negative_logits.gather(1, hard_columns)\\n- selected_negative_weights = selected_negative_weights.gather(\\n- 1, hard_columns\\n+ hard_logits = negative_logits.gather(1, hard_columns)\\n+ base_loss = corrected_negative_loss(base_logits, base_weights, config)\\n+ hard_loss = F.softplus(hard_logits).mean(dim=1)\\n+ negative_loss = (\\n+ (1.0 - config.hard_negative_weight) * base_loss\\n+ + config.hard_negative_weight * hard_loss\\n )\\n+ # Diagnostics retain the production 32-negative denominator.\\n+ negative_logits = base_logits\\n+ else:\\n+ negative_loss = corrected_negative_loss(\\n+ negative_logits, selected_negative_weights, config\\n+ )\\n positive_loss = F.softplus(-positive_logits)\\n- if negatives.shape[1] > retained_count:\\n- # Top-k selection changes the proposal distribution, so the\\n- # production in-batch propensity correction is no longer valid.\\n- # Equal mass on retained confusions is the ranking objective.\\n- negative_loss = F.softplus(negative_logits).mean(dim=1)\\n- else:\\n- negative_loss = corrected_negative_loss(\\n- negative_logits, selected_negative_weights, config\\n- )\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:19:34.296Z Script completed Wall time 0.0 seconds Output: {} ## Step 70 · agent · 2026-08-31T19:19:37.059Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:19:45.535Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n parser.add_argument(\\n \\\"--hard-negative-pool-size\\\",\\n@@\\n )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-count\\\", type=int, default=Config.hard_negative_count\\n+ )\\n+ parser.add_argument(\\n+ \\\"--hard-negative-weight\\\", type=float, default=Config.hard_negative_weight\\n+ )\\n@@\\n or args.hard_negative_pool_size\\n < Config.uniform_negatives + Config.inbatch_negatives\\n+ or args.hard_negative_count < 1\\n+ or args.hard_negative_count > args.hard_negative_pool_size\\n+ or not 0.0 <= args.hard_negative_weight <= 1.0\\n@@\\n hard_negative_pool_size=args.hard_negative_pool_size,\\n+ hard_negative_count=args.hard_negative_count,\\n+ hard_negative_weight=args.hard_negative_weight,\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:19:45.626Z Script completed Wall time 0.0 seconds Output: {} ## Step 71 · agent · 2026-08-31T19:19:49.417Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:19:53.673Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/blended_checkpoints --output /app/solution/blended_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:20:04.699Z Script running with cell ID 38 Wall time 11.0 seconds Output: ## Step 72 · agent · 2026-08-31T19:20:08.329Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:20:09.157Z { "cell_id": "38", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:20:23.862Z Script completed Wall time 14.7 seconds Output: {"chunk_id":"3571ad","wall_time_seconds":30.001729939,"session_id":27972,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 73 · agent · 2026-08-31T19:20:30.591Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:20:39.510Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport numpy as np, pandas as pd\\nz=np.load('/task/data/validation_eligible_candidates.npz')\\nuids=z['uids']; items=z['item_ids']; packed=z['packed_eligible']\\nrows=[]\\nfor r,uid in enumerate(uids):\\n mask=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n # candidate_catalog and NPZ share catalog ordering (asserted separately below)\\n cols=np.flatnonzero(mask)[:100]\\n rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(cols,1))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('popularity_diagnostic.parquet',index=False)\\nPY\\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/popularity_diagnostic.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:20:47.723Z Script completed Wall time 8.2 seconds Output: {"chunk_id":"f4bfd3","wall_time_seconds":8.003511864,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\n"} ## Step 74 · agent · 2026-08-31T19:20:50.256Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:20:51.666Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:21:02.684Z Script running with cell ID 40 Wall time 11.0 seconds Output: ## Step 75 · agent · 2026-08-31T19:21:04.437Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:21:05.197Z { "cell_id": "40", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:21:21.689Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"f62855","wall_time_seconds":30.001052335,"session_id":27972,"original_token_count":0,"output":""} ## Step 76 · agent · 2026-08-31T19:21:23.097Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:21:24.508Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:21:35.543Z Script running with cell ID 41 Wall time 11.0 seconds Output: ## Step 77 · agent · 2026-08-31T19:21:37.149Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:21:37.912Z { "cell_id": "41", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:21:54.562Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"8773b6","wall_time_seconds":30.00121818,"session_id":27972,"original_token_count":0,"output":""} ## Step 78 · agent · 2026-08-31T19:21:56.306Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:21:57.680Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:22:08.702Z Script running with cell ID 42 Wall time 11.0 seconds Output: ## Step 79 · agent · 2026-08-31T19:22:10.296Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:22:11.070Z { "cell_id": "42", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:22:27.704Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"b29a35","wall_time_seconds":30.001268725,"session_id":27972,"original_token_count":0,"output":""} ## Step 80 · agent · 2026-08-31T19:22:29.529Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:22:30.901Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:22:41.934Z Script running with cell ID 43 Wall time 11.0 seconds Output: ## Step 81 · agent · 2026-08-31T19:22:43.696Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:22:44.470Z { "cell_id": "43", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:23:00.946Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"e42b0f","wall_time_seconds":30.001806786,"session_id":27972,"original_token_count":0,"output":""} ## Step 82 · agent · 2026-08-31T19:23:03.078Z ### Thinking unavailable — no text recorded A global-popularity diagnostic scores only **0.01071**, confirming that head bias alone cannot explain or solve the gap; the sequential user signal is carrying most of the baseline quality. The mixed hard-negative run is still within the expected CPU time, and the cached data now removes about 50 seconds from each validation iteration. ### exec · 2026-08-31T19:23:07.251Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:23:18.307Z Script running with cell ID 44 Wall time 11.0 seconds Output: ## Step 83 · agent · 2026-08-31T19:23:20.184Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:23:20.967Z { "cell_id": "44", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:23:37.319Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"21aa29","wall_time_seconds":30.001979474,"session_id":27972,"original_token_count":0,"output":""} ## Step 84 · agent · 2026-08-31T19:23:38.936Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:23:40.324Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:23:51.373Z Script running with cell ID 45 Wall time 11.0 seconds Output: ## Step 85 · agent · 2026-08-31T19:23:53.306Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:23:54.076Z { "cell_id": "45", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:24:10.390Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"c34f7d","wall_time_seconds":30.001910921,"session_id":27972,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.623926 running_bce=0.620101 elapsed=234.2s\n"} ## Step 86 · agent · 2026-08-31T19:24:12.235Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:24:13.657Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:24:24.678Z Script running with cell ID 46 Wall time 11.0 seconds Output: ## Step 87 · agent · 2026-08-31T19:24:26.374Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:24:27.188Z { "cell_id": "46", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:24:43.688Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"ffa0bd","wall_time_seconds":30.001943572,"session_id":27972,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/blended_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.61885 validation_loss=0.58598 pair_accuracy=0.754 train_targets=1,052,953 validation_targets=130,103 train_seconds=249.5 validation_seconds=20.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.585976\n"} ## Step 88 · agent · 2026-08-31T19:24:45.658Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:24:47.028Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:24:58.059Z Script running with cell ID 47 Wall time 11.0 seconds Output: ## Step 89 · agent · 2026-08-31T19:24:59.785Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:25:00.625Z { "cell_id": "47", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:25:17.070Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"26f36d","wall_time_seconds":30.001921575,"session_id":27972,"original_token_count":0,"output":""} ## Step 90 · agent · 2026-08-31T19:25:19.916Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:21.153Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:25:32.175Z Script running with cell ID 48 Wall time 11.0 seconds Output: ## Step 91 · agent · 2026-08-31T19:25:33.921Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:25:34.777Z { "cell_id": "48", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:25:51.178Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"369477","wall_time_seconds":30.000480142,"session_id":27972,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"} ## Step 92 · agent · 2026-08-31T19:25:53.106Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:54.497Z { "code": "const r = await tools.write_stdin({session_id:27972,chars:\"\",yield_time_ms:10000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:25:54.582Z Script completed Wall time 0.0 seconds Output: {"chunk_id":"9950cd","wall_time_seconds":0.00000232,"exit_code":0,"original_token_count":770,"output":"{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/blended_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 354.035,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"sampled-hard-pool-with-full-history-filter\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"hard_negative_pool_size\": 48,\n \"hard_negative_count\": 8,\n \"hard_negative_weight\": 0.25,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.6188478072126893,\n \"positive_logit\": 0.29570962463799333,\n \"negative_logit\": -0.5569627711488554,\n \"pair_accuracy\": 0.7536614288576983,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 249.50920362500005,\n \"validation_loss\": 0.5859764186777909,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 20.203965118000042\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.195103406906128,\n \"query_norm_std\": 0.6614366769790649,\n \"item_norm_mean\": 1.912940263748169,\n \"item_norm_std\": 1.0607719421386719\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 93 · agent · 2026-08-31T19:25:57.137Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:25:59.390Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/blended_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:26:02.789Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"d4f857","wall_time_seconds":3.221938694,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/blended_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01577,\n \"quality_users\": 4263\n }\n}\n"} ## Step 94 · agent · 2026-08-31T19:26:21.810Z ### Thinking unavailable — no text recorded The blended mined term also regressed (**0.01577**), so both hard-mining formulations are rejected. The failure is consistent across them: a one-epoch CPU budget does not let the towers re-equilibrate after aggressively changing negative difficulty. I’m switching to a lower-variance diagnosis: the baseline’s inverse-propensity correction explicitly downweights popular in-batch confusions, making them behave more like uniform negatives. The next isolated test keeps 32 examples and ordinary BCE, but uses a 16/16 uniform/in-batch mix without that de-hardening correction. ### exec · 2026-08-31T19:26:35.170Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- uniform_negatives: int = 24\\n- inbatch_negatives: int = 8\\n+ uniform_negatives: int = 16\\n+ inbatch_negatives: int = 16\\n@@\\n- hard_negative_pool_size: int = 48\\n+ hard_negative_pool_size: int = 32\\n@@\\n- inbatch_correction_power: float = 0.5\\n+ inbatch_correction_power: float = 0.0\\n@@\\n- or args.hard_negative_pool_size\\n- < Config.uniform_negatives + Config.inbatch_negatives\\n+ or args.hard_negative_pool_size\\n+ < Config.uniform_negatives + Config.inbatch_negatives\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"sampled-hard-pool-with-full-history-filter\\\"\\n+ \\\"16-uniform-plus-16-uncorrected-inbatch\\\"\\n ),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:26:35.255Z Script completed Wall time 0.0 seconds Output: {} ## Step 95 · agent · 2026-08-31T19:26:38.355Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:26:42.681Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/inbatch16_checkpoints --output /app/solution/inbatch16_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:26:53.704Z Script running with cell ID 52 Wall time 11.0 seconds Output: ## Step 96 · agent · 2026-08-31T19:26:55.413Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:26:56.216Z { "cell_id": "52", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:27:12.866Z Script completed Wall time 16.6 seconds Output: {"chunk_id":"262672","wall_time_seconds":30.000404581,"session_id":7731,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 97 · agent · 2026-08-31T19:27:24.437Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:27:26.260Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:27:37.281Z Script running with cell ID 53 Wall time 11.0 seconds Output: ## Step 98 · agent · 2026-08-31T19:27:39.532Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:27:40.325Z { "cell_id": "53", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:27:56.292Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"94c3f6","wall_time_seconds":30.001549668,"session_id":7731,"original_token_count":0,"output":""} ## Step 99 · agent · 2026-08-31T19:27:57.761Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:27:59.124Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:28:10.177Z Script running with cell ID 54 Wall time 11.0 seconds Output: ## Step 100 · agent · 2026-08-31T19:28:11.974Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:28:12.785Z { "cell_id": "54", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:28:29.180Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"7ccd75","wall_time_seconds":30.001279986,"session_id":7731,"original_token_count":0,"output":""} ## Step 101 · agent · 2026-08-31T19:28:30.896Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:28:32.152Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:28:43.170Z Script running with cell ID 55 Wall time 11.0 seconds Output: ## Step 102 · agent · 2026-08-31T19:28:45.105Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:28:45.896Z { "cell_id": "55", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:29:02.184Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"b382c5","wall_time_seconds":30.001452541,"session_id":7731,"original_token_count":0,"output":""} ## Step 103 · agent · 2026-08-31T19:29:04.837Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:06.249Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:29:17.271Z Script running with cell ID 56 Wall time 11.0 seconds Output: ## Step 104 · agent · 2026-08-31T19:29:18.987Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:29:19.788Z { "cell_id": "56", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:29:36.273Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"860edd","wall_time_seconds":30.000538459,"session_id":7731,"original_token_count":0,"output":""} ## Step 105 · agent · 2026-08-31T19:29:37.881Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:29:39.301Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:29:50.336Z Script running with cell ID 57 Wall time 11.0 seconds Output: ## Step 106 · agent · 2026-08-31T19:29:52.217Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:29:53.026Z { "cell_id": "57", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:30:09.343Z Script completed Wall time 16.3 seconds Output: {"chunk_id":"14f562","wall_time_seconds":30.001055745,"session_id":7731,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.564065 running_bce=0.586965 elapsed=190.3s\n"} ## Step 107 · agent · 2026-08-31T19:30:11.041Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:13.190Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:30:24.207Z Script running with cell ID 58 Wall time 11.0 seconds Output: ## Step 108 · agent · 2026-08-31T19:30:26.063Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:30:26.841Z { "cell_id": "58", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:30:43.217Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"351bbd","wall_time_seconds":30.001543581,"session_id":7731,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/inbatch16_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.58454 validation_loss=0.54140 pair_accuracy=0.728 train_targets=1,052,953 validation_targets=130,103 train_seconds=203.0 validation_seconds=14.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.541397\n"} ## Step 109 · agent · 2026-08-31T19:30:44.856Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:30:46.224Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:30:57.260Z Script running with cell ID 59 Wall time 11.0 seconds Output: ## Step 110 · agent · 2026-08-31T19:31:00.463Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:31:01.250Z { "cell_id": "59", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:31:16.266Z Script completed Wall time 15.0 seconds Output: {"chunk_id":"c8d368","wall_time_seconds":30.001639457,"session_id":7731,"original_token_count":0,"output":""} ## Step 111 · agent · 2026-08-31T19:31:17.819Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:19.205Z { "code": "const r = await tools.write_stdin({session_id:7731,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:31:30.226Z Script running with cell ID 60 Wall time 11.0 seconds Output: ## Step 112 · agent · 2026-08-31T19:31:32.171Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:31:32.964Z { "cell_id": "60", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:31:48.579Z Script completed Wall time 15.6 seconds Output: {"chunk_id":"9f96a0","wall_time_seconds":29.342123552,"exit_code":0,"original_token_count":824,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/inbatch16_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 301.348,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"16-uniform-plus-16-uncorrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 16,\n \"inbatch_negatives\": 16,\n \"hard_negative_pool_size\": 32,\n \"hard_negative_count\": 8,\n \"hard_negative_weight\": 0.25,\n \"inbatch_correction_power\": 0.0,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"organic-strong\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5845398030558726,\n \"positive_logit\": 0.4497601119500438,\n \"negative_logit\": -0.514334340422768,\n \"pair_accuracy\": 0.7281032783514554,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 632551.0,\n \"batches\": 266.0,\n \"seconds\": 202.991531957,\n \"validation_loss\": 0.54139749728081,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 78769.25,\n \"validation_seconds\": 14.09756859700019\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.5723634958267212,\n \"query_norm_std\": 0.8727400898933411,\n \"item_norm_mean\": 1.7504078149795532,\n \"item_norm_std\": 0.7237846851348877\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 113 · agent · 2026-08-31T19:31:50.177Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:31:53.037Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/inbatch16_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:31:56.384Z Script completed Wall time 3.3 seconds Output: {"chunk_id":"9cc654","wall_time_seconds":3.172107859,"exit_code":0,"original_token_count":35,"output":"{\n \"model\": \"/app/solution/inbatch16_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014744,\n \"quality_users\": 4263\n }\n}\n"} ## Step 114 · agent · 2026-08-31T19:32:16.573Z ### Thinking unavailable — no text recorded ## Step 115 · agent · 2026-08-31T19:32:20.450Z ### Thinking unavailable — no text recorded The 16/16 uncorrected mix regressed further (**0.014744**). Together, the tests show that harder/popularity-skewed negatives are mostly false assumptions about the *eligible discovery* distribution: candidate eligibility has already removed prior encounters, and the inverse-propensity correction is important for catalog coverage. I’m restoring the validated 24/8 corrected sampler and testing the objective’s other documented mismatch—grade-3 organic-like outcomes are currently weighted exactly like grade-1 strong listens—using only cutoff-safe outcomes inside the training prefix. ### exec · 2026-08-31T19:32:32.033Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/train_current_model.py\\n@@\\n- uniform_negatives: int = 16\\n- inbatch_negatives: int = 16\\n+ uniform_negatives: int = 24\\n+ inbatch_negatives: int = 8\\n@@\\n- inbatch_correction_power: float = 0.0\\n+ inbatch_correction_power: float = 0.5\\n@@\\n- target_weighting: str = \\\"organic-strong\\\"\\n+ target_weighting: str = \\\"discovery-graded\\\"\\n@@\\n \\\"negative_sampling\\\": (\\n- \\\"16-uniform-plus-16-uncorrected-inbatch\\\"\\n+ \\\"24-uniform-plus-8-propensity-corrected-inbatch\\\"\\n ),\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:32:32.059Z Script completed Wall time 0.0 seconds Output: {} ## Step 116 · agent · 2026-08-31T19:32:34.230Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:32:38.533Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -m py_compile /app/solution/train_current_model.py\\n/opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/validation_prepared.npz --checkpoint-dir /app/solution/graded_checkpoints --output /app/solution/graded_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:32:49.552Z Script running with cell ID 63 Wall time 11.0 seconds Output: ## Step 117 · agent · 2026-08-31T19:32:51.352Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:32:52.206Z { "cell_id": "63", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:33:08.726Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"de5190","wall_time_seconds":30.001676182,"session_id":35704,"original_token_count":131,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"} ## Step 118 · agent · 2026-08-31T19:33:12.041Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:13.511Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:33:24.560Z Script running with cell ID 64 Wall time 11.0 seconds Output: ## Step 119 · agent · 2026-08-31T19:33:28.093Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:33:28.893Z { "cell_id": "64", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:33:43.564Z Script completed Wall time 14.7 seconds Output: {"chunk_id":"6d987d","wall_time_seconds":30.001470629,"session_id":35704,"original_token_count":0,"output":""} ## Step 120 · agent · 2026-08-31T19:33:45.952Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:33:47.355Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:33:58.389Z Script running with cell ID 65 Wall time 11.0 seconds Output: ## Step 121 · agent · 2026-08-31T19:34:00.555Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:34:01.362Z { "cell_id": "65", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:34:17.413Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"f8b529","wall_time_seconds":30.003785754,"session_id":35704,"original_token_count":0,"output":""} ## Step 122 · agent · 2026-08-31T19:34:19.411Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:34:20.814Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:34:31.864Z Script running with cell ID 66 Wall time 11.0 seconds Output: ## Step 123 · agent · 2026-08-31T19:34:34.760Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:34:35.548Z { "cell_id": "66", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:34:50.868Z Script completed Wall time 15.3 seconds Output: {"chunk_id":"7b78f5","wall_time_seconds":30.001353778,"session_id":35704,"original_token_count":0,"output":""} ## Step 124 · agent · 2026-08-31T19:34:54.025Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:34:55.424Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:35:06.477Z Script running with cell ID 67 Wall time 11.0 seconds Output: ## Step 125 · agent · 2026-08-31T19:35:08.999Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:35:09.718Z { "cell_id": "67", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:35:25.496Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"ed9f4e","wall_time_seconds":30.001909899,"session_id":35704,"original_token_count":0,"output":""} ## Step 126 · agent · 2026-08-31T19:35:28.016Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:35:28.839Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:35:39.884Z Script running with cell ID 68 Wall time 11.0 seconds Output: ## Step 127 · agent · 2026-08-31T19:35:42.748Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:35:43.148Z { "cell_id": "68", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:35:58.895Z Script completed Wall time 15.7 seconds Output: {"chunk_id":"c89898","wall_time_seconds":30.001693789,"session_id":35704,"original_token_count":0,"output":""} ## Step 128 · agent · 2026-08-31T19:36:00.502Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:01.900Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:36:12.917Z Script running with cell ID 69 Wall time 11.0 seconds Output: ## Step 129 · agent · 2026-08-31T19:36:15.343Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:36:16.155Z { "cell_id": "69", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:36:31.920Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"424955","wall_time_seconds":30.000682628,"session_id":35704,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=197.1s\n"} ## Step 130 · agent · 2026-08-31T19:36:34.049Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:36:35.717Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:36:46.764Z Script running with cell ID 70 Wall time 11.0 seconds Output: ## Step 131 · agent · 2026-08-31T19:36:49.934Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:36:50.696Z { "cell_id": "70", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:37:05.774Z Script completed Wall time 15.1 seconds Output: {"chunk_id":"937c7e","wall_time_seconds":30.001842521,"session_id":35704,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\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=210.2 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.504831\n"} ## Step 132 · agent · 2026-08-31T19:37:07.634Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:09.185Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:37:20.223Z Script running with cell ID 71 Wall time 11.0 seconds Output: ## Step 133 · agent · 2026-08-31T19:37:24.067Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:37:24.839Z { "cell_id": "71", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:37:39.240Z Script completed Wall time 14.4 seconds Output: {"chunk_id":"3ac12e","wall_time_seconds":30.001956858,"session_id":35704,"original_token_count":0,"output":""} ## Step 134 · agent · 2026-08-31T19:37:40.925Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:37:42.344Z { "code": "const r = await tools.write_stdin({session_id:35704,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:37:53.371Z Script running with cell ID 72 Wall time 11.0 seconds Output: ## Step 135 · agent · 2026-08-31T19:37:56.904Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:37:57.690Z { "cell_id": "72", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:38:01.976Z Script completed Wall time 4.3 seconds Output: {"chunk_id":"ba7e7f","wall_time_seconds":19.606326709,"exit_code":0,"original_token_count":828,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"validation\",\n \"output\": \"/app/solution/graded_validation.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 318.957,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"discovery-graded\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"hard_negative_pool_size\": 32,\n \"hard_negative_count\": 8,\n \"hard_negative_weight\": 0.25,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20056329,\n \"all_next_item_targets\": 20047194,\n \"selected_training_targets\": 1052953,\n \"selected_validation_targets\": 130103,\n \"discarded_old_targets\": 18864138,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5421154203829986,\n \"positive_logit\": 0.7774311310299945,\n \"negative_logit\": -0.6407562001908027,\n \"pair_accuracy\": 0.7602795423917307,\n \"targets\": 1052953.0,\n \"target_weight_sum\": 348780.0,\n \"batches\": 266.0,\n \"seconds\": 210.21664959500004,\n \"validation_loss\": 0.5048305744945647,\n \"validation_targets\": 130103.0,\n \"validation_target_weight_sum\": 42129.5,\n \"validation_seconds\": 15.439250831999743\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.7521849870681763,\n \"query_norm_std\": 0.8638168573379517,\n \"item_norm_mean\": 1.8543912172317505,\n \"item_norm_std\": 0.8459439873695374\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 136 · agent · 2026-08-31T19:38:03.850Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:38:06.032Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/graded_validation.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:38:09.391Z Script completed Wall time 3.4 seconds Output: {"chunk_id":"ec933d","wall_time_seconds":3.181827419,"exit_code":0,"original_token_count":34,"output":"{\n \"model\": \"/app/solution/graded_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.023522,\n \"quality_users\": 4263\n }\n}\n"} ## Step 137 · agent · 2026-08-31T19:38:18.429Z ### Thinking unavailable — no text recorded The cutoff-safe graded objective clears the baseline: **0.023522 NDCG@10 versus 0.02128**, a **10.5% relative lift**. It keeps the production 24/8 corrected negative sampler—the public tests showed that more aggressive hard/popularity sampling was harmful under this CPU budget—but fixes how positives paired with those negatives are weighted: first-encounter tracks followed by an organic like within seven days receive grade-3 emphasis. I’m now refitting this selected configuration on `train + val` and ranking only the test-eligible candidates. ### exec · 2026-08-31T19:38:32.162Z { "code": "const p = await tools.update_plan({explanation:\"Reproduced and diagnosed several negative-sampling variants. Aggressive hard/popularity sampling regressed; the cutoff-safe discovery-graded objective improved public NDCG by 10.5%, so it is selected for the final refit.\",plan:[\n {step:\"Reproduce baseline and inspect training/evaluation pipeline\",status:\"completed\"},\n {step:\"Diagnose negative sampling and implement focused variants\",status:\"completed\"},\n {step:\"Run public backtests and select the best valid model\",status:\"completed\"},\n {step:\"Refit on train + validation and generate hidden-test submission\",status:\"in_progress\"},\n {step:\"Validate artifact schema/eligibility and document results\",status:\"pending\"}\n]});\nconst r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/submission_prepared.npz --checkpoint-dir /app/solution/final_checkpoints --output /app/solution/submission.parquet\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:38:43.181Z Script running with cell ID 74 Wall time 11.0 seconds Output: ## Step 138 · agent · 2026-08-31T19:38:45.715Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:38:46.553Z { "cell_id": "74", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:39:02.344Z Script completed Wall time 15.8 seconds Output: {"chunk_id":"14a93d","wall_time_seconds":30.001286372,"session_id":64619,"original_token_count":79,"output":"loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.2s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.1s\n"} ## Step 139 · agent · 2026-08-31T19:39:05.698Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:07.454Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:39:18.488Z Script running with cell ID 75 Wall time 11.0 seconds Output: ## Step 140 · agent · 2026-08-31T19:39:20.211Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:39:21.015Z { "cell_id": "75", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:39:37.485Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"5df01d","wall_time_seconds":30.001970817,"session_id":64619,"original_token_count":207,"output":"prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.1s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.0s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=36.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.1s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.0s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\n"} ## Step 141 · agent · 2026-08-31T19:39:39.537Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:39:40.961Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:39:52.002Z Script running with cell ID 76 Wall time 11.0 seconds Output: ## Step 142 · agent · 2026-08-31T19:39:53.814Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:39:54.608Z { "cell_id": "76", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:40:11.020Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"803b26","wall_time_seconds":30.001933442,"session_id":64619,"original_token_count":53,"output":"grade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"} ## Step 143 · agent · 2026-08-31T19:40:12.879Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:14.330Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:40:25.372Z Script running with cell ID 77 Wall time 11.0 seconds Output: ## Step 144 · agent · 2026-08-31T19:40:27.544Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:40:28.340Z { "cell_id": "77", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:40:44.377Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"cd246f","wall_time_seconds":30.001944957,"session_id":64619,"original_token_count":0,"output":""} ## Step 145 · agent · 2026-08-31T19:40:48.749Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:40:48.749Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:40:59.808Z Script running with cell ID 78 Wall time 11.0 seconds Output: ## Step 146 · agent · 2026-08-31T19:41:01.544Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:02.324Z { "cell_id": "78", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:41:18.813Z Script completed Wall time 16.5 seconds Output: {"chunk_id":"196273","wall_time_seconds":30.00195649,"session_id":64619,"original_token_count":0,"output":""} ## Step 147 · agent · 2026-08-31T19:41:20.495Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:41:21.937Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:41:32.977Z Script running with cell ID 79 Wall time 11.0 seconds Output: ## Step 148 · agent · 2026-08-31T19:41:35.073Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:41:35.839Z { "cell_id": "79", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:41:51.991Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"de8f3d","wall_time_seconds":30.001216189,"session_id":64619,"original_token_count":0,"output":""} ## Step 149 · agent · 2026-08-31T19:41:53.946Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:41:55.436Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:42:06.469Z Script running with cell ID 80 Wall time 11.0 seconds Output: ## Step 150 · agent · 2026-08-31T19:42:09.258Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:10.020Z { "cell_id": "80", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:42:25.472Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"1e3443","wall_time_seconds":30.001030494,"session_id":64619,"original_token_count":0,"output":""} ## Step 151 · agent · 2026-08-31T19:42:27.122Z ### Thinking unavailable — no text recorded The final refit has correctly incorporated the validation window as observed history: 20.7M sequence tokens, 1.068M selected training targets, and 47,773 cutoff-safe grade-3 examples. Candidate eligibility and preference state are the test-cutoff artifacts; no validation outcomes are being used as test labels. ### exec · 2026-08-31T19:42:31.267Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:42:42.308Z Script running with cell ID 81 Wall time 11.0 seconds Output: ## Step 152 · agent · 2026-08-31T19:42:44.344Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:42:45.072Z { "cell_id": "81", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:43:01.320Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"dd044d","wall_time_seconds":30.001481266,"session_id":64619,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.455749 running_bce=0.544431 elapsed=200.0s\n"} ## Step 153 · agent · 2026-08-31T19:43:03.021Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:43:04.418Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:43:15.463Z Script running with cell ID 82 Wall time 11.0 seconds Output: ## Step 154 · agent · 2026-08-31T19:43:17.496Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:43:18.277Z { "cell_id": "82", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:43:34.468Z Script completed Wall time 16.2 seconds Output: {"chunk_id":"38fce5","wall_time_seconds":30.001274486,"session_id":64619,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\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=214.4 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.512617\n"} ## Step 155 · agent · 2026-08-31T19:43:36.310Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:43:37.794Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:43:48.828Z Script running with cell ID 83 Wall time 11.0 seconds Output: ## Step 156 · agent · 2026-08-31T19:43:50.987Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:43:51.854Z { "cell_id": "83", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:44:07.828Z Script completed Wall time 16.0 seconds Output: {"chunk_id":"7e258d","wall_time_seconds":30.001469987,"session_id":64619,"original_token_count":0,"output":""} ## Step 157 · agent · 2026-08-31T19:44:09.859Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:11.277Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:44:22.322Z Script running with cell ID 84 Wall time 11.0 seconds Output: ## Step 158 · agent · 2026-08-31T19:44:24.330Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:44:25.191Z { "cell_id": "84", "yield_time_ms": 30000, "max_tokens": 20000 } ### Tool output · 2026-08-31T19:44:41.327Z Script completed Wall time 16.1 seconds Output: {"chunk_id":"24fd14","wall_time_seconds":30.001992019,"session_id":64619,"original_token_count":0,"output":""} ## Step 159 · agent · 2026-08-31T19:44:43.094Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:44:44.480Z { "code": "const r = await tools.write_stdin({session_id:64619,chars:\"\",yield_time_ms:30000,max_output_tokens:20000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:44:52.409Z Script completed Wall time 7.9 seconds Output: {"chunk_id":"2664cf","wall_time_seconds":7.890011691,"exit_code":0,"original_token_count":826,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": \"submission\",\n \"output\": \"/app/solution/submission.parquet\",\n \"rows\": 845600,\n \"users\": 8456,\n \"runtime_seconds\": 375.873,\n \"public_inputs_only\": true,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": false,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n \"false_negative_filter\": \"full-training-history\",\n \"target_weighting\": \"discovery-graded\"\n },\n \"config\": {\n \"bptt_steps\": 100,\n \"user_batch_size\": 64,\n \"embedding_dim\": 192,\n \"hidden_dim\": 192,\n \"user_hidden_1\": 512,\n \"user_hidden_2\": 256,\n \"item_hidden_1\": 512,\n \"item_hidden_2\": 256,\n \"dropout\": 0.05,\n \"epochs\": 1,\n \"learning_rate\": 0.001,\n \"weight_decay\": 1e-05,\n \"gradient_clip\": 2.0,\n \"loss_microbatch_size\": 65536,\n \"uniform_negatives\": 24,\n \"inbatch_negatives\": 8,\n \"hard_negative_pool_size\": 32,\n \"hard_negative_count\": 8,\n \"hard_negative_weight\": 0.25,\n \"inbatch_correction_power\": 0.5,\n \"inbatch_correction_min\": 0.25,\n \"inbatch_correction_max\": 4.0,\n \"negative_filter_scope\": \"full-training-history\",\n \"target_weighting\": \"discovery-graded\",\n \"ordinary_target_weight\": 0.25,\n \"organic_strong_target_weight\": 1.0,\n \"organic_like_target_weight\": 3.0,\n \"discovery_horizon_days\": 7,\n \"validation_fraction\": 0.1,\n \"max_train_targets_per_user\": 128,\n \"max_validation_targets_per_user\": 16,\n \"early_stopping_patience\": 1,\n \"early_stopping_min_delta\": 0.0001,\n \"seed\": 29\n },\n \"sequence_tokens\": 20712971,\n \"all_next_item_targets\": 20703818,\n \"selected_training_targets\": 1067606,\n \"selected_validation_targets\": 131997,\n \"discarded_old_targets\": 19504215,\n \"training_history\": [\n {\n \"epoch\": 1.0,\n \"loss\": 0.5408499574223266,\n \"positive_logit\": 0.7880318116306771,\n \"negative_logit\": -0.656281838298863,\n \"pair_accuracy\": 0.7620188592514467,\n \"targets\": 1067606.0,\n \"target_weight_sum\": 353472.75,\n \"batches\": 270.0,\n \"seconds\": 214.4452345550003,\n \"validation_loss\": 0.5126167220076836,\n \"validation_targets\": 131997.0,\n \"validation_target_weight_sum\": 43182.0,\n \"validation_seconds\": 15.432067502999871\n }\n ],\n \"embedding_diagnostics\": {\n \"query_norm_mean\": 1.4391510486602783,\n \"query_norm_std\": 0.6248915791511536,\n \"item_norm_mean\": 1.8983010053634644,\n \"item_norm_std\": 0.8495252728462219\n },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n"} ## Step 160 · agent · 2026-08-31T19:45:00.112Z ### Thinking unavailable — no text recorded The final ranking has been written with 845,600 rows for all 8,456 target users. I’m doing an independent artifact audit now—integer schema, exact 100× users, rank permutation, distinct items, and row-by-row test eligibility—then I’ll record the experiment and reproduction command. ### exec · 2026-08-31T19:45:20.461Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\npath=Path('submission.parquet')\\nassert path.is_file() and not path.is_symlink()\\ndf=pd.read_parquet(path)\\nassert list(df.columns)==['uid','item_id','rank'], df.columns\\nassert len(df)==845600\\nassert all(np.issubdtype(df[c].dtype,np.integer) for c in df.columns), df.dtypes\\nassert np.isfinite(df[['uid','item_id','rank']].to_numpy()).all()\\ntarget=pd.read_parquet('/task/data/target_users.parquet')['uid'].to_numpy(np.int64)\\nassert np.array_equal(np.sort(df.uid.unique()),np.sort(target))\\ng=df.groupby('uid',sort=False)\\nassert (g.size()==100).all()\\nassert (g.item_id.nunique()==100).all()\\nassert (g['rank'].min()==1).all() and (g['rank'].max()==100).all() and (g['rank'].nunique()==100).all()\\nz=np.load('/task/data/test_eligible_candidates.npz')\\nuid_row={int(u):r for r,u in enumerate(z['uids'])}\\nitem_col={int(i):c for c,i in enumerate(z['item_ids'])}\\nfor uid,item in df[['uid','item_id']].itertuples(index=False,name=None):\\n r=uid_row[int(uid)]; c=item_col[int(item)]\\n assert (int(z['packed_eligible'][r,c>>3]) >> (c&7)) & 1\\nprint({'file':str(path.resolve()),'regular':True,'rows':len(df),'users':df.uid.nunique(),'per_user':100,'ranks':'1..100','distinct_items':True,'test_eligible':True,'dtypes':{c:str(df[c].dtype) for c in df}})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:45:31.480Z Script running with cell ID 86 Wall time 11.0 seconds Output: ## Step 161 · agent · 2026-08-31T19:45:33.520Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:45:34.251Z { "cell_id": "86", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:45:50.645Z Script completed Wall time 16.4 seconds Output: {"chunk_id":"154dbb","wall_time_seconds":30.00433669,"session_id":62531,"original_token_count":0,"output":""} ## Step 162 · agent · 2026-08-31T19:45:52.832Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:45:54.230Z { "code": "const r = await tools.write_stdin({session_id:62531,chars:\"\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:46:05.249Z Script running with cell ID 87 Wall time 11.0 seconds Output: ## Step 163 · agent · 2026-08-31T19:46:07.900Z ### Thinking unavailable — no text recorded ### wait · 2026-08-31T19:46:08.737Z { "cell_id": "87", "yield_time_ms": 30000, "max_tokens": 10000 } ### Tool output · 2026-08-31T19:46:24.253Z Script completed Wall time 15.5 seconds Output: {"chunk_id":"87a354","wall_time_seconds":30.001086273,"session_id":62531,"original_token_count":0,"output":""} ## Step 164 · agent · 2026-08-31T19:46:28.509Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:30.056Z { "code": "const r = await tools.write_stdin({session_id:62531,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:46:30.189Z Script completed Wall time 0.1 seconds Output: {"chunk_id":"31229b","wall_time_seconds":0.058292173,"exit_code":130,"original_token_count":236,"output":"Traceback (most recent call last):\n File \"\", line 21, in \n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"} ## Step 165 · agent · 2026-08-31T19:46:34.090Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:46:52.273Z { "code": "const r = await tools.exec_command({cmd:\"cd /app/solution && /opt/task-venv/bin/python - <<'PY'\\nfrom pathlib import Path\\nimport numpy as np, pandas as pd\\npath=Path('submission.parquet')\\nassert path.is_file() and not path.is_symlink()\\ndf=pd.read_parquet(path)\\nassert list(df.columns)==['uid','item_id','rank']\\nassert len(df)==845600\\nassert all(np.issubdtype(df[c].dtype,np.integer) for c in df.columns)\\nassert np.isfinite(df[['uid','item_id','rank']].to_numpy()).all()\\ntarget=pd.read_parquet('/task/data/target_users.parquet')['uid'].to_numpy(np.int64)\\nassert np.array_equal(np.sort(df.uid.unique()),np.sort(target))\\ng=df.groupby('uid',sort=False)\\nassert (g.size()==100).all()\\nassert (g.item_id.nunique()==100).all()\\nassert (g['rank'].min()==1).all() and (g['rank'].max()==100).all() and (g['rank'].nunique()==100).all()\\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\\nr=pd.Index(uids).get_indexer(df.uid.to_numpy(np.int64))\\nc=pd.Index(items).get_indexer(df.item_id.to_numpy(np.int64))\\nassert (r>=0).all() and (c>=0).all()\\neligible=((packed[r,c>>3] >> (c&7)) & 1).astype(bool)\\nassert eligible.all(), int((~eligible).sum())\\nprint({'file':str(path.resolve()),'regular':True,'rows':len(df),'users':df.uid.nunique(),'per_user':100,'ranks':'1..100','distinct_items':True,'test_eligible':True,'dtypes':{c:str(df[c].dtype) for c in df}})\\nPY\\nls -lh /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000}); text(JSON.stringify(r));" } ### Tool output · 2026-08-31T19:46:54.074Z Script completed Wall time 1.8 seconds Output: {"chunk_id":"06b381","wall_time_seconds":1.6197288250000001,"exit_code":0,"original_token_count":119,"output":"{'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'per_user': 100, 'ranks': '1..100', 'distinct_items': True, 'test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n-rw-r--r-- 1 agent agent 1.2M Aug 31 19:44 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 82K Aug 31 19:32 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent 16K Aug 31 19:02 /app/solution/two_tower_model.py\n"} ## Step 166 · agent · 2026-08-31T19:47:02.638Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:26.723Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/README.md\\n+# Improved Yambda two-tower\\n+\\n+## Result\\n+\\n+Public validation NDCG@10 improved from **0.021280** to **0.023522**\\n+(+0.002242 absolute, +10.54% relative; 4,263 scored users).\\n+\\n+The final artifact is `submission.parquet`. It has 845,600 rows, exactly 100\\n+distinct eligible items and ranks 1--100 for each of the 8,456 target users.\\n+\\n+## Diagnosis and model change\\n+\\n+The production sampler uses 24 uniform and eight propensity-corrected in-batch\\n+negatives, with a full-training-history false-negative filter. I tested three\\n+more aggressive alternatives under the same chronological protocol:\\n+\\n+| Variant | Public NDCG@10 |\\n+| --- | ---: |\\n+| Production objective | 0.021280 |\\n+| Replace loss with top-32 from a 48-item hard pool | 0.019374 |\\n+| Production loss plus a 25% top-8 hard term | 0.015770 |\\n+| 16 uniform + 16 uncorrected in-batch | 0.014744 |\\n+| **Selected: discovery-graded weighting, production sampler** | **0.023522** |\\n+\\n+The negative tests showed that popularity-skewed and aggressively mined\\n+negatives over-concentrate a one-epoch CPU fit. The platform eligibility rule\\n+already removes prior encounters, while the production uniform component,\\n+propensity correction, and full-history filter approximate that discovery\\n+candidate boundary well.\\n+\\n+The selected change fixes the remaining loss mismatch without changing the\\n+towers or serving score. Adjacent targets remain nonzero-weight examples;\\n+organic >=80% listens receive grade-1 emphasis, and cutoff-safe first encounters\\n+followed by an organic like within seven days receive grade-3 emphasis. On the\\n+validation stage, only outcomes inside `train_*` are used to construct these\\n+weights. The final fit analogously uses `train_* + val_*` as observed history.\\n+\\n+The model remains a single sequential user tower and independent precomputable\\n+item tower with raw dot-product retrieval. It has no user-ID embedding or\\n+per-candidate sequence computation.\\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+ --prepared-cache /app/solution/submission_prepared.npz \\\\\\n+ --checkpoint-dir /app/solution/final_checkpoints \\\\\\n+ --output /app/solution/submission.parquet\\n+```\\n+\\n+The prepared cache and checkpoint flags are optional and only reduce rerun or\\n+recovery cost.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));" } ### Tool output · 2026-08-31T19:47:26.791Z Script completed Wall time 0.0 seconds Output: {} ## Step 167 · agent · 2026-08-31T19:47:34.581Z ### Thinking unavailable — no text recorded The independent audit passed every submission constraint: regular Parquet file, integer `int64` fields, exactly 8,456 expected users, 100 distinct items per user, ranks 1–100, and every one of the 845,600 rows is test-eligible. The implementation and experiment record are in place; I’m doing a final file-level sanity check and then handing off the artifact. ### exec · 2026-08-31T19:47:43.206Z { "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\\nfind /app/solution -maxdepth 1 -type f -printf '%f %s bytes\\\\n' | sort\\nsha256sum /app/solution/submission.parquet /app/solution/train_current_model.py /app/solution/two_tower_model.py /app/solution/README.md\\nsed -n '1,220p' /app/solution/README.md\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:12000}); text(r.output);" } ### Tool output · 2026-08-31T19:47:43.383Z Script completed Wall time 0.2 seconds Output: README.md 2302 bytes blended_validation.parquet 1254039 bytes graded_validation.parquet 1249069 bytes hard48_validation.parquet 1208798 bytes inbatch16_validation.parquet 1282732 bytes popularity_diagnostic.parquet 728599 bytes production_validation.parquet 1244231 bytes submission.parquet 1200186 bytes submission_prepared.npz 552686232 bytes train_current_model.py 83886 bytes two_tower_model.py 15985 bytes validation_prepared.npz 535225808 bytes 51089f5db60c7c08e7be996437485b2c6067c10df8ed67fa5070260480fb7399 /app/solution/submission.parquet 524461294c95ab652c3cebb97946693b357b482687b54dbc7661a5df56f3376b /app/solution/train_current_model.py 944adf51f9fc61fcbb8392c5c62243b9cc74b5e2ca2f403b5722967f14773f16 /app/solution/two_tower_model.py 465af08ef5d339eaf1696b18a1e8cb9b16f77c0b79757026ca21a819f1b53472 /app/solution/README.md # Improved Yambda two-tower ## Result Public validation NDCG@10 improved from **0.021280** to **0.023522** (+0.002242 absolute, +10.54% relative; 4,263 scored users). The final artifact is `submission.parquet`. It has 845,600 rows, exactly 100 distinct eligible items and ranks 1--100 for each of the 8,456 target users. ## Diagnosis and model change The production sampler uses 24 uniform and eight propensity-corrected in-batch negatives, with a full-training-history false-negative filter. I tested three more aggressive alternatives under the same chronological protocol: | Variant | Public NDCG@10 | | --- | ---: | | Production objective | 0.021280 | | Replace loss with top-32 from a 48-item hard pool | 0.019374 | | Production loss plus a 25% top-8 hard term | 0.015770 | | 16 uniform + 16 uncorrected in-batch | 0.014744 | | **Selected: discovery-graded weighting, production sampler** | **0.023522** | The negative tests showed that popularity-skewed and aggressively mined negatives over-concentrate a one-epoch CPU fit. The platform eligibility rule already removes prior encounters, while the production uniform component, propensity correction, and full-history filter approximate that discovery candidate boundary well. The selected change fixes the remaining loss mismatch without changing the towers or serving score. Adjacent targets remain nonzero-weight examples; organic >=80% listens receive grade-1 emphasis, and cutoff-safe first encounters followed by an organic like within seven days receive grade-3 emphasis. On the validation stage, only outcomes inside `train_*` are used to construct these weights. The final fit analogously uses `train_* + val_*` as observed history. The model remains a single sequential user tower and independent precomputable item tower with raw dot-product retrieval. It has no user-ID embedding or per-candidate sequence computation. ## Reproduce the final artifact ```sh /opt/task-venv/bin/python /app/solution/train_current_model.py \ --stage submission \ --public-dir /task \ --prepared-cache /app/solution/submission_prepared.npz \ --checkpoint-dir /app/solution/final_checkpoints \ --output /app/solution/submission.parquet ``` The prepared cache and checkpoint flags are optional and only reduce rerun or recovery cost. ## Step 168 · agent · 2026-08-31T19:47:45.489Z ### Thinking unavailable — no text recorded ### exec · 2026-08-31T19:47:50.716Z { "code": "const p = await tools.update_plan({explanation:\"Selected configuration was refit on train + validation, the test ranking was generated, independently validated against every artifact constraint, and documented.\",plan:[\n {step:\"Reproduce baseline and inspect training/evaluation pipeline\",status:\"completed\"},\n {step:\"Diagnose negative sampling and implement focused variants\",status:\"completed\"},\n {step:\"Run public backtests and select the best valid model\",status:\"completed\"},\n {step:\"Refit on train + validation and generate hidden-test submission\",status:\"completed\"},\n {step:\"Validate artifact schema/eligibility and document results\",status:\"completed\"}\n]}); text(p);" } ### Tool output · 2026-08-31T19:47:50.879Z Script completed Wall time 0.0 seconds Output: {} ## Step 169 · agent · 2026-08-31T19:47:54.545Z ### Thinking unavailable — no text recorded Completed and validated the improved two-tower submission. - Final artifact: [submission.parquet](/app/solution/submission.parquet) - Improved trainer: [train_current_model.py](/app/solution/train_current_model.py) - Model module: [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**—a **10.54% relative lift**. The submission passed all structural, user-coverage, rank, distinct-item, integer-type, and test-eligibility checks.