{
  "schemaVersion": 1,
  "taskId": "yambda-discovery-ranking-two-tower-case-1",
  "attemptId": "opus-high-2.1.251-exact-r03-2026-09-24",
  "taskChecksum": "8404d794ce8e18d154cd225cfe6b12bf6b4ee02aaefba1d315aa215c1175a013",
  "source": {
    "format": "claude-code-session",
    "sha256": "7ccb1b08811a55ca3c1124bb40b42965afd5f3d1f1d65b42e5407a1dd2248971",
    "records": 266,
    "bookkeepingRecords": {
      "queue-operation": 2,
      "atis-latch": 15,
      "last-prompt": 15
    }
  },
  "sessionId": "016455d5-6bb9-461d-a0e8-a1d02ce1292f",
  "agentVersion": "2.1.251",
  "model": "claude-opus-5",
  "steps": [
    {
      "id": 1,
      "role": "user",
      "timestamp": "2026-09-24T17:36:34.188Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:36:34.188Z",
          "text": "# Improve a production music-discovery two-tower\n\nYou have joined the recommendations team for a music streaming service. The\n\"Made for You\" shelf is served by a mature production two-tower model. Its\nchronological data protocol and indexed serving path are sound, but its offline\nretrieval quality no longer clears the team's next launch gate.\n\nYour job is to investigate the production model, improve its retrieval quality,\nand ship a better ranking artifact. Use the implementation and public validation\ndata to form and test your own diagnosis.\n\nThe source used to reproduce the production model is included under\n`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add\ntests, and improve its training objective, but keep the data protocol,\ntwo-tower serving contract, and evaluation boundary intact.\n\n## Production Context\n\nThe model must satisfy a strict request-time latency budget. Item vectors are\nprecomputed offline. At request time, one user vector is computed from observed\ninteraction history and compared with candidate item vectors by a batchable\ntwo-tower score. Do not introduce a cross-encoder, per-candidate sequence model,\nor user-ID lookup table.\n\nThe current trainer is a mature baseline with chronological internal\nvalidation. Focus your investigation on how its training objective selects and\nuses negative examples. Diagnose limitations in the current negative-sampling\nstrategy and improve it under the fixed CPU budget. Validate changes with the\npublic backtest. The final approach must still train and rank within the supplied\nCPU environment while preserving the serving and data boundaries above.\n\n## What You Have\n\nAll public inputs are under `/task/data/`. A stage cutoff is the timestamp at\nwhich observed history and candidate eligibility are frozen for a ranking. The\npublic validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute\ngap precedes the seven-day `val_*` window. For the final submission, that\nvalidation window becomes observed history, and another 30-minute gap precedes\nthe hidden seven-day outcome window.\n\nThe package includes:\n\n- `/task/current_model/train_current_model.py`: the production training and\n  ranking script. It can reproduce the public validation ranking and produce a\n  hidden-test submission after refitting.\n- `/task/current_model/two_tower_model.py`: model primitives used by the\n  production script.\n- `/task/evaluate_public.py`: the public validation evaluator for development.\n- `/task/data/validation_eligible_candidates.npz` and\n  `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for\n  the validation and hidden-test cutoffs.\n- `/task/data/validation_preference_state.parquet` and\n  `/task/data/test_preference_state.parquet`: active explicit preference state\n  at the corresponding cutoffs.\n- `/task/requirements.txt`: the exact offline Python distribution inventory.\n\nYou can reproduce the production public backtest with:\n\n```sh\npython /task/current_model/train_current_model.py \\\n  --stage validation \\\n  --public-dir /task \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\n## Data\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action came\nthrough a recommendation surface. A listen is a conventional positive at 50%\ncompletion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. For model development and selection, train only from `train_*` and score the\n   subsequent `val_*` outcomes.\n2. After selecting an improvement, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nValidation candidates exclude all `train_*` encounters. Test candidates exclude\nall `train_*` and `val_*` encounters. Future test interactions are hidden and\nused only by the separate Harbor verifier.\n\n## Discovery Objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this same objective for development, but its score\nis not a separate acceptance stage. Final acceptance is determined on the\nhidden test using the same objective. The Yambda logs contain interactions, not\nimpression propensities; treat the organic objective as a robust offline target,\nnot as a causal estimate of online lift.\n\n## Deliverable\n\nSubmit one final ranking as `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.\nIf both files exist, the verifier evaluates `submission.parquet` and ignores\n`submission.csv`.\n\nThe ranking must:\n\n- include the columns `uid`, `item_id`, and `rank`; only these columns are\n  evaluated;\n- use finite, integer-valued entries in all three required columns;\n- cover exactly the users in `target_users.parquet`, with no missing or extra\n  users;\n- contain exactly 100 rows and 100 distinct items for every target user;\n- contain every integer rank from 1 through 100 exactly once per user; and\n- use only items present in that user's `test_eligible_candidates.npz`\n  candidate set.\n\nAlso leave the improved implementation used to build the artifact at\n`/app/solution/train_current_model.py`, along with any local modules it imports.\nA concise `/app/solution/README.md` with the diagnosis, before-and-after public\nscore, model change, and submission command is useful for the experiment record,\nbut it is not an automated acceptance gate.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model-provider\n  connection managed by Harbor.\n- Do not alter cutoff definitions, target users, candidate eligibility, or\n  relevance labels.\n- Improve the production pipeline in place. Do not substitute an unrelated\n  model family or evade the indexed two-tower serving constraint.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Prioritize measured validation improvements, a\n  correct final refit, and a valid submission over broad architecture search.\n- Keep the best valid submission saved throughout the attempt so it survives\n  the deadline.\n"
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:34.187Z",
          "data": {
            "type": "deferred_tools_delta",
            "addedNames": [
              "CronCreate",
              "CronDelete",
              "CronList",
              "EnterWorktree",
              "ExitWorktree",
              "NotebookEdit",
              "RemoteTrigger",
              "SendMessage",
              "TaskOutput",
              "TaskStop",
              "WebFetch",
              "WebSearch"
            ],
            "addedLines": [
              "CronCreate",
              "CronDelete",
              "CronList",
              "EnterWorktree",
              "ExitWorktree",
              "NotebookEdit",
              "RemoteTrigger",
              "SendMessage",
              "TaskOutput",
              "TaskStop",
              "WebFetch",
              "WebSearch"
            ],
            "removedNames": [],
            "wireHiddenNames": [],
            "readdedNames": [],
            "pendingMcpServers": [],
            "needsAuthMcpServers": [],
            "failedMcpServers": []
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:34.187Z",
          "data": {
            "type": "agent_listing_delta",
            "addedTypes": [
              "claude",
              "Explore",
              "general-purpose",
              "Plan",
              "statusline-setup"
            ],
            "addedLines": [
              "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)",
              "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
              "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)",
              "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
              "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"
            ],
            "removedTypes": [],
            "isInitial": true,
            "showConcurrencyNote": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:34.187Z",
          "data": {
            "type": "skill_listing",
            "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch",
            "skillCount": 13,
            "isInitial": true,
            "names": [
              "dataviz",
              "update-config",
              "keybindings-help",
              "code-review",
              "simplify",
              "fewer-permission-prompts",
              "loop",
              "schedule",
              "claude-api",
              "workflow-authoring",
              "run",
              "init",
              "security-review"
            ]
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:34.187Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>15000000 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 2,
      "role": "agent",
      "timestamp": "2026-09-24T17:36:37.573Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:36:37.573Z",
          "text": "I'll start by investigating the production model and data."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:38.287Z",
          "callId": "toolu_018VQUobNUpVKKTCNjbPWokH",
          "name": "Bash",
          "input": {
            "command": "ls -la /task/ /task/data/ /task/current_model/ 2>&1 | head -60; echo \"---\"; nproc; free -g | head -3"
          },
          "result": {
            "timestamp": "2026-09-24T17:36:38.362Z",
            "content": "/task/:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 17:32 .\ndrwxr-xr-x 1 root root 4096 Sep 24 17:35 ..\n-r--r--r-- 1 root root    0 Sep 24 17:30 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 17:30 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 17:30 data\n-r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 17:30 requirements.txt\n\n/task/current_model/:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 24 17:30 .\ndr-xr-xr-x 1 root root  4096 Sep 24 17:32 ..\n-r--r--r-- 1 root root 80058 Sep 24 17:30 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 24 17:30 two_tower_model.py\n\n/task/data/:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 24 17:30 .\ndr-xr-xr-x 1 root root      4096 Sep 24 17:32 ..\n-r--r--r-- 1 root root    349352 Sep 24 17:30 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 24 17:30 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 24 17:30 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 24 17:30 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 24 17:30 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 24 17:30 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 24 17:30 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 24 17:30 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 24 17:30 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 24 17:30 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 24 17:30 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 24 17:30 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 24 17:30 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 24 17:30 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 24 17:30 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 24 17:30 validation_preference_state.parquet\n---\n8\n               total        used        free      shared  buff/cache   available\nMem:              15           1           0           0          13          14\nSwap:              0           0           0",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:38.726Z",
          "callId": "toolu_01LZU4cpP86eENx4YPvqm729",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/train_current_model.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:36:38.859Z",
            "content": "1\t#!/usr/bin/env python3\n2\t\"\"\"Train the Yambda windowed two-tower baseline.\n3\t\n4\tEvery catalog listen with at least 50 percent completion is a sequence token.\n5\tWithin each selected chronological window, every adjacent target contributes a\n6\tloss.  The fast default retains each user's most recent 128 training targets and\n7\t16 held-out validation targets; passing zero for either cap restores the\n8\tuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\n9\tcomputes all of its timestep losses and carries the detached GRU hidden state\n10\tinto the next block.\n11\t\n12\tThe model is a pure two-tower retriever: no user-ID embedding, no output\n13\tnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n14\tand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n15\tselected adjacent target retains a nonzero weight. Only files under the supplied\n16\tpublic directory are read.\n17\t\"\"\"\n18\tfrom __future__ import annotations\n19\t\n20\timport argparse\n21\timport json\n22\timport random\n23\timport sys\n24\timport time\n25\tfrom dataclasses import asdict, dataclass\n26\tfrom pathlib import Path\n27\t\n28\timport numpy as np\n29\timport pandas as pd\n30\timport torch\n31\tfrom torch import nn\n32\tfrom torch.nn import functional as F\n33\t\n34\timport two_tower_model as base\n35\t\n36\t\n37\tCACHE_VERSION = 2\n38\t\n39\t\n40\t@dataclass(frozen=True)\n41\tclass Config:\n42\t    bptt_steps: int = 100\n43\t    user_batch_size: int = 64\n44\t    embedding_dim: int = 192\n45\t    hidden_dim: int = 192\n46\t    user_hidden_1: int = 512\n47\t    user_hidden_2: int = 256\n48\t    item_hidden_1: int = 512\n49\t    item_hidden_2: int = 256\n50\t    dropout: float = 0.05\n51\t    epochs: int = 1\n52\t    learning_rate: float = 1.0e-3\n53\t    weight_decay: float = 1.0e-5\n54\t    gradient_clip: float = 2.0\n55\t    loss_microbatch_size: int = 65_536\n56\t    uniform_negatives: int = 24\n57\t    inbatch_negatives: int = 8\n58\t    inbatch_correction_power: float = 0.5\n59\t    inbatch_correction_min: float = 0.25\n60\t    inbatch_correction_max: float = 4.0\n61\t    negative_filter_scope: str = \"full-training-history\"\n62\t    target_weighting: str = \"organic-strong\"\n63\t    ordinary_target_weight: float = 0.25\n64\t    organic_strong_target_weight: float = 1.0\n65\t    organic_like_target_weight: float = 3.0\n66\t    discovery_horizon_days: int = 7\n67\t    validation_fraction: float = 0.10\n68\t    max_train_targets_per_user: int = 128\n69\t    max_validation_targets_per_user: int = 16\n70\t    early_stopping_patience: int = 1\n71\t    early_stopping_min_delta: float = 1.0e-4\n72\t    seed: int = 29\n73\t\n74\t\n75\t@dataclass(frozen=True)\n76\tclass CandidateSet:\n77\t    uids: np.ndarray\n78\t    item_ids: np.ndarray\n79\t    packed: np.ndarray\n80\t\n81\t    @classmethod\n82\t    def read(cls, path: Path) -> \"CandidateSet\":\n83\t        with np.load(path, allow_pickle=False) as artifact:\n84\t            result = cls(\n85\t                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n86\t                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n87\t                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n88\t            )\n89\t        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n90\t        if result.packed.shape != expected:\n91\t            raise ValueError(\n92\t                f\"invalid candidate artifact shape {result.packed.shape}; \"\n93\t                f\"expected {expected}\"\n94\t            )\n95\t        return result\n96\t\n97\t\n98\t@dataclass(frozen=True)\n99\tclass SequenceData:\n100\t    item_ids: np.ndarray\n101\t    item_features: np.ndarray\n102\t    user_ids: np.ndarray\n103\t    offsets: np.ndarray\n104\t    sequence_timestamps: np.ndarray\n105\t    sequence_items: np.ndarray\n106\t    event_features: np.ndarray\n107\t    activity_features: np.ndarray\n108\t    state_ids: np.ndarray\n109\t    liked_states: np.ndarray\n110\t    disliked_states: np.ndarray\n111\t    state_counts: np.ndarray\n112\t    inference_user_rows: np.ndarray\n113\t    inference_liked_items: np.ndarray\n114\t    inference_disliked_items: np.ndarray\n115\t    inference_user_features: np.ndarray\n116\t\n117\t    @property\n118\t    def lengths(self) -> np.ndarray:\n119\t        return np.diff(self.offsets)\n120\t\n121\t    @property\n122\t    def target_count(self) -> int:\n123\t        return int(np.maximum(self.lengths - 1, 0).sum())\n124\t\n125\t\n126\t@dataclass(frozen=True)\n127\tclass TargetPartition:\n128\t    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n129\t\n130\t    window_starts: np.ndarray\n131\t    training_counts: np.ndarray\n132\t    validation_counts: np.ndarray\n133\t    history_training_counts: np.ndarray\n134\t\n135\t    @property\n136\t    def selected_count(self) -> int:\n137\t        return int(self.training_counts.sum() + self.validation_counts.sum())\n138\t\n139\t\n140\tdef log(message: str) -> None:\n141\t    print(message, file=sys.stderr, flush=True)\n142\t\n143\t\n144\tdef seed_everything(seed: int) -> None:\n145\t    random.seed(seed)\n146\t    np.random.seed(seed)\n147\t    torch.manual_seed(seed)\n148\t\n149\t\n150\tdef model_config(config: Config) -> base.TowerConfig:\n151\t    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n152\t    return base.TowerConfig(\n153\t        sequence_length=config.bptt_steps,\n154\t        embedding_dim=config.embedding_dim,\n155\t        hidden_dim=config.hidden_dim,\n156\t        user_hidden_1=config.user_hidden_1,\n157\t        user_hidden_2=config.user_hidden_2,\n158\t        item_hidden_1=config.item_hidden_1,\n159\t        item_hidden_2=config.item_hidden_2,\n160\t        dropout=config.dropout,\n161\t        epochs=config.epochs,\n162\t        learning_rate=config.learning_rate,\n163\t        weight_decay=config.weight_decay,\n164\t        gradient_clip=config.gradient_clip,\n165\t        uniform_negatives=config.uniform_negatives,\n166\t        inbatch_negatives=config.inbatch_negatives,\n167\t        inbatch_correction_power=config.inbatch_correction_power,\n168\t        inbatch_correction_min=config.inbatch_correction_min,\n169\t        inbatch_correction_max=config.inbatch_correction_max,\n170\t        seed=config.seed,\n171\t    )\n172\t\n173\t\n174\tdef append_state_snapshot(\n175\t    active_likes: dict[int, int],\n176\t    active_dislikes: dict[int, int],\n177\t    n_items: int,\n178\t    liked_states: list[np.ndarray],\n179\t    disliked_states: list[np.ndarray],\n180\t    state_counts: list[tuple[int, int]],\n181\t) -> int:\n182\t    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n183\t    disliked_states.append(\n184\t        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n185\t    )\n186\t    state_counts.append((len(active_likes), len(active_dislikes)))\n187\t    return len(liked_states) - 1\n188\t\n189\t\n190\tdef causal_activity_features(\n191\t    timestamps: np.ndarray,\n192\t    items: np.ndarray,\n193\t    organic: np.ndarray,\n194\t    completion: np.ndarray,\n195\t) -> np.ndarray:\n196\t    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n197\t    length = len(items)\n198\t    result = np.zeros((length, 4), dtype=np.float16)\n199\t    if not length:\n200\t        return result\n201\t    left_rows = np.searchsorted(\n202\t        timestamps,\n203\t        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n204\t        side=\"left\",\n205\t    )\n206\t    rows = np.arange(length, dtype=np.int64)\n207\t    counts = rows - left_rows + 1\n208\t    organic_sum = np.cumsum(organic.astype(np.float64))\n209\t    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n210\t    before_organic = np.where(\n211\t        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n212\t    )\n213\t    before_completion = np.where(\n214\t        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n215\t    )\n216\t\n217\t    unique_counts = np.empty(length, dtype=np.int32)\n218\t    frequencies: dict[int, int] = {}\n219\t    left = 0\n220\t    for right, raw_item in enumerate(items):\n221\t        item = int(raw_item)\n222\t        frequencies[item] = frequencies.get(item, 0) + 1\n223\t        required_left = int(left_rows[right])\n224\t        while left < required_left:\n225\t            old_item = int(items[left])\n226\t            remaining = frequencies[old_item] - 1\n227\t            if remaining:\n228\t                frequencies[old_item] = remaining\n229\t            else:\n230\t                del frequencies[old_item]\n231\t            left += 1\n232\t        unique_counts[right] = len(frequencies)\n233\t\n234\t    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n235\t    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n236\t    result[:, 2] = (\n237\t        (organic_sum - before_organic) / np.maximum(counts, 1)\n238\t    ).astype(np.float16)\n239\t    result[:, 3] = (\n240\t        (completion_sum - before_completion)\n241\t        / np.maximum(counts, 1)\n242\t        / 100.0\n243\t    ).astype(np.float16)\n244\t    return result\n245\t\n246\t\n247\tdef build_sequence_parts(\n248\t    listens: base.ListenEvents,\n249\t    explicit: base.ExplicitEvents,\n250\t    n_items: int,\n251\t    liked_states: list[np.ndarray],\n252\t    disliked_states: list[np.ndarray],\n253\t    state_counts: list[tuple[int, int]],\n254\t) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n255\t    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n256\t    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n257\t    items = listens.items[qualifying].astype(np.uint16, copy=False)\n258\t    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n259\t    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n260\t    if not len(items):\n261\t        return None\n262\t\n263\t    event_features = np.zeros((len(items), 3), dtype=np.float16)\n264\t    event_features[:, 0] = organic.astype(np.float16)\n265\t    event_features[:, 1] = (\n266\t        np.minimum(completion, 100.0) / 100.0\n267\t    ).astype(np.float16)\n268\t    if len(items) > 1:\n269\t        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n270\t        event_features[1:, 2] = np.exp(\n271\t            -gaps.astype(np.float64) / (60.0 * base.DAY)\n272\t        ).astype(np.float16)\n273\t\n274\t    activity = causal_activity_features(\n275\t        timestamps, items, organic, completion\n276\t    )\n277\t    states = np.zeros(len(items), dtype=np.uint32)\n278\t    active_likes: dict[int, int] = {}\n279\t    active_dislikes: dict[int, int] = {}\n280\t    explicit_position = 0\n281\t    current_state = 0\n282\t    for row, timestamp in enumerate(timestamps):\n283\t        changed = False\n284\t        while (\n285\t            explicit_position < len(explicit.timestamps)\n286\t            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n287\t        ):\n288\t            item = int(explicit.items[explicit_position])\n289\t            event_timestamp = int(explicit.timestamps[explicit_position])\n290\t            destination = (\n291\t                active_likes\n292\t                if int(explicit.channel[explicit_position]) == 0\n293\t                else active_dislikes\n294\t            )\n295\t            if int(explicit.active[explicit_position]) == 1:\n296\t                destination[item] = event_timestamp\n297\t            else:\n298\t                destination.pop(item, None)\n299\t            explicit_position += 1\n300\t            changed = True\n301\t        if changed:\n302\t            current_state = append_state_snapshot(\n303\t                active_likes,\n304\t                active_dislikes,\n305\t                n_items,\n306\t                liked_states,\n307\t                disliked_states,\n308\t                state_counts,\n309\t            )\n310\t        states[row] = np.uint32(current_state)\n311\t    return timestamps, items.copy(), event_features, activity, states\n312\t\n313\t\n314\tdef final_user_features(\n315\t    timestamps: np.ndarray,\n316\t    items: np.ndarray,\n317\t    event_features: np.ndarray,\n318\t    history_end: int,\n319\t    like_count: int,\n320\t    dislike_count: int,\n321\t) -> np.ndarray:\n322\t    start = int(\n323\t        np.searchsorted(\n324\t            timestamps,\n325\t            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n326\t            side=\"left\",\n327\t        )\n328\t    )\n329\t    result = np.zeros(6, dtype=np.float32)\n330\t    recent_items = items[start:]\n331\t    if len(recent_items):\n332\t        result[0] = np.log1p(len(recent_items)) / 8.0\n333\t        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n334\t        result[2] = float(np.mean(event_features[start:, 0]))\n335\t        result[3] = float(np.mean(event_features[start:, 1]))\n336\t    result[4] = np.log1p(like_count) / 6.0\n337\t    result[5] = np.log1p(dislike_count) / 4.0\n338\t    return result\n339\t\n340\t\n341\tdef prepare_data(\n342\t    public: Path, stage: str, config: Config\n343\t) -> tuple[SequenceData, CandidateSet]:\n344\t    data = public / \"data\"\n345\t    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n346\t        \"popularity_rank\"\n347\t    )\n348\t    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n349\t    item_index = pd.Index(item_ids)\n350\t    n_items = len(item_ids)\n351\t    if n_items >= np.iinfo(np.uint16).max:\n352\t        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n353\t\n354\t    if stage == \"validation\":\n355\t        prefixes = (\"train\",)\n356\t        history_end = base.TRAIN_END\n357\t        candidate_name = \"validation_eligible_candidates.npz\"\n358\t        state_name = \"validation_preference_state.parquet\"\n359\t    else:\n360\t        prefixes = (\"train\", \"val\")\n361\t        history_end = base.VALIDATION_END\n362\t        candidate_name = \"test_eligible_candidates.npz\"\n363\t        state_name = \"test_preference_state.parquet\"\n364\t    eligibility = CandidateSet.read(data / candidate_name)\n365\t    if not np.array_equal(eligibility.item_ids, item_ids):\n366\t        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n367\t\n368\t    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n369\t    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n370\t        base.load_inference_state(\n371\t            data / state_name,\n372\t            item_index,\n373\t            eligibility.uids,\n374\t            n_items,\n375\t        )\n376\t    )\n377\t    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n378\t    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n379\t    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n380\t\n381\t    # Snapshot zero is the shared empty explicit state.\n382\t    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n383\t    disliked_states = [\n384\t        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n385\t    ]\n386\t    state_counts: list[tuple[int, int]] = [(0, 0)]\n387\t    user_ids: list[int] = []\n388\t    offsets = [0]\n389\t    timestamp_parts: list[np.ndarray] = []\n390\t    item_parts: list[np.ndarray] = []\n391\t    event_parts: list[np.ndarray] = []\n392\t    activity_parts: list[np.ndarray] = []\n393\t    state_parts: list[np.ndarray] = []\n394\t\n395\t    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n396\t    started = time.monotonic()\n397\t    for seen_users, (uid, listens) in enumerate(\n398\t        base.merge_listen_streams(listen_paths, item_index), start=1\n399\t    ):\n400\t        parts = build_sequence_parts(\n401\t            listens,\n402\t            explicit_by_user.get(uid, base.empty_explicit_events()),\n403\t            n_items,\n404\t            liked_states,\n405\t            disliked_states,\n406\t            state_counts,\n407\t        )\n408\t        if parts is None:\n409\t            continue\n410\t        sequence_row = len(user_ids)\n411\t        user_ids.append(uid)\n412\t        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n413\t        item_parts.append(parts[1])\n414\t        event_parts.append(parts[2])\n415\t        activity_parts.append(parts[3])\n416\t        state_parts.append(parts[4])\n417\t        offsets.append(offsets[-1] + len(parts[1]))\n418\t        inference_row = inference_rows.get(uid)\n419\t        if inference_row is not None:\n420\t            inference_sequence_rows[inference_row] = sequence_row\n421\t            inference_user_features[inference_row] = final_user_features(\n422\t                parts[0],\n423\t                parts[1],\n424\t                parts[2],\n425\t                history_end,\n426\t                int(like_counts[inference_row]),\n427\t                int(dislike_counts[inference_row]),\n428\t            )\n429\t        if seen_users % 1_000 == 0:\n430\t            log(\n431\t                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n432\t                f\"states={len(liked_states):,} \"\n433\t                f\"elapsed={time.monotonic() - started:.1f}s\"\n434\t            )\n435\t\n436\t    missing = np.flatnonzero(inference_sequence_rows < 0)\n437\t    if len(missing):\n438\t        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n439\t        inference_user_features[missing, 5] = (\n440\t            np.log1p(dislike_counts[missing]) / 4.0\n441\t        )\n442\t        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n443\t\n444\t    prepared = SequenceData(\n445\t        item_ids=item_ids,\n446\t        item_features=base.build_item_features(catalog),\n447\t        user_ids=np.asarray(user_ids, dtype=np.int64),\n448\t        offsets=np.asarray(offsets, dtype=np.int64),\n449\t        sequence_timestamps=np.concatenate(timestamp_parts),\n450\t        sequence_items=np.concatenate(item_parts),\n451\t        event_features=np.concatenate(event_parts),\n452\t        activity_features=np.concatenate(activity_parts),\n453\t        state_ids=np.concatenate(state_parts),\n454\t        liked_states=np.stack(liked_states),\n455\t        disliked_states=np.stack(disliked_states),\n456\t        state_counts=np.asarray(state_counts, dtype=np.int32),\n457\t        inference_user_rows=inference_sequence_rows,\n458\t        inference_liked_items=inference_likes,\n459\t        inference_disliked_items=inference_dislikes,\n460\t        inference_user_features=inference_user_features,\n461\t    )\n462\t    log(\n463\t        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n464\t        f\"tokens={len(prepared.sequence_items):,} \"\n465\t        f\"next_item_targets={prepared.target_count:,} \"\n466\t        f\"states={len(prepared.liked_states):,}\"\n467\t    )\n468\t    return prepared, eligibility\n469\t\n470\t\n471\tdef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n472\t    return {\n473\t        \"cache_version\": CACHE_VERSION,\n474\t        \"stage\": stage,\n475\t        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n476\t        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n477\t        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n478\t    }\n479\t\n480\t\n481\tdef save_cache(\n482\t    path: Path, prepared: SequenceData, stage: str, config: Config\n483\t) -> None:\n484\t    path.parent.mkdir(parents=True, exist_ok=True)\n485\t    np.savez(\n486\t        path,\n487\t        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n488\t        **prepared.__dict__,\n489\t    )\n490\t    log(f\"saved public-derived full-sequence cache: {path}\")\n491\t\n492\t\n493\tdef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n494\t    with np.load(path, allow_pickle=False) as artifact:\n495\t        actual = json.loads(str(artifact[\"metadata\"].item()))\n496\t        expected = cache_metadata(stage, config)\n497\t        if actual != expected:\n498\t            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n499\t        values = {\n500\t            field: artifact[field].copy()\n501\t            for field in SequenceData.__dataclass_fields__\n502\t        }\n503\t    prepared = SequenceData(**values)\n504\t    log(\n505\t        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n506\t        f\"targets={prepared.target_count:,}\"\n507\t    )\n508\t    return prepared\n509\t\n510\t\n511\tdef load_or_prepare(\n512\t    public: Path,\n513\t    stage: str,\n514\t    config: Config,\n515\t    cache: Path | None,\n516\t) -> tuple[SequenceData, CandidateSet]:\n517\t    candidate_name = (\n518\t        \"validation_eligible_candidates.npz\"\n519\t        if stage == \"validation\"\n520\t        else \"test_eligible_candidates.npz\"\n521\t    )\n522\t    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n523\t    if cache is not None and cache.is_file():\n524\t        prepared = load_cache(cache, stage, config)\n525\t        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n526\t            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n527\t        return prepared, eligibility\n528\t    prepared, built_eligibility = prepare_data(public, stage, config)\n529\t    if cache is not None:\n530\t        save_cache(cache, prepared, stage, config)\n531\t    return prepared, built_eligibility\n532\t\n533\t\n534\tdef build_item_correction(\n535\t    prepared: SequenceData,\n536\t    config: Config,\n537\t    target_starts: np.ndarray,\n538\t    target_counts: np.ndarray,\n539\t) -> torch.Tensor:\n540\t    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n541\t    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n542\t    for user_row, count in enumerate(target_counts):\n543\t        if not count:\n544\t            continue\n545\t        offset = int(prepared.offsets[user_row])\n546\t        target_start = int(target_starts[user_row])\n547\t        counts += np.bincount(\n548\t            prepared.sequence_items[\n549\t                offset\n550\t                + target_start\n551\t                + 1 : offset\n552\t                + target_start\n553\t                + int(count)\n554\t                + 1\n555\t            ].astype(np.int64),\n556\t            minlength=len(prepared.item_ids),\n557\t        )\n558\t    total = float(counts.sum())\n559\t    probabilities = (counts + 1.0) / (total + len(counts))\n560\t    raw = np.power(\n561\t        (1.0 / len(counts)) / probabilities,\n562\t        config.inbatch_correction_power,\n563\t    )\n564\t    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n565\t    correction = np.clip(\n566\t        raw / max(normalization, 1.0e-12),\n567\t        config.inbatch_correction_min,\n568\t        config.inbatch_correction_max,\n569\t    ).astype(np.float32)\n570\t    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n571\t    log(\n572\t        f\"inbatch correction: min={correction.min():.3f} \"\n573\t        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n574\t    )\n575\t    return torch.from_numpy(correction)\n576\t\n577\t\n578\tdef build_known_positive_filter(\n579\t    prepared: SequenceData, training_counts: np.ndarray\n580\t) -> np.ndarray:\n581\t    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n582\t    width = (len(prepared.item_ids) + 7) // 8\n583\t    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n584\t    retained = 0\n585\t    for user_row, target_count in enumerate(training_counts):\n586\t        if not target_count:\n587\t            continue\n588\t        offset = int(prepared.offsets[user_row])\n589\t        # T adjacent targets consume sequence positions 0 through T.  The\n590\t        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n591\t        items = np.unique(\n592\t            prepared.sequence_items[\n593\t                offset : offset + int(target_count) + 1\n594\t            ].astype(np.int64)\n595\t        )\n596\t        byte_columns = items >> 3\n597\t        bit_values = np.left_shift(\n598\t            np.uint8(1), (items & 7).astype(np.uint8)\n599\t        )\n600\t        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n601\t        retained += len(items)\n602\t    log(\n603\t        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n604\t        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n605\t    )\n606\t    return packed\n607\t\n608\t\n609\tdef invalid_full_history_negatives(\n610\t    negatives: np.ndarray,\n611\t    targets: np.ndarray,\n612\t    user_rows: np.ndarray,\n613\t    known_positive_packed: np.ndarray,\n614\t    liked_items: np.ndarray,\n615\t) -> np.ndarray:\n616\t    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617\t    byte_columns = negatives >> 3\n618\t    bit_columns = negatives & 7\n619\t    known = known_positive_packed[user_rows[:, None], byte_columns]\n620\t    invalid = ((known >> bit_columns) & 1).astype(bool)\n621\t    invalid |= negatives == targets[:, None]\n622\t    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623\t    for column in range(1, negatives.shape[1]):\n624\t        invalid[:, column] |= (\n625\t            negatives[:, column, None] == negatives[:, :column]\n626\t        ).any(axis=1)\n627\t    return invalid\n628\t\n629\t\n630\tdef draw_full_history_filtered_negatives(\n631\t    targets: torch.Tensor,\n632\t    user_rows: np.ndarray,\n633\t    liked_items: torch.Tensor,\n634\t    known_positive_packed: np.ndarray,\n635\t    n_items: int,\n636\t    config: Config,\n637\t    item_correction: torch.Tensor,\n638\t    generator: torch.Generator,\n639\t    device: torch.device,\n640\t) -> tuple[torch.Tensor, torch.Tensor]:\n641\t    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n642\t    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n643\t    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n644\t    batch_size = len(targets_cpu)\n645\t    uniform = torch.randint(\n646\t        n_items,\n647\t        (batch_size, config.uniform_negatives),\n648\t        generator=generator,\n649\t        device=\"cpu\",\n650\t    )\n651\t    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n652\t    corrected_count = 0\n653\t    if config.inbatch_negatives:\n654\t        unique_targets = torch.unique(targets.detach().cpu())\n655\t        corrected_count = min(\n656\t            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n657\t        )\n658\t        if corrected_count:\n659\t            priorities = torch.rand(\n660\t                (batch_size, len(unique_targets)), generator=generator\n661\t            )\n662\t            priorities.masked_fill_(\n663\t                unique_targets[None, :].eq(\n664\t                    torch.from_numpy(targets_cpu)[:, None]\n665\t                ),\n666\t                -1.0,\n667\t            )\n668\t            columns = torch.topk(\n669\t                priorities, k=corrected_count, dim=1\n670\t            ).indices\n671\t            inbatch = unique_targets[columns]\n672\t        if corrected_count < config.inbatch_negatives:\n673\t            filler = torch.randint(\n674\t                n_items,\n675\t                (batch_size, config.inbatch_negatives - corrected_count),\n676\t                generator=generator,\n677\t                device=\"cpu\",\n678\t            )\n679\t            inbatch = torch.cat([inbatch, filler], dim=1)\n680\t\n681\t    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682\t    correction = np.ones_like(negatives, dtype=np.float32)\n683\t    if corrected_count:\n684\t        correction[\n685\t            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n686\t        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n687\t    for _ in range(32):\n688\t        invalid = invalid_full_history_negatives(\n689\t            negatives,\n690\t            targets_cpu,\n691\t            user_rows,\n692\t            known_positive_packed,\n693\t            liked_cpu,\n694\t        )\n695\t        if not np.any(invalid):\n696\t            return (\n697\t                torch.from_numpy(negatives).to(device),\n698\t                torch.from_numpy(correction).to(device),\n699\t            )\n700\t        replacements = torch.randint(\n701\t            n_items,\n702\t            (int(invalid.sum()),),\n703\t            generator=generator,\n704\t            device=\"cpu\",\n705\t        ).numpy()\n706\t        negatives[invalid] = replacements\n707\t        # Rejected in-batch items become ordinary uniform draws.\n708\t        correction[invalid] = 1.0\n709\t    invalid = invalid_full_history_negatives(\n710\t        negatives,\n711\t        targets_cpu,\n712\t        user_rows,\n713\t        known_positive_packed,\n714\t        liked_cpu,\n715\t    )\n716\t    if np.any(invalid):\n717\t        raise RuntimeError(\"full-history negative resampling failed\")\n718\t    return (\n719\t        torch.from_numpy(negatives).to(device),\n720\t        torch.from_numpy(correction).to(device),\n721\t    )\n722\t\n723\t\n724\tdef draw_training_negatives(\n725\t    positive_items: torch.Tensor,\n726\t    known_history: torch.Tensor,\n727\t    liked_items: torch.Tensor,\n728\t    user_rows: np.ndarray,\n729\t    known_positive_packed: np.ndarray | None,\n730\t    prepared: SequenceData,\n731\t    config: Config,\n732\t    item_correction: torch.Tensor,\n733\t    generator: torch.Generator,\n734\t    device: torch.device,\n735\t) -> tuple[torch.Tensor, torch.Tensor]:\n736\t    if config.negative_filter_scope == \"full-training-history\":\n737\t        if known_positive_packed is None:\n738\t            raise RuntimeError(\"full-history negative filter was not built\")\n739\t        return draw_full_history_filtered_negatives(\n740\t            positive_items,\n741\t            user_rows,\n742\t            liked_items,\n743\t            known_positive_packed,\n744\t            len(prepared.item_ids),\n745\t            config,\n746\t            item_correction,\n747\t            generator,\n748\t            device,\n749\t        )\n750\t    return base.draw_filtered_mixed_negatives(\n751\t        positive_items,\n752\t        known_history,\n753\t        liked_items,\n754\t        len(prepared.item_ids),\n755\t        config.uniform_negatives,\n756\t        config.inbatch_negatives,\n757\t        item_correction,\n758\t        generator,\n759\t        device,\n760\t    )\n761\t\n762\t\n763\tdef event_sequence(\n764\t    model: base.SequentialTwoTower,\n765\t    items: torch.Tensor,\n766\t    features: torch.Tensor,\n767\t    hidden: torch.Tensor,\n768\t) -> tuple[torch.Tensor, torch.Tensor]:\n769\t    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n770\t    projected = model.event_projection(inputs)\n771\t    return model.sequence_encoder(projected, hidden)\n772\t\n773\t\n774\tdef pool_explicit_states(\n775\t    model: base.SequentialTwoTower,\n776\t    prepared: SequenceData,\n777\t    state_ids: np.ndarray,\n778\t    device: torch.device,\n779\t) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n780\t    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n781\t    liked_items = torch.from_numpy(\n782\t        prepared.liked_states[unique_states].astype(np.int64)\n783\t    ).to(device)\n784\t    disliked_items = torch.from_numpy(\n785\t        prepared.disliked_states[unique_states].astype(np.int64)\n786\t    ).to(device)\n787\t    liked_vectors = model._mean_context_channel(liked_items)\n788\t    disliked_vectors = model._mean_context_channel(disliked_items)\n789\t    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n790\t    counts = prepared.state_counts[state_ids]\n791\t    return (\n792\t        liked_vectors[inverse_rows],\n793\t        disliked_vectors[inverse_rows],\n794\t        torch.from_numpy(counts.astype(np.float32)).to(device),\n795\t    )\n796\t\n797\t\n798\tdef user_queries(\n799\t    model: base.SequentialTwoTower,\n800\t    sequence_vectors: torch.Tensor,\n801\t    liked_vectors: torch.Tensor,\n802\t    disliked_vectors: torch.Tensor,\n803\t    user_features: torch.Tensor,\n804\t) -> torch.Tensor:\n805\t    return model.user_tower(\n806\t        torch.cat(\n807\t            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n808\t            dim=1,\n809\t        )\n810\t    )\n811\t\n812\t\n813\tdef candidate_vectors(\n814\t    model: base.SequentialTwoTower,\n815\t    positives: torch.Tensor,\n816\t    negatives: torch.Tensor,\n817\t) -> tuple[torch.Tensor, torch.Tensor]:\n818\t    all_items = torch.cat([positives, negatives.reshape(-1)])\n819\t    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n820\t    encoded = model.encode_items(unique_items)\n821\t    positive_vectors = encoded[inverse[: len(positives)]]\n822\t    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823\t        len(positives), negatives.shape[1], -1\n824\t    )\n825\t    return positive_vectors, negative_vectors\n826\t\n827\t\n828\tdef history_windows(\n829\t    sequence: np.ndarray,\n830\t    start: int,\n831\t    count: int,\n832\t    width: int,\n833\t    padding_item: int,\n834\t) -> np.ndarray:\n835\t    base_start = max(0, start - width + 1)\n836\t    segment = sequence[base_start : start + count]\n837\t    padded = np.concatenate(\n838\t        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n839\t    )\n840\t    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n841\t    offset = start - base_start\n842\t    return windows[offset : offset + count].copy()\n843\t\n844\t\n845\tdef corrected_negative_loss(\n846\t    logits: torch.Tensor,\n847\t    correction: torch.Tensor,\n848\t    config: Config,\n849\t) -> torch.Tensor:\n850\t    raw = F.softplus(logits)\n851\t    total = config.uniform_negatives + config.inbatch_negatives\n852\t    result = torch.zeros(len(logits), device=logits.device)\n853\t    if config.uniform_negatives:\n854\t        result += config.uniform_negatives * raw[\n855\t            :, : config.uniform_negatives\n856\t        ].mean(dim=1)\n857\t    if config.inbatch_negatives:\n858\t        selected = slice(config.uniform_negatives, total)\n859\t        weights = correction[:, selected]\n860\t        result += config.inbatch_negatives * (\n861\t            (raw[:, selected] * weights).sum(dim=1)\n862\t            / weights.sum(dim=1).clamp_min(1.0e-6)\n863\t        )\n864\t    return result / total\n865\t\n866\t\n867\tdef retained_target_window_starts(\n868\t    total_targets: np.ndarray,\n869\t    validation_counts: np.ndarray,\n870\t    training_counts: np.ndarray,\n871\t) -> np.ndarray:\n872\t    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n873\t    training_region_ends = total_targets - validation_counts\n874\t    window_starts = training_region_ends - training_counts\n875\t    if (\n876\t        np.any(window_starts < 0)\n877\t        or np.any(training_counts < 0)\n878\t        or np.any(validation_counts < 0)\n879\t    ):\n880\t        raise RuntimeError(\"chronological target partition produced negative counts\")\n881\t    if not np.array_equal(\n882\t        window_starts + training_counts + validation_counts, total_targets\n883\t    ):\n884\t        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n885\t    return window_starts\n886\t\n887\t\n888\tdef chronological_target_partition(\n889\t    prepared: SequenceData,\n890\t    validation_fraction: float,\n891\t    max_train_targets_per_user: int,\n892\t    max_validation_targets_per_user: int,\n893\t) -> TargetPartition:\n894\t    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n895\t    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n896\t    validation = np.floor(totals * validation_fraction).astype(np.int64)\n897\t    validation[(totals >= 2) & (validation == 0)] = 1\n898\t    if max_validation_targets_per_user:\n899\t        validation = np.minimum(validation, max_validation_targets_per_user)\n900\t\n901\t    history_training = totals - validation\n902\t    training = history_training.copy()\n903\t    if max_train_targets_per_user:\n904\t        training = np.minimum(training, max_train_targets_per_user)\n905\t    window_starts = retained_target_window_starts(totals, validation, training)\n906\t    return TargetPartition(\n907\t        window_starts=window_starts,\n908\t        training_counts=training,\n909\t        validation_counts=validation,\n910\t        history_training_counts=history_training,\n911\t    )\n912\t\n913\t\n914\tdef load_organic_like_outcomes(\n915\t    public: Path, stage: str, item_ids: np.ndarray\n916\t) -> dict[int, dict[int, np.ndarray]]:\n917\t    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n918\t    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n919\t    frames = [\n920\t        pd.read_parquet(\n921\t            public / \"data\" / f\"{prefix}_likes.parquet\",\n922\t            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n923\t        )\n924\t        for prefix in prefixes\n925\t    ]\n926\t    likes = pd.concat(frames, ignore_index=True)\n927\t    likes = likes[likes.is_organic == 1].copy()\n928\t    mapped = pd.Index(item_ids).get_indexer(\n929\t        likes.item_id.to_numpy(dtype=np.int64)\n930\t    )\n931\t    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n932\t    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n933\t    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n934\t    nested: dict[int, dict[int, list[int]]] = {}\n935\t    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n936\t        index=False, name=None\n937\t    ):\n938\t        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n939\t            int(raw_timestamp)\n940\t        )\n941\t    result = {\n942\t        uid: {\n943\t            item: np.asarray(timestamps, dtype=np.int64)\n944\t            for item, timestamps in items.items()\n945\t        }\n946\t        for uid, items in nested.items()\n947\t    }\n948\t    log(\n949\t        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n950\t        f\"users={len(result):,}\"\n951\t    )\n952\t    return result\n953\t\n954\t\n955\tdef build_target_weights(\n956\t    public: Path,\n957\t    stage: str,\n958\t    prepared: SequenceData,\n959\t    training_counts: np.ndarray,\n960\t    config: Config,\n961\t) -> np.ndarray:\n962\t    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n963\t    if config.target_weighting == \"uniform\":\n964\t        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n965\t    else:\n966\t        weights = np.full(\n967\t            len(prepared.sequence_items),\n968\t            config.ordinary_target_weight,\n969\t            dtype=np.float32,\n970\t        )\n971\t        organic_strong = (\n972\t            (prepared.event_features[:, 0] > 0.5)\n973\t            & (prepared.event_features[:, 1] >= 0.8)\n974\t        )\n975\t        weights[organic_strong] = config.organic_strong_target_weight\n976\t        if config.target_weighting == \"discovery-graded\":\n977\t            like_outcomes = load_organic_like_outcomes(\n978\t                public, stage, prepared.item_ids\n979\t            )\n980\t            history_end = (\n981\t                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n982\t            )\n983\t            horizon = config.discovery_horizon_days * base.DAY\n984\t            grade_three = 0\n985\t            for user_row, uid in enumerate(prepared.user_ids):\n986\t                start = int(prepared.offsets[user_row])\n987\t                end = int(prepared.offsets[user_row + 1])\n988\t                if end - start < 2:\n989\t                    continue\n990\t                last_training_target = start + int(training_counts[user_row])\n991\t                first_validation_target = last_training_target + 1\n992\t                split_timestamp = (\n993\t                    int(prepared.sequence_timestamps[first_validation_target])\n994\t                    if first_validation_target < end\n995\t                    else history_end\n996\t                )\n997\t                user_likes = like_outcomes.get(int(uid), {})\n998\t                seen = {int(prepared.sequence_items[start])}\n999\t                for position in range(start + 1, end):\n1000\t                    item = int(prepared.sequence_items[position])\n1001\t                    novel = item not in seen\n1002\t                    seen.add(item)\n1003\t                    if not novel:\n1004\t                        # Repeats remain training examples, but they cannot be\n1005\t                        # eligible discovery outcomes at the corresponding\n1006\t                        # cutoff and therefore keep only the ordinary weight.\n1007\t                        weights[position] = config.ordinary_target_weight\n1008\t                        continue\n1009\t                    timestamps = user_likes.get(item)\n1010\t                    if timestamps is None:\n1011\t                        continue\n1012\t                    timestamp = int(prepared.sequence_timestamps[position])\n1013\t                    upper = min(\n1014\t                        timestamp + horizon,\n1015\t                        split_timestamp\n1016\t                        if position <= last_training_target\n1017\t                        else history_end,\n1018\t                    )\n1019\t                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n1020\t                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n1021\t                        weights[position] = config.organic_like_target_weight\n1022\t                        grade_three += 1\n1023\t            log(\n1024\t                f\"grade-3 first-encounter targets={grade_three:,} \"\n1025\t                f\"horizon_days={config.discovery_horizon_days}\"\n1026\t            )\n1027\t    log(\n1028\t        f\"target weighting={config.target_weighting} \"\n1029\t        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n1030\t        f\"max={weights.max():.3f}\"\n1031\t    )\n1032\t    return weights\n1033\t\n1034\t\n1035\tdef build_dense_block(\n1036\t    prepared: SequenceData,\n1037\t    target_weights: np.ndarray,\n1038\t    users: np.ndarray,\n1039\t    window_starts: np.ndarray,\n1040\t    process_counts: np.ndarray,\n1041\t    score_starts: np.ndarray,\n1042\t    start: int,\n1043\t    steps: int,\n1044\t    config: Config,\n1045\t) -> tuple[np.ndarray, ...]:\n1046\t    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n1047\t    batch_size = len(users)\n1048\t    padding_item = len(prepared.item_ids)\n1049\t    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n1050\t    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n1051\t    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n1052\t    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n1053\t    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n1054\t    weights = np.ones((batch_size, steps), dtype=np.float32)\n1055\t    scored = np.zeros((batch_size, steps), dtype=bool)\n1056\t    histories = np.full(\n1057\t        (batch_size, steps, config.bptt_steps),\n1058\t        padding_item,\n1059\t        dtype=np.uint16,\n1060\t    )\n1061\t    processed = np.zeros(batch_size, dtype=np.int32)\n1062\t\n1063\t    lengths = prepared.lengths\n1064\t    for row, user_row in enumerate(users):\n1065\t        count = min(steps, max(int(process_counts[row]) - start, 0))\n1066\t        if not count:\n1067\t            continue\n1068\t        offset = int(prepared.offsets[user_row])\n1069\t        window_start = int(window_starts[row])\n1070\t        absolute_start = window_start + start\n1071\t        positions = slice(\n1072\t            offset + absolute_start,\n1073\t            offset + absolute_start + count,\n1074\t        )\n1075\t        target_positions = slice(\n1076\t            offset + absolute_start + 1,\n1077\t            offset + absolute_start + count + 1,\n1078\t        )\n1079\t        input_items[row, :count] = prepared.sequence_items[positions]\n1080\t        event_features[row, :count] = prepared.event_features[positions]\n1081\t        activity_features[row, :count] = prepared.activity_features[positions]\n1082\t        state_ids[row, :count] = prepared.state_ids[positions]\n1083\t        targets[row, :count] = prepared.sequence_items[target_positions]\n1084\t        weights[row, :count] = target_weights[target_positions]\n1085\t        score_from = min(count, max(int(score_starts[row]) - start, 0))\n1086\t        scored[row, score_from:count] = True\n1087\t        processed[row] = count\n1088\t        user_sequence = prepared.sequence_items[\n1089\t            offset : offset + int(lengths[user_row])\n1090\t        ]\n1091\t        histories[row, :count] = history_windows(\n1092\t            user_sequence,\n1093\t            absolute_start,\n1094\t            count,\n1095\t            config.bptt_steps,\n1096\t            padding_item,\n1097\t        )\n1098\t    return (\n1099\t        input_items,\n1100\t        event_features,\n1101\t        activity_features,\n1102\t        state_ids,\n1103\t        targets,\n1104\t        scored,\n1105\t        histories,\n1106\t        processed,\n1107\t        weights,\n1108\t    )\n1109\t\n1110\t\n1111\tdef score_target_microbatches(\n1112\t    model: base.SequentialTwoTower,\n1113\t    prepared: SequenceData,\n1114\t    config: Config,\n1115\t    sequence_vectors: torch.Tensor,\n1116\t    flat_states: np.ndarray,\n1117\t    flat_activity: np.ndarray,\n1118\t    positive_items: torch.Tensor,\n1119\t    negatives: torch.Tensor,\n1120\t    negative_weights: torch.Tensor,\n1121\t    sample_weights: np.ndarray,\n1122\t    device: torch.device,\n1123\t    backward: bool,\n1124\t) -> tuple[dict[str, float], torch.Tensor | None]:\n1125\t    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n1126\t    target_count = len(positive_items)\n1127\t    total_weight = float(sample_weights.sum())\n1128\t    if total_weight <= 0:\n1129\t        raise RuntimeError(\"target block has no positive sample weight\")\n1130\t    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n1131\t    statistics = {\n1132\t        \"loss_sum\": 0.0,\n1133\t        \"positive_sum\": 0.0,\n1134\t        \"negative_sum\": 0.0,\n1135\t        \"pair_correct\": 0.0,\n1136\t        \"weight_sum\": 0.0,\n1137\t    }\n1138\t    for begin in range(0, target_count, config.loss_microbatch_size):\n1139\t        end = min(begin + config.loss_microbatch_size, target_count)\n1140\t        selected_states = flat_states[begin:end]\n1141\t        if backward:\n1142\t            sequence_input = (\n1143\t                sequence_vectors[begin:end].detach().requires_grad_(True)\n1144\t            )\n1145\t        else:\n1146\t            sequence_input = sequence_vectors[begin:end]\n1147\t        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n1148\t            model, prepared, selected_states, device\n1149\t        )\n1150\t        counts = prepared.state_counts[selected_states]\n1151\t        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n1152\t        numeric[:, :4] = flat_activity[begin:end]\n1153\t        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n1154\t        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n1155\t        query_vectors = user_queries(\n1156\t            model,\n1157\t            sequence_input,\n1158\t            liked_vectors,\n1159\t            disliked_vectors,\n1160\t            torch.from_numpy(numeric).to(device),\n1161\t        )\n1162\t        positive_vectors, negative_vectors = candidate_vectors(\n1163\t            model,\n1164\t            positive_items[begin:end],\n1165\t            negatives[begin:end],\n1166\t        )\n1167\t        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n1168\t        negative_logits = torch.einsum(\n1169\t            \"bd,bkd->bk\", query_vectors, negative_vectors\n1170\t        )\n1171\t        positive_loss = F.softplus(-positive_logits)\n1172\t        negative_loss = corrected_negative_loss(\n1173\t            negative_logits, negative_weights[begin:end], config\n1174\t        )\n1175\t        selected_weights = torch.from_numpy(\n1176\t            sample_weights[begin:end].astype(np.float32, copy=False)\n1177\t        ).to(device)\n1178\t        loss_sum = (\n1179\t            selected_weights * (positive_loss + negative_loss) / 2.0\n1180\t        ).sum()\n1181\t        if not bool(torch.isfinite(loss_sum)):\n1182\t            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n1183\t        if backward:\n1184\t            (loss_sum / total_weight).backward()\n1185\t            if sequence_input.grad is None:\n1186\t                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n1187\t            sequence_gradient[begin:end] = sequence_input.grad\n1188\t        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n1189\t        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n1190\t        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1191\t        statistics[\"pair_correct\"] += float(\n1192\t            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1193\t        )\n1194\t        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n1195\t    return statistics, sequence_gradient\n1196\t\n1197\t\n1198\tdef release_accelerator_cache(device: torch.device) -> None:\n1199\t    if device.type == \"mps\":\n1200\t        torch.mps.empty_cache()\n1201\t\n1202\t\n1203\tdef run_training_epoch(\n1204\t    model: base.SequentialTwoTower,\n1205\t    optimizer: torch.optim.Optimizer,\n1206\t    prepared: SequenceData,\n1207\t    config: Config,\n1208\t    target_weights: np.ndarray,\n1209\t    window_starts: np.ndarray,\n1210\t    target_counts: np.ndarray,\n1211\t    item_correction: torch.Tensor,\n1212\t    known_positive_packed: np.ndarray | None,\n1213\t    generator: torch.Generator,\n1214\t    device: torch.device,\n1215\t    epoch: int,\n1216\t) -> dict[str, float]:\n1217\t    model.train()\n1218\t    train_users = np.flatnonzero(target_counts > 0)\n1219\t    sorted_users = train_users[\n1220\t        np.argsort(target_counts[train_users], kind=\"stable\")\n1221\t    ]\n1222\t    groups = [\n1223\t        sorted_users[start : start + config.user_batch_size]\n1224\t        for start in range(0, len(sorted_users), config.user_batch_size)\n1225\t    ]\n1226\t    group_order = torch.randperm(len(groups), generator=generator).numpy()\n1227\t    negative_count = config.uniform_negatives + config.inbatch_negatives\n1228\t    total_loss = 0.0\n1229\t    total_targets = 0\n1230\t    total_weight = 0.0\n1231\t    total_positive = 0.0\n1232\t    total_negative = 0.0\n1233\t    total_pair_correct = 0.0\n1234\t    batches = 0\n1235\t    started = time.monotonic()\n1236\t\n1237\t    for group_number in group_order:\n1238\t        users = groups[int(group_number)]\n1239\t        group_counts = target_counts[users]\n1240\t        hidden = torch.zeros(\n1241\t            1, len(users), config.embedding_dim, device=device\n1242\t        )\n1243\t        max_targets = int(group_counts.max())\n1244\t        for start in range(0, max_targets, config.bptt_steps):\n1245\t            steps = min(config.bptt_steps, max_targets - start)\n1246\t            block = build_dense_block(\n1247\t                prepared,\n1248\t                target_weights,\n1249\t                users,\n1250\t                window_starts[users],\n1251\t                group_counts,\n1252\t                np.zeros(len(users), dtype=np.int64),\n1253\t                start,\n1254\t                steps,\n1255\t                config,\n1256\t            )\n1257\t            sequence_outputs, _ = event_sequence(\n1258\t                model,\n1259\t                torch.from_numpy(block[0].astype(np.int64)).to(device),\n1260\t                torch.from_numpy(block[1].astype(np.float32)).to(device),\n1261\t                hidden,\n1262\t            )\n1263\t            hidden = torch.stack(\n1264\t                [\n1265\t                    sequence_outputs[row, count - 1]\n1266\t                    if count\n1267\t                    else hidden[0, row]\n1268\t                    for row, count in enumerate(block[7])\n1269\t                ]\n1270\t            ).unsqueeze(0).detach()\n1271\t            flat_scored = block[5].reshape(-1)\n1272\t            if not np.any(flat_scored):\n1273\t                continue\n1274\t            mask = torch.from_numpy(flat_scored).to(device)\n1275\t            sequence_vectors = sequence_outputs.reshape(\n1276\t                -1, config.embedding_dim\n1277\t            )[mask]\n1278\t            flat_states = block[3].reshape(-1)[flat_scored]\n1279\t            positive_items = torch.from_numpy(\n1280\t                block[4].reshape(-1)[flat_scored].astype(np.int64)\n1281\t            ).to(device)\n1282\t            known_history = torch.from_numpy(\n1283\t                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1284\t                    np.int64\n1285\t                )\n1286\t            ).to(device)\n1287\t            liked_items = torch.from_numpy(\n1288\t                prepared.liked_states[flat_states].astype(np.int64)\n1289\t            ).to(device)\n1290\t            flat_user_rows = np.broadcast_to(\n1291\t                users[:, None], block[5].shape\n1292\t            ).reshape(-1)[flat_scored]\n1293\t            negatives, negative_weights = draw_training_negatives(\n1294\t                positive_items,\n1295\t                known_history,\n1296\t                liked_items,\n1297\t                flat_user_rows,\n1298\t                known_positive_packed,\n1299\t                prepared,\n1300\t                config,\n1301\t                item_correction,\n1302\t                generator,\n1303\t                device,\n1304\t            )\n1305\t            optimizer.zero_grad(set_to_none=True)\n1306\t            statistics, sequence_gradient = score_target_microbatches(\n1307\t                model,\n1308\t                prepared,\n1309\t                config,\n1310\t                sequence_vectors,\n1311\t                flat_states,\n1312\t                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n1313\t                positive_items,\n1314\t                negatives,\n1315\t                negative_weights,\n1316\t                block[8].reshape(-1)[flat_scored],\n1317\t                device,\n1318\t                backward=True,\n1319\t            )\n1320\t            if sequence_gradient is None:\n1321\t                raise RuntimeError(\"training block omitted its sequence gradient\")\n1322\t            sequence_vectors.backward(sequence_gradient)\n1323\t            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n1324\t            optimizer.step()\n1325\t\n1326\t            count = len(positive_items)\n1327\t            total_loss += statistics[\"loss_sum\"]\n1328\t            total_targets += count\n1329\t            total_weight += statistics[\"weight_sum\"]\n1330\t            total_positive += statistics[\"positive_sum\"]\n1331\t            total_negative += statistics[\"negative_sum\"]\n1332\t            total_pair_correct += statistics[\"pair_correct\"]\n1333\t            batches += 1\n1334\t            if batches % 25 == 0:\n1335\t                release_accelerator_cache(device)\n1336\t            if batches % 250 == 0:\n1337\t                batch_bce = statistics[\"loss_sum\"] / max(\n1338\t                    statistics[\"weight_sum\"], 1.0e-12\n1339\t                )\n1340\t                running_bce = total_loss / max(total_weight, 1.0e-12)\n1341\t                log(\n1342\t                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n1343\t                    f\"targets={total_targets:,} \"\n1344\t                    f\"batch_bce={batch_bce:.6f} \"\n1345\t                    f\"running_bce={running_bce:.6f} \"\n1346\t                    f\"elapsed={time.monotonic() - started:.1f}s\"\n1347\t                )\n1348\t\n1349\t    return {\n1350\t        \"epoch\": float(epoch),\n1351\t        \"loss\": total_loss / max(total_weight, 1.0e-12),\n1352\t        \"positive_logit\": total_positive / max(total_targets, 1),\n1353\t        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1354\t        \"pair_accuracy\": total_pair_correct\n1355\t        / max(total_targets * negative_count, 1),\n1356\t        \"targets\": float(total_targets),\n1357\t        \"target_weight_sum\": total_weight,\n1358\t        \"batches\": float(batches),\n1359\t        \"seconds\": time.monotonic() - started,\n1360\t    }\n1361\t\n1362\t\n1363\t@torch.no_grad()\n1364\tdef evaluate_validation_bce(\n1365\t    model: base.SequentialTwoTower,\n1366\t    prepared: SequenceData,\n1367\t    config: Config,\n1368\t    target_weights: np.ndarray,\n1369\t    window_starts: np.ndarray,\n1370\t    training_counts: np.ndarray,\n1371\t    validation_counts: np.ndarray,\n1372\t    item_correction: torch.Tensor,\n1373\t    known_positive_packed: np.ndarray | None,\n1374\t    device: torch.device,\n1375\t) -> dict[str, float]:\n1376\t    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n1377\t    model.eval()\n1378\t    totals = training_counts + validation_counts\n1379\t    users_with_validation = np.flatnonzero(validation_counts > 0)\n1380\t    ordered = users_with_validation[\n1381\t        np.argsort(totals[users_with_validation], kind=\"stable\")\n1382\t    ]\n1383\t    groups = [\n1384\t        ordered[start : start + config.user_batch_size]\n1385\t        for start in range(0, len(ordered), config.user_batch_size)\n1386\t    ]\n1387\t    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n1388\t    total_loss = 0.0\n1389\t    total_targets = 0\n1390\t    total_weight = 0.0\n1391\t    started = time.monotonic()\n1392\t\n1393\t    for group_number, users in enumerate(groups, start=1):\n1394\t        group_totals = totals[users]\n1395\t        group_starts = training_counts[users]\n1396\t        hidden = torch.zeros(\n1397\t            1, len(users), config.embedding_dim, device=device\n1398\t        )\n1399\t        max_targets = int(group_totals.max())\n1400\t        for start in range(0, max_targets, config.bptt_steps):\n1401\t            steps = min(config.bptt_steps, max_targets - start)\n1402\t            block = build_dense_block(\n1403\t                prepared,\n1404\t                target_weights,\n1405\t                users,\n1406\t                window_starts[users],\n1407\t                group_totals,\n1408\t                group_starts,\n1409\t                start,\n1410\t                steps,\n1411\t                config,\n1412\t            )\n1413\t            sequence_outputs, _ = event_sequence(\n1414\t                model,\n1415\t                torch.from_numpy(block[0].astype(np.int64)).to(device),\n1416\t                torch.from_numpy(block[1].astype(np.float32)).to(device),\n1417\t                hidden,\n1418\t            )\n1419\t            hidden = torch.stack(\n1420\t                [\n1421\t                    sequence_outputs[row, count - 1]\n1422\t                    if count\n1423\t                    else hidden[0, row]\n1424\t                    for row, count in enumerate(block[7])\n1425\t                ]\n1426\t            ).unsqueeze(0)\n1427\t            flat_scored = block[5].reshape(-1)\n1428\t            if not np.any(flat_scored):\n1429\t                continue\n1430\t            mask = torch.from_numpy(flat_scored).to(device)\n1431\t            sequence_vectors = sequence_outputs.reshape(\n1432\t                -1, config.embedding_dim\n1433\t            )[mask]\n1434\t            flat_states = block[3].reshape(-1)[flat_scored]\n1435\t            positive_items = torch.from_numpy(\n1436\t                block[4].reshape(-1)[flat_scored].astype(np.int64)\n1437\t            ).to(device)\n1438\t            known_history = torch.from_numpy(\n1439\t                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n1440\t                    np.int64\n1441\t                )\n1442\t            ).to(device)\n1443\t            liked_items = torch.from_numpy(\n1444\t                prepared.liked_states[flat_states].astype(np.int64)\n1445\t            ).to(device)\n1446\t            flat_user_rows = np.broadcast_to(\n1447\t                users[:, None], block[5].shape\n1448\t            ).reshape(-1)[flat_scored]\n1449\t            negatives, negative_weights = draw_training_negatives(\n1450\t                positive_items,\n1451\t                known_history,\n1452\t                liked_items,\n1453\t                flat_user_rows,\n1454\t                known_positive_packed,\n1455\t                prepared,\n1456\t                config,\n1457\t                item_correction,\n1458\t                generator,\n1459\t                device,\n1460\t            )\n1461\t            statistics, _ = score_target_microbatches(\n1462\t                model,\n1463\t                prepared,\n1464\t                config,\n1465\t                sequence_vectors,\n1466\t                flat_states,\n1467\t                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n1468\t                positive_items,\n1469\t                negatives,\n1470\t                negative_weights,\n1471\t                block[8].reshape(-1)[flat_scored],\n1472\t                device,\n1473\t                backward=False,\n1474\t            )\n1475\t            total_loss += statistics[\"loss_sum\"]\n1476\t            total_targets += len(positive_items)\n1477\t            total_weight += statistics[\"weight_sum\"]\n1478\t        if group_number % 25 == 0:\n1479\t            release_accelerator_cache(device)\n1480\t    return {\n1481\t        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n1482\t        \"validation_targets\": float(total_targets),\n1483\t        \"validation_target_weight_sum\": total_weight,\n1484\t        \"validation_seconds\": time.monotonic() - started,\n1485\t    }\n1486\t\n1487\t\n1488\tdef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1489\t    return {\n1490\t        name: value.detach().cpu().clone()\n1491\t        for name, value in model.state_dict().items()\n1492\t    }\n1493\t\n1494\t\n1495\tdef save_epoch_checkpoint(\n1496\t    checkpoint_dir: Path,\n1497\t    epoch: int,\n1498\t    model_state: dict[str, torch.Tensor],\n1499\t    config: Config,\n1500\t    diagnostics: dict[str, float],\n1501\t    optimizer: torch.optim.Optimizer,\n1502\t    generator: torch.Generator,\n1503\t) -> Path:\n1504\t    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n1505\t    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n1506\t    torch.save(\n1507\t        {\n1508\t            \"epoch\": epoch,\n1509\t            \"config\": asdict(config),\n1510\t            \"diagnostics\": diagnostics,\n1511\t            \"model_state\": model_state,\n1512\t            \"optimizer_state\": optimizer.state_dict(),\n1513\t            \"generator_state\": generator.get_state(),\n1514\t        },\n1515\t        path,\n1516\t    )\n1517\t    log(f\"saved epoch checkpoint: {path}\")\n1518\t    return path\n1519\t\n1520\t\n1521\tdef train_model(\n1522\t    public: Path,\n1523\t    stage: str,\n1524\t    prepared: SequenceData,\n1525\t    config: Config,\n1526\t    device: torch.device,\n1527\t    checkpoint_dir: Path | None,\n1528\t    resume_checkpoint: Path | None,\n1529\t) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n1530\t    partition = chronological_target_partition(\n1531\t        prepared,\n1532\t        config.validation_fraction,\n1533\t        config.max_train_targets_per_user,\n1534\t        config.max_validation_targets_per_user,\n1535\t    )\n1536\t    training_counts = partition.training_counts\n1537\t    validation_counts = partition.validation_counts\n1538\t    log(\n1539\t        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n1540\t        f\"validation={int(validation_counts.sum()):,} \"\n1541\t        f\"selected={partition.selected_count:,} \"\n1542\t        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n1543\t        f\"all={prepared.target_count:,}\"\n1544\t    )\n1545\t    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n1546\t        device\n1547\t    )\n1548\t    optimizer = torch.optim.AdamW(\n1549\t        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n1550\t    )\n1551\t    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n1552\t    item_correction = build_item_correction(\n1553\t        prepared,\n1554\t        config,\n1555\t        partition.window_starts,\n1556\t        training_counts,\n1557\t    )\n1558\t    target_weights = build_target_weights(\n1559\t        public,\n1560\t        stage,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:38.859Z",
          "data": {
            "type": "read_truncation_notice",
            "banner": "[Truncated: PARTIAL view — /task/current_model/train_current_model.py: showing lines 1-1560 of 2165 total (29478 tokens, cap 25000). Call Read with offset=1561 limit=1560 for the next page, or Grep to find a specific section. Do NOT answer from this page alone if the answer may be further in the file.]",
            "toolUseID": "toolu_01LZU4cpP86eENx4YPvqm729"
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:38.863Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14981253 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 3,
      "role": "agent",
      "timestamp": "2026-09-24T17:36:42.731Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:36:42.731Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:43.617Z",
          "callId": "toolu_014ktMQebQsxqktKY8L2SzoV",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/train_current_model.py",
            "offset": 1561,
            "limit": 620
          },
          "result": {
            "timestamp": "2026-09-24T17:36:43.623Z",
            "content": "1561\t        prepared,\n1562\t        partition.history_training_counts,\n1563\t        config,\n1564\t    )\n1565\t    known_positive_packed = (\n1566\t        build_known_positive_filter(prepared, partition.history_training_counts)\n1567\t        if config.negative_filter_scope == \"full-training-history\"\n1568\t        else None\n1569\t    )\n1570\t    history: list[dict[str, float]] = []\n1571\t    best_loss = float(\"inf\")\n1572\t    best_epoch = 0\n1573\t    best_state: dict[str, torch.Tensor] | None = None\n1574\t    epochs_without_improvement = 0\n1575\t    start_epoch = 0\n1576\t\n1577\t    if resume_checkpoint is not None:\n1578\t        payload = torch.load(\n1579\t            resume_checkpoint, map_location=device, weights_only=False\n1580\t        )\n1581\t        stored_config = payload.get(\"config\", {})\n1582\t        architecture_fields = (\n1583\t            \"bptt_steps\",\n1584\t            \"embedding_dim\",\n1585\t            \"hidden_dim\",\n1586\t            \"user_hidden_1\",\n1587\t            \"user_hidden_2\",\n1588\t            \"item_hidden_1\",\n1589\t            \"item_hidden_2\",\n1590\t        )\n1591\t        mismatches = {\n1592\t            name: (stored_config.get(name), getattr(config, name))\n1593\t            for name in architecture_fields\n1594\t            if stored_config.get(name) != getattr(config, name)\n1595\t        }\n1596\t        if mismatches:\n1597\t            raise RuntimeError(\n1598\t                f\"resume checkpoint architecture differs: {mismatches}\"\n1599\t            )\n1600\t        stored_filter_scope = stored_config.get(\n1601\t            \"negative_filter_scope\", \"last-100\"\n1602\t        )\n1603\t        stored_target_weighting = stored_config.get(\n1604\t            \"target_weighting\", \"uniform\"\n1605\t        )\n1606\t        stored_weight_values = (\n1607\t            stored_config.get(\"ordinary_target_weight\", 1.0),\n1608\t            stored_config.get(\"organic_strong_target_weight\", 1.0),\n1609\t            stored_config.get(\"organic_like_target_weight\", 3.0),\n1610\t            stored_config.get(\"discovery_horizon_days\", 7),\n1611\t        )\n1612\t        current_weight_values = (\n1613\t            config.ordinary_target_weight,\n1614\t            config.organic_strong_target_weight,\n1615\t            config.organic_like_target_weight,\n1616\t            config.discovery_horizon_days,\n1617\t        )\n1618\t        stored_window_values = (\n1619\t            stored_config.get(\"validation_fraction\", 0.10),\n1620\t            stored_config.get(\"max_train_targets_per_user\", 0),\n1621\t            stored_config.get(\"max_validation_targets_per_user\", 0),\n1622\t        )\n1623\t        current_window_values = (\n1624\t            config.validation_fraction,\n1625\t            config.max_train_targets_per_user,\n1626\t            config.max_validation_targets_per_user,\n1627\t        )\n1628\t        objective_changed = (\n1629\t            stored_filter_scope != config.negative_filter_scope\n1630\t            or stored_target_weighting != config.target_weighting\n1631\t            or stored_weight_values != current_weight_values\n1632\t            or stored_window_values != current_window_values\n1633\t        )\n1634\t        model.load_state_dict(payload[\"model_state\"])\n1635\t        if \"optimizer_state\" in payload and not objective_changed:\n1636\t            optimizer.load_state_dict(payload[\"optimizer_state\"])\n1637\t            for group in optimizer.param_groups:\n1638\t                group[\"lr\"] = config.learning_rate\n1639\t                group[\"weight_decay\"] = config.weight_decay\n1640\t            log(f\"restored optimizer state from {resume_checkpoint}\")\n1641\t        else:\n1642\t            log(\n1643\t                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n1644\t            )\n1645\t        if \"generator_state\" in payload and not objective_changed:\n1646\t            generator.set_state(payload[\"generator_state\"])\n1647\t        start_epoch = int(payload[\"epoch\"])\n1648\t        if start_epoch >= config.epochs:\n1649\t            raise RuntimeError(\n1650\t                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n1651\t            )\n1652\t        prior = {\n1653\t            str(name): float(value)\n1654\t            for name, value in payload.get(\"diagnostics\", {}).items()\n1655\t        }\n1656\t        if objective_changed:\n1657\t            prior.pop(\"validation_loss\", None)\n1658\t            log(\n1659\t                \"BCE objective changed: filter \"\n1660\t                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1661\t                \"target weighting \"\n1662\t                f\"{stored_target_weighting}->{config.target_weighting}, \"\n1663\t                f\"target window {stored_window_values}->{current_window_values}; \"\n1664\t                \"recomputing the resume checkpoint's validation BCE\"\n1665\t            )\n1666\t        if \"validation_loss\" not in prior:\n1667\t            prior.update(\n1668\t                evaluate_validation_bce(\n1669\t                    model,\n1670\t                    prepared,\n1671\t                    config,\n1672\t                    target_weights,\n1673\t                    partition.window_starts,\n1674\t                    training_counts,\n1675\t                    validation_counts,\n1676\t                    item_correction,\n1677\t                    known_positive_packed,\n1678\t                    device,\n1679\t                )\n1680\t            )\n1681\t        history.append(prior)\n1682\t        best_loss = float(prior[\"validation_loss\"])\n1683\t        best_epoch = start_epoch\n1684\t        best_state = snapshot_model(model)\n1685\t        log(\n1686\t            f\"resumed validation-BCE training: epoch={start_epoch} \"\n1687\t            f\"loss={best_loss:.6f}\"\n1688\t        )\n1689\t\n1690\t    for epoch in range(start_epoch + 1, config.epochs + 1):\n1691\t        diagnostics = run_training_epoch(\n1692\t            model,\n1693\t            optimizer,\n1694\t            prepared,\n1695\t            config,\n1696\t            target_weights,\n1697\t            partition.window_starts,\n1698\t            training_counts,\n1699\t            item_correction,\n1700\t            known_positive_packed,\n1701\t            generator,\n1702\t            device,\n1703\t            epoch,\n1704\t        )\n1705\t        validation = evaluate_validation_bce(\n1706\t            model,\n1707\t            prepared,\n1708\t            config,\n1709\t            target_weights,\n1710\t            partition.window_starts,\n1711\t            training_counts,\n1712\t            validation_counts,\n1713\t            item_correction,\n1714\t            known_positive_packed,\n1715\t            device,\n1716\t        )\n1717\t        diagnostics.update(validation)\n1718\t        history.append(diagnostics)\n1719\t        current_state = snapshot_model(model)\n1720\t        if checkpoint_dir is not None:\n1721\t            save_epoch_checkpoint(\n1722\t                checkpoint_dir,\n1723\t                epoch,\n1724\t                current_state,\n1725\t                config,\n1726\t                diagnostics,\n1727\t                optimizer,\n1728\t                generator,\n1729\t            )\n1730\t        improved = (\n1731\t            diagnostics[\"validation_loss\"]\n1732\t            < best_loss - config.early_stopping_min_delta\n1733\t        )\n1734\t        if improved:\n1735\t            best_loss = diagnostics[\"validation_loss\"]\n1736\t            best_epoch = epoch\n1737\t            best_state = current_state\n1738\t            epochs_without_improvement = 0\n1739\t            if checkpoint_dir is not None:\n1740\t                torch.save(\n1741\t                    {\n1742\t                        \"epoch\": epoch,\n1743\t                        \"config\": asdict(config),\n1744\t                        \"diagnostics\": diagnostics,\n1745\t                        \"model_state\": current_state,\n1746\t                        \"optimizer_state\": optimizer.state_dict(),\n1747\t                        \"generator_state\": generator.get_state(),\n1748\t                    },\n1749\t                    checkpoint_dir / \"best.pt\",\n1750\t                )\n1751\t        else:\n1752\t            epochs_without_improvement += 1\n1753\t        log(\n1754\t            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755\t            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1756\t            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n1757\t            f\"train_targets={int(diagnostics['targets']):,} \"\n1758\t            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1759\t            f\"train_seconds={diagnostics['seconds']:.1f} \"\n1760\t            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1761\t        )\n1762\t        release_accelerator_cache(device)\n1763\t        if epochs_without_improvement >= config.early_stopping_patience:\n1764\t            log(\n1765\t                f\"early stopping after epoch={epoch}; \"\n1766\t                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1767\t            )\n1768\t            break\n1769\t\n1770\t    if best_state is None:\n1771\t        raise RuntimeError(\"training did not produce a valid checkpoint\")\n1772\t    model.load_state_dict(best_state)\n1773\t    model.to(device).eval()\n1774\t    log(\n1775\t        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1776\t        f\"loss={best_loss:.6f}\"\n1777\t    )\n1778\t    return model, history\n1779\t\n1780\t\n1781\t@torch.inference_mode()\n1782\tdef inference_queries(\n1783\t    model: base.SequentialTwoTower,\n1784\t    prepared: SequenceData,\n1785\t    config: Config,\n1786\t    device: torch.device,\n1787\t) -> torch.Tensor:\n1788\t    sequence_rows = prepared.inference_user_rows\n1789\t    lengths = np.where(\n1790\t        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n1791\t    )\n1792\t    ordered = np.argsort(lengths, kind=\"stable\")\n1793\t    result = torch.zeros(\n1794\t        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n1795\t    )\n1796\t    for group_start in range(0, len(ordered), config.user_batch_size):\n1797\t        output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798\t        rows = sequence_rows[output_rows]\n1799\t        group_lengths = lengths[output_rows]\n1800\t        hidden = torch.zeros(\n1801\t            1, len(output_rows), config.embedding_dim, device=device\n1802\t        )\n1803\t        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n1804\t            steps = min(\n1805\t                config.bptt_steps,\n1806\t                int(group_lengths.max(initial=0)) - start,\n1807\t            )\n1808\t            items = np.full(\n1809\t                (len(output_rows), steps),\n1810\t                len(prepared.item_ids),\n1811\t                dtype=np.uint16,\n1812\t            )\n1813\t            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814\t            counts = np.zeros(len(output_rows), dtype=np.int32)\n1815\t            for local_row, sequence_row in enumerate(rows):\n1816\t                if sequence_row < 0:\n1817\t                    continue\n1818\t                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n1819\t                if not count:\n1820\t                    continue\n1821\t                offset = int(prepared.offsets[sequence_row])\n1822\t                selected = slice(offset + start, offset + start + count)\n1823\t                items[local_row, :count] = prepared.sequence_items[selected]\n1824\t                features[local_row, :count] = prepared.event_features[selected]\n1825\t                counts[local_row] = count\n1826\t            outputs, _ = event_sequence(\n1827\t                model,\n1828\t                torch.from_numpy(items.astype(np.int64)).to(device),\n1829\t                torch.from_numpy(features.astype(np.float32)).to(device),\n1830\t                hidden,\n1831\t            )\n1832\t            next_rows: list[torch.Tensor] = []\n1833\t            for local_row, count in enumerate(counts):\n1834\t                next_rows.append(\n1835\t                    outputs[local_row, count - 1]\n1836\t                    if count\n1837\t                    else hidden[0, local_row]\n1838\t                )\n1839\t            hidden = torch.stack(next_rows).unsqueeze(0)\n1840\t\n1841\t        liked_items = torch.from_numpy(\n1842\t            prepared.inference_liked_items[output_rows].astype(np.int64)\n1843\t        ).to(device)\n1844\t        disliked_items = torch.from_numpy(\n1845\t            prepared.inference_disliked_items[output_rows].astype(np.int64)\n1846\t        ).to(device)\n1847\t        queries = user_queries(\n1848\t            model,\n1849\t            hidden.squeeze(0),\n1850\t            model._mean_context_channel(liked_items),\n1851\t            model._mean_context_channel(disliked_items),\n1852\t            torch.from_numpy(\n1853\t                prepared.inference_user_features[output_rows].astype(np.float32)\n1854\t            ).to(device),\n1855\t        )\n1856\t        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1857\t    return result\n1858\t\n1859\t\n1860\t@torch.inference_mode()\n1861\tdef rank_candidates(\n1862\t    model: base.SequentialTwoTower,\n1863\t    prepared: SequenceData,\n1864\t    eligibility: CandidateSet,\n1865\t    config: Config,\n1866\t    device: torch.device,\n1867\t    ranking_batch_size: int,\n1868\t) -> tuple[pd.DataFrame, dict[str, float]]:\n1869\t    model.eval()\n1870\t    queries = inference_queries(model, prepared, config, device)\n1871\t    item_parts: list[torch.Tensor] = []\n1872\t    for start in range(0, len(prepared.item_ids), 2_048):\n1873\t        rows = torch.arange(\n1874\t            start,\n1875\t            min(start + 2_048, len(prepared.item_ids)),\n1876\t            dtype=torch.long,\n1877\t            device=device,\n1878\t        )\n1879\t        item_parts.append(model.encode_items(rows).cpu())\n1880\t    items = torch.cat(item_parts)\n1881\t    rows: list[tuple[int, int, int]] = []\n1882\t    for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883\t        end = min(start + ranking_batch_size, len(eligibility.uids))\n1884\t        scores = queries[start:end] @ items.T\n1885\t        eligible = np.unpackbits(\n1886\t            eligibility.packed[start:end],\n1887\t            axis=1,\n1888\t            count=len(prepared.item_ids),\n1889\t            bitorder=\"little\",\n1890\t        ).astype(bool, copy=False)\n1891\t        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n1892\t        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n1893\t        for local_row, columns in enumerate(selected):\n1894\t            uid = int(eligibility.uids[start + local_row])\n1895\t            rows.extend(\n1896\t                (uid, int(prepared.item_ids[column]), rank)\n1897\t                for rank, column in enumerate(columns, start=1)\n1898\t            )\n1899\t        if end == len(eligibility.uids) or end % 1_024 == 0:\n1900\t            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1901\t    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n1902\t    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n1903\t    diagnostics = {\n1904\t        \"query_norm_mean\": float(query_norm.mean()),\n1905\t        \"query_norm_std\": float(query_norm.std()),\n1906\t        \"item_norm_mean\": float(item_norm.mean()),\n1907\t        \"item_norm_std\": float(item_norm.std()),\n1908\t    }\n1909\t    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1910\t\n1911\t\n1912\tdef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913\t    output.parent.mkdir(parents=True, exist_ok=True)\n1914\t    if output.suffix == \".parquet\":\n1915\t        frame.to_parquet(output, index=False, compression=\"zstd\")\n1916\t    elif output.suffix == \".csv\":\n1917\t        frame.to_csv(output, index=False)\n1918\t    else:\n1919\t        raise ValueError(\"--output must end in .parquet or .csv\")\n1920\t\n1921\t\n1922\tdef main() -> int:\n1923\t    parser = argparse.ArgumentParser(description=__doc__)\n1924\t    parser.add_argument(\n1925\t        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1926\t    )\n1927\t    parser.add_argument(\"--output\", type=Path, required=True)\n1928\t    parser.add_argument(\"--public-dir\", type=Path, required=True)\n1929\t    parser.add_argument(\"--prepared-cache\", type=Path)\n1930\t    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n1931\t    parser.add_argument(\"--threads\", type=int, default=8)\n1932\t    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n1933\t    parser.add_argument(\n1934\t        \"--user-batch-size\", type=int, default=Config.user_batch_size\n1935\t    )\n1936\t    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n1937\t    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n1938\t    parser.add_argument(\n1939\t        \"--loss-microbatch-size\",\n1940\t        type=int,\n1941\t        default=Config.loss_microbatch_size,\n1942\t    )\n1943\t    parser.add_argument(\n1944\t        \"--negative-filter-scope\",\n1945\t        choices=(\"full-training-history\", \"last-100\"),\n1946\t        default=Config.negative_filter_scope,\n1947\t    )\n1948\t    parser.add_argument(\n1949\t        \"--target-weighting\",\n1950\t        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n1951\t        default=Config.target_weighting,\n1952\t    )\n1953\t    parser.add_argument(\n1954\t        \"--ordinary-target-weight\",\n1955\t        type=float,\n1956\t        default=Config.ordinary_target_weight,\n1957\t    )\n1958\t    parser.add_argument(\n1959\t        \"--organic-strong-target-weight\",\n1960\t        type=float,\n1961\t        default=Config.organic_strong_target_weight,\n1962\t    )\n1963\t    parser.add_argument(\n1964\t        \"--organic-like-target-weight\",\n1965\t        type=float,\n1966\t        default=Config.organic_like_target_weight,\n1967\t    )\n1968\t    parser.add_argument(\n1969\t        \"--discovery-horizon-days\",\n1970\t        type=int,\n1971\t        default=Config.discovery_horizon_days,\n1972\t    )\n1973\t    parser.add_argument(\n1974\t        \"--validation-fraction\",\n1975\t        type=float,\n1976\t        default=Config.validation_fraction,\n1977\t    )\n1978\t    parser.add_argument(\n1979\t        \"--max-train-targets-per-user\",\n1980\t        type=int,\n1981\t        default=Config.max_train_targets_per_user,\n1982\t        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1983\t    )\n1984\t    parser.add_argument(\n1985\t        \"--max-validation-targets-per-user\",\n1986\t        type=int,\n1987\t        default=Config.max_validation_targets_per_user,\n1988\t        help=\"retain this many final validation targets per user; 0 is uncapped\",\n1989\t    )\n1990\t    parser.add_argument(\n1991\t        \"--early-stopping-patience\",\n1992\t        type=int,\n1993\t        default=Config.early_stopping_patience,\n1994\t    )\n1995\t    parser.add_argument(\n1996\t        \"--early-stopping-min-delta\",\n1997\t        type=float,\n1998\t        default=Config.early_stopping_min_delta,\n1999\t    )\n2000\t    parser.add_argument(\"--checkpoint-dir\", type=Path)\n2001\t    parser.add_argument(\"--resume-checkpoint\", type=Path)\n2002\t    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n2003\t    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n2004\t    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n2005\t    args = parser.parse_args()\n2006\t\n2007\t    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n2008\t        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n2009\t    if (\n2010\t        args.epochs < 1\n2011\t        or args.learning_rate <= 0\n2012\t        or args.loss_microbatch_size < 1\n2013\t        or args.early_stopping_patience < 1\n2014\t        or args.early_stopping_min_delta < 0\n2015\t        or args.ordinary_target_weight <= 0\n2016\t        or args.organic_strong_target_weight <= 0\n2017\t        or args.organic_like_target_weight <= 0\n2018\t        or args.discovery_horizon_days < 1\n2019\t        or args.max_train_targets_per_user < 0\n2020\t        or args.max_validation_targets_per_user < 0\n2021\t    ):\n2022\t        parser.error(\"training counts, learning rate, and patience are invalid\")\n2023\t    if not 0.0 < args.validation_fraction < 1.0:\n2024\t        parser.error(\"validation fraction must be strictly between zero and one\")\n2025\t    public = args.public_dir.resolve()\n2026\t    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n2027\t        parser.error(f\"public task data not found under {public}\")\n2028\t    config = Config(\n2029\t        bptt_steps=args.bptt_steps,\n2030\t        user_batch_size=args.user_batch_size,\n2031\t        epochs=args.epochs,\n2032\t        learning_rate=args.learning_rate,\n2033\t        loss_microbatch_size=args.loss_microbatch_size,\n2034\t        negative_filter_scope=args.negative_filter_scope,\n2035\t        target_weighting=args.target_weighting,\n2036\t        ordinary_target_weight=args.ordinary_target_weight,\n2037\t        organic_strong_target_weight=args.organic_strong_target_weight,\n2038\t        organic_like_target_weight=args.organic_like_target_weight,\n2039\t        discovery_horizon_days=args.discovery_horizon_days,\n2040\t        validation_fraction=args.validation_fraction,\n2041\t        max_train_targets_per_user=args.max_train_targets_per_user,\n2042\t        max_validation_targets_per_user=args.max_validation_targets_per_user,\n2043\t        early_stopping_patience=args.early_stopping_patience,\n2044\t        early_stopping_min_delta=args.early_stopping_min_delta,\n2045\t        seed=args.seed,\n2046\t    )\n2047\t    torch.set_num_threads(args.threads)\n2048\t    torch.set_num_interop_threads(1)\n2049\t    seed_everything(config.seed)\n2050\t    device = torch.device(args.device)\n2051\t    if device.type == \"mps\" and not torch.backends.mps.is_available():\n2052\t        parser.error(\"MPS was requested but is unavailable\")\n2053\t\n2054\t    started = time.monotonic()\n2055\t    prepared, eligibility = load_or_prepare(\n2056\t        public,\n2057\t        args.stage,\n2058\t        config,\n2059\t        args.prepared_cache.resolve() if args.prepared_cache else None,\n2060\t    )\n2061\t    target_partition = chronological_target_partition(\n2062\t        prepared,\n2063\t        config.validation_fraction,\n2064\t        config.max_train_targets_per_user,\n2065\t        config.max_validation_targets_per_user,\n2066\t    )\n2067\t    if args.prepare_only:\n2068\t        print(\n2069\t            json.dumps(\n2070\t                {\n2071\t                    \"stage\": args.stage,\n2072\t                    \"sequence_users\": len(prepared.user_ids),\n2073\t                    \"sequence_tokens\": len(prepared.sequence_items),\n2074\t                    \"next_item_targets\": prepared.target_count,\n2075\t                    \"selected_training_targets\": int(\n2076\t                        target_partition.training_counts.sum()\n2077\t                    ),\n2078\t                    \"selected_validation_targets\": int(\n2079\t                        target_partition.validation_counts.sum()\n2080\t                    ),\n2081\t                    \"discarded_old_targets\": int(\n2082\t                        target_partition.window_starts.sum()\n2083\t                    ),\n2084\t                    \"target_users\": len(eligibility.uids),\n2085\t                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n2086\t                    \"config\": asdict(config),\n2087\t                },\n2088\t                indent=2,\n2089\t            )\n2090\t        )\n2091\t        return 0\n2092\t\n2093\t    model, training_history = train_model(\n2094\t        public,\n2095\t        args.stage,\n2096\t        prepared,\n2097\t        config,\n2098\t        device,\n2099\t        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n2100\t        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n2101\t    )\n2102\t    ranking, embedding_diagnostics = rank_candidates(\n2103\t        model,\n2104\t        prepared,\n2105\t        eligibility,\n2106\t        config,\n2107\t        device,\n2108\t        args.ranking_batch_size,\n2109\t    )\n2110\t    write_ranking(ranking, args.output.resolve())\n2111\t    print(\n2112\t        json.dumps(\n2113\t            {\n2114\t                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n2115\t                \"stage\": args.stage,\n2116\t                \"output\": str(args.output.resolve()),\n2117\t                \"rows\": len(ranking),\n2118\t                \"users\": int(ranking.uid.nunique()),\n2119\t                \"runtime_seconds\": round(time.monotonic() - started, 3),\n2120\t                \"public_inputs_only\": True,\n2121\t                \"architecture\": {\n2122\t                    \"family\": \"pure-windowed-sequence-two-tower\",\n2123\t                    \"sequence_training\": (\n2124\t                        \"every-adjacent-target-within-recent-chronological-window\"\n2125\t                    ),\n2126\t                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2127\t                    \"bptt_hidden_state\": \"carried-and-detached\",\n2128\t                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n2129\t                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n2130\t                    \"user_id_embedding\": False,\n2131\t                    \"user_mlp\": \"582-512-256-192\",\n2132\t                    \"item_mlp\": \"196-512-256-192\",\n2133\t                    \"normalization\": \"none\",\n2134\t                    \"score\": \"raw-dot-product\",\n2135\t                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n2136\t                    \"negative_sampling\": (\n2137\t                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n2138\t                    ),\n2139\t                    \"false_negative_filter\": config.negative_filter_scope,\n2140\t                    \"target_weighting\": config.target_weighting,\n2141\t                },\n2142\t                \"config\": asdict(config),\n2143\t                \"sequence_tokens\": len(prepared.sequence_items),\n2144\t                \"all_next_item_targets\": prepared.target_count,\n2145\t                \"selected_training_targets\": int(\n2146\t                    target_partition.training_counts.sum()\n2147\t                ),\n2148\t                \"selected_validation_targets\": int(\n2149\t                    target_partition.validation_counts.sum()\n2150\t                ),\n2151\t                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n2152\t                \"training_history\": training_history,\n2153\t                \"embedding_diagnostics\": embedding_diagnostics,\n2154\t                \"torch_version\": torch.__version__,\n2155\t                \"device\": str(device),\n2156\t            },\n2157\t            indent=2,\n2158\t        )\n2159\t    )\n2160\t    return 0\n2161\t\n2162\t\n2163\tif __name__ == \"__main__\":\n2164\t    raise SystemExit(main())\n2165\t",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:43.996Z",
          "callId": "toolu_01CaBjWnjW1dUhx56cbKxWhG",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/two_tower_model.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:36:44.001Z",
            "content": "1\t\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\n2\tfrom __future__ import annotations\n3\t\n4\timport sys\n5\tfrom dataclasses import dataclass\n6\tfrom pathlib import Path\n7\tfrom typing import Iterator\n8\t\n9\timport numpy as np\n10\timport pandas as pd\n11\timport pyarrow.parquet as pq\n12\timport torch\n13\tfrom torch import nn\n14\t\n15\t\n16\tDAY = 86_400\n17\tTRAIN_END = 24_786_800\n18\tVALIDATION_END = 25_393_400\n19\tLISTEN_POSITIVE_THRESHOLD = 50\n20\tPROFILE_LOOKBACK_DAYS = 120\n21\tLIKE_ITEMS = 48\n22\tDISLIKE_ITEMS = 24\n23\tTOP_N = 100\n24\t\n25\t\n26\t@dataclass(frozen=True)\n27\tclass TowerConfig:\n28\t    sequence_length: int = 100\n29\t    embedding_dim: int = 192\n30\t    hidden_dim: int = 192\n31\t    user_hidden_1: int = 512\n32\t    user_hidden_2: int = 256\n33\t    item_hidden_1: int = 512\n34\t    item_hidden_2: int = 256\n35\t    dropout: float = 0.05\n36\t    epochs: int = 1\n37\t    learning_rate: float = 1.0e-3\n38\t    weight_decay: float = 1.0e-5\n39\t    gradient_clip: float = 2.0\n40\t    uniform_negatives: int = 24\n41\t    inbatch_negatives: int = 8\n42\t    inbatch_correction_power: float = 0.5\n43\t    inbatch_correction_min: float = 0.25\n44\t    inbatch_correction_max: float = 4.0\n45\t    seed: int = 29\n46\t\n47\t\n48\t@dataclass(frozen=True)\n49\tclass ListenEvents:\n50\t    timestamps: np.ndarray\n51\t    items: np.ndarray\n52\t    organic: np.ndarray\n53\t    completion_pct: np.ndarray\n54\t\n55\t\n56\t@dataclass(frozen=True)\n57\tclass ExplicitEvents:\n58\t    timestamps: np.ndarray\n59\t    items: np.ndarray\n60\t    channel: np.ndarray\n61\t    active: np.ndarray\n62\t    organic_like: np.ndarray\n63\t\n64\t\n65\tdef log(message: str) -> None:\n66\t    print(message, file=sys.stderr, flush=True)\n67\t\n68\t\n69\tdef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n70\t    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n71\t    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n72\t    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n73\t    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n74\t    features = np.column_stack(\n75\t        [\n76\t            np.log1p(positives),\n77\t            np.log1p(organic),\n78\t            organic / np.maximum(positives, 1.0),\n79\t            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n80\t        ]\n81\t    )\n82\t    means = features.mean(axis=0, keepdims=True)\n83\t    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n84\t    standardized = ((features - means) / scales).astype(np.float32)\n85\t    if not np.isfinite(standardized).all():\n86\t        raise RuntimeError(\"catalog features contain non-finite values\")\n87\t    return standardized\n88\t\n89\t\n90\tdef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n91\t    if len(parts) == 1:\n92\t        return parts[0]\n93\t    timestamps = np.concatenate([part.timestamps for part in parts])\n94\t    items = np.concatenate([part.items for part in parts])\n95\t    organic = np.concatenate([part.organic for part in parts])\n96\t    completion = np.concatenate([part.completion_pct for part in parts])\n97\t    order = np.argsort(timestamps, kind=\"stable\")\n98\t    return ListenEvents(\n99\t        timestamps=timestamps[order],\n100\t        items=items[order],\n101\t        organic=organic[order],\n102\t        completion_pct=completion[order],\n103\t    )\n104\t\n105\t\n106\tdef iter_listen_users(\n107\t    path: Path, item_index: pd.Index\n108\t) -> Iterator[tuple[int, ListenEvents]]:\n109\t    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n110\t    parquet = pq.ParquetFile(path)\n111\t    pending_uid: int | None = None\n112\t    pending: list[ListenEvents] = []\n113\t    previous_uid = -1\n114\t    for batch in parquet.iter_batches(\n115\t        batch_size=750_000,\n116\t        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n117\t    ):\n118\t        frame = batch.to_pandas()\n119\t        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n120\t        keep = mapped >= 0\n121\t        if not np.any(keep):\n122\t            continue\n123\t        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n124\t        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n125\t        items = mapped[keep].astype(np.int32, copy=False)\n126\t        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n127\t        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n128\t        if np.any(uids[1:] < uids[:-1]):\n129\t            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n130\t        boundaries = np.r_[\n131\t            0,\n132\t            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n133\t            len(uids),\n134\t        ]\n135\t        for start, end in zip(boundaries[:-1], boundaries[1:]):\n136\t            uid = int(uids[start])\n137\t            part = ListenEvents(\n138\t                timestamps[start:end].copy(),\n139\t                items[start:end].copy(),\n140\t                organic[start:end].copy(),\n141\t                completion[start:end].copy(),\n142\t            )\n143\t            if pending_uid is None:\n144\t                pending_uid = uid\n145\t            elif uid != pending_uid:\n146\t                if pending_uid < previous_uid:\n147\t                    raise RuntimeError(f\"{path.name} user order regressed\")\n148\t                yield pending_uid, _combine_listen_chunks(pending)\n149\t                previous_uid = pending_uid\n150\t                pending_uid = uid\n151\t                pending = []\n152\t            pending.append(part)\n153\t    if pending_uid is not None:\n154\t        yield pending_uid, _combine_listen_chunks(pending)\n155\t\n156\t\n157\tdef merge_listen_streams(\n158\t    paths: list[Path], item_index: pd.Index\n159\t) -> Iterator[tuple[int, ListenEvents]]:\n160\t    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n161\t    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n162\t    heads: list[tuple[int, ListenEvents] | None] = [\n163\t        next(stream, None) for stream in streams\n164\t    ]\n165\t    while any(head is not None for head in heads):\n166\t        uid = min(head[0] for head in heads if head is not None)\n167\t        parts: list[ListenEvents] = []\n168\t        for position, head in enumerate(heads):\n169\t            if head is not None and head[0] == uid:\n170\t                parts.append(head[1])\n171\t                heads[position] = next(streams[position], None)\n172\t        yield uid, _combine_listen_chunks(parts)\n173\t\n174\t\n175\tdef load_explicit_events(\n176\t    data: Path,\n177\t    prefixes: tuple[str, ...],\n178\t    item_index: pd.Index,\n179\t) -> dict[int, ExplicitEvents]:\n180\t    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n181\t    specifications = (\n182\t        (\"likes\", 0, 1, 0),\n183\t        (\"unlikes\", 0, 0, 1),\n184\t        (\"dislikes\", 1, 1, 0),\n185\t        (\"undislikes\", 1, 0, 1),\n186\t    )\n187\t    parts: list[pd.DataFrame] = []\n188\t    for family, channel, active, tie_priority in specifications:\n189\t        for prefix in prefixes:\n190\t            frame = pd.read_parquet(\n191\t                data / f\"{prefix}_{family}.parquet\",\n192\t                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n193\t            )\n194\t            mapped = item_index.get_indexer(\n195\t                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n196\t            )\n197\t            keep = mapped >= 0\n198\t            if not np.any(keep):\n199\t                continue\n200\t            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n201\t            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n202\t            retained[\"channel\"] = np.int8(channel)\n203\t            retained[\"active\"] = np.int8(active)\n204\t            retained[\"tie_priority\"] = np.int8(tie_priority)\n205\t            retained[\"organic_like\"] = np.int8(\n206\t                family == \"likes\"\n207\t            ) * retained.is_organic.astype(np.int8)\n208\t            parts.append(retained.drop(columns=\"is_organic\"))\n209\t    if not parts:\n210\t        return {}\n211\t    events = pd.concat(parts, ignore_index=True)\n212\t    events = events.sort_values(\n213\t        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n214\t    ).reset_index(drop=True)\n215\t    result: dict[int, ExplicitEvents] = {}\n216\t    for uid, rows in events.groupby(\"uid\", sort=False):\n217\t        result[int(uid)] = ExplicitEvents(\n218\t            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n219\t            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n220\t            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n221\t            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n222\t            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n223\t        )\n224\t    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n225\t    return result\n226\t\n227\t\n228\tdef empty_explicit_events() -> ExplicitEvents:\n229\t    return ExplicitEvents(\n230\t        timestamps=np.empty(0, dtype=np.int64),\n231\t        items=np.empty(0, dtype=np.int32),\n232\t        channel=np.empty(0, dtype=np.int8),\n233\t        active=np.empty(0, dtype=np.int8),\n234\t        organic_like=np.empty(0, dtype=np.int8),\n235\t    )\n236\t\n237\t\n238\tdef pack_active_items(\n239\t    active: dict[int, int], width: int, padding_item: int\n240\t) -> np.ndarray:\n241\t    packed = np.full(width, padding_item, dtype=np.uint16)\n242\t    if not active:\n243\t        return packed\n244\t    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n245\t    packed[: len(ordered)] = np.fromiter(\n246\t        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n247\t    )\n248\t    return packed\n249\t\n250\t\n251\tdef load_inference_state(\n252\t    path: Path,\n253\t    item_index: pd.Index,\n254\t    uids: np.ndarray,\n255\t    n_items: int,\n256\t) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n257\t    \"\"\"Pack the task-provided active explicit state.\"\"\"\n258\t    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n259\t    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n260\t    keep = mapped >= 0\n261\t    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n262\t    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n263\t    user_rows = pd.Index(uids)\n264\t    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n265\t    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n266\t    like_counts = np.zeros(len(uids), dtype=np.int32)\n267\t    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n268\t    for value, width, destination, counts in (\n269\t        (\"liked\", LIKE_ITEMS, liked, like_counts),\n270\t        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n271\t    ):\n272\t        selected = state[state.state == value]\n273\t        for uid, rows in selected.groupby(\"uid\", sort=False):\n274\t            row = int(user_rows.get_indexer([int(uid)])[0])\n275\t            if row < 0:\n276\t                continue\n277\t            items = np.sort(rows.item.unique())\n278\t            counts[row] = len(items)\n279\t            retained = items[:width]\n280\t            destination[row, : len(retained)] = retained.astype(np.uint16)\n281\t    return liked, disliked, like_counts, dislike_counts\n282\t\n283\t\n284\tclass SequentialTwoTower(nn.Module):\n285\t    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n286\t\n287\t    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n288\t        super().__init__()\n289\t        n_items, n_item_features = item_features.shape\n290\t        dim = config.embedding_dim\n291\t        self.n_items = n_items\n292\t        self.context_embedding = nn.Embedding(\n293\t            n_items + 1, dim, padding_idx=n_items\n294\t        )\n295\t        self.event_projection = nn.Sequential(\n296\t            nn.Linear(dim + 3, config.hidden_dim),\n297\t            nn.GELU(),\n298\t        )\n299\t        self.sequence_encoder = nn.GRU(\n300\t            input_size=config.hidden_dim,\n301\t            hidden_size=dim,\n302\t            num_layers=1,\n303\t            batch_first=True,\n304\t        )\n305\t        self.user_tower = nn.Sequential(\n306\t            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n307\t            nn.GELU(),\n308\t            nn.Dropout(config.dropout),\n309\t            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n310\t            nn.GELU(),\n311\t            nn.Dropout(config.dropout),\n312\t            nn.Linear(config.user_hidden_2, dim),\n313\t        )\n314\t        self.candidate_embedding = nn.Embedding(n_items, dim)\n315\t        self.register_buffer(\n316\t            \"item_features\", torch.from_numpy(item_features), persistent=True\n317\t        )\n318\t        self.item_tower = nn.Sequential(\n319\t            nn.Linear(dim + n_item_features, config.item_hidden_1),\n320\t            nn.GELU(),\n321\t            nn.Dropout(config.dropout),\n322\t            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n323\t            nn.GELU(),\n324\t            nn.Dropout(config.dropout),\n325\t            nn.Linear(config.item_hidden_2, dim),\n326\t        )\n327\t        nn.init.normal_(self.context_embedding.weight, std=0.02)\n328\t        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n329\t        with torch.no_grad():\n330\t            self.context_embedding.weight[n_items].zero_()\n331\t\n332\t    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n333\t        mask = items.ne(self.n_items)\n334\t        vectors = self.context_embedding(items)\n335\t        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n336\t            dim=1, keepdim=True\n337\t        ).clamp_min(1)\n338\t\n339\t    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n340\t        inputs = torch.cat(\n341\t            [self.candidate_embedding(items), self.item_features[items]], dim=1\n342\t        )\n343\t        return self.item_tower(inputs)\n344\t\n345\t\n346\tdef invalid_negative_mask(\n347\t    negatives: torch.Tensor,\n348\t    targets: torch.Tensor,\n349\t    context_items: torch.Tensor,\n350\t    liked_items: torch.Tensor,\n351\t) -> torch.Tensor:\n352\t    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n353\t    invalid = negatives.eq(targets[:, None])\n354\t    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n355\t    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n356\t    for column in range(1, negatives.shape[1]):\n357\t        invalid[:, column] |= negatives[:, column, None].eq(\n358\t            negatives[:, :column]\n359\t        ).any(dim=1)\n360\t    return invalid\n361\t\n362\t\n363\tdef draw_filtered_mixed_negatives(\n364\t    targets: torch.Tensor,\n365\t    context_items: torch.Tensor,\n366\t    liked_items: torch.Tensor,\n367\t    n_items: int,\n368\t    uniform_count: int,\n369\t    inbatch_count: int,\n370\t    item_correction: torch.Tensor,\n371\t    generator: torch.Generator,\n372\t    device: torch.device,\n373\t) -> tuple[torch.Tensor, torch.Tensor]:\n374\t    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n375\t    batch_size = len(targets)\n376\t    uniform = torch.randint(\n377\t        n_items,\n378\t        (batch_size, uniform_count),\n379\t        generator=generator,\n380\t        device=\"cpu\",\n381\t    )\n382\t    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n383\t    corrected_count = 0\n384\t    if inbatch_count:\n385\t        unique_targets = torch.unique(targets.detach().cpu())\n386\t        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n387\t        if corrected_count:\n388\t            priorities = torch.rand(\n389\t                (batch_size, len(unique_targets)), generator=generator\n390\t            )\n391\t            priorities.masked_fill_(\n392\t                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n393\t                -1.0,\n394\t            )\n395\t            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n396\t            inbatch = unique_targets[columns]\n397\t        if corrected_count < inbatch_count:\n398\t            filler = torch.randint(\n399\t                n_items,\n400\t                (batch_size, inbatch_count - corrected_count),\n401\t                generator=generator,\n402\t                device=\"cpu\",\n403\t            )\n404\t            inbatch = torch.cat([inbatch, filler], dim=1)\n405\t\n406\t    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n407\t    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n408\t    if corrected_count:\n409\t        correction_cpu[\n410\t            :, uniform_count : uniform_count + corrected_count\n411\t        ] = item_correction[inbatch[:, :corrected_count]]\n412\t    negatives = negatives_cpu.to(device)\n413\t    correction = correction_cpu.to(device)\n414\t    for _ in range(32):\n415\t        invalid = invalid_negative_mask(\n416\t            negatives, targets, context_items, liked_items\n417\t        )\n418\t        if not bool(invalid.any()):\n419\t            return negatives, correction\n420\t        replacements = torch.randint(\n421\t            n_items,\n422\t            (int(invalid.sum().detach().cpu()),),\n423\t            generator=generator,\n424\t            device=\"cpu\",\n425\t        ).to(device)\n426\t        negatives[invalid] = replacements\n427\t        correction[invalid] = 1.0\n428\t    invalid = invalid_negative_mask(\n429\t        negatives, targets, context_items, liked_items\n430\t    )\n431\t    if bool(invalid.any()):\n432\t        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n433\t    return negatives, correction\n434\t",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:44.013Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14954599 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 4,
      "role": "agent",
      "timestamp": "2026-09-24T17:37:11.842Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:37:11.842Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:37:12.257Z",
          "text": "Now let me set up and kick off the baseline immediately to get timing and a score."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:13.723Z",
          "callId": "toolu_015wy5eHVuUi3GhnibhSAiBu",
          "name": "Bash",
          "input": {
            "command": "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && ls /app/solution/ && python -c \"import pandas as pd; c=pd.read_parquet('/task/data/candidate_catalog.parquet'); print(c.head()); print(len(c)); t=pd.read_parquet('/task/data/target_users.parquet'); print(t.shape, t.columns.tolist())\""
          },
          "result": {
            "timestamp": "2026-09-24T17:37:15.387Z",
            "content": "train_current_model.py\ntwo_tower_model.py\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n30000\n(8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:14.016Z",
          "callId": "toolu_01HvsLwGgih9cmRMBqdZfZku",
          "name": "Read",
          "input": {
            "file_path": "/task/evaluate_public.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:37:15.391Z",
            "content": "1\t#!/usr/bin/env python3\n2\t\"\"\"Score a validation-stage ranking against public discovery labels.\n3\t\n4\t``build_task.py`` publishes this module with the solver-visible task files. It\n5\tloads target users, validation eligibility, and validation-window events from\n6\tthe adjacent ``data`` directory, then evaluates one ranking supplied on the\n7\tcommand line. Stage-independent mechanics come from the adjacent public\n8\t``ranking_evaluation.py`` module. The builder installs the same checked-in\n9\tmodule in the private judge, so public and private scoring mechanics cannot\n10\tdrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\n11\tan organic listen with at least 80 percent completion; a like takes precedence\n12\twhen both events exist.\n13\t\n14\tBefore scoring, the ranking must contain exactly 100 distinct eligible items\n15\tfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\n16\tcolumns and ranks 1 through 100 exactly once per user. Extra columns are\n17\tignored. Quality is graded NDCG@10, averaged only over users who have at least\n18\tone eligible relevant item in the validation window.\n19\t\n20\tThe evaluator prints a JSON object containing the model path and scores. It\n21\tdoes not apply the task's pass threshold; its purpose is model development and\n22\tselection. The private judge applies the threshold to the final submission.\n23\t\n24\tUsage::\n25\t\n26\t    python evaluate_public.py <validation-ranking.parquet|csv>\n27\t\"\"\"\n28\tfrom __future__ import annotations\n29\t\n30\timport json\n31\timport sys\n32\tfrom pathlib import Path\n33\t\n34\timport pandas as pd\n35\t\n36\tfrom ranking_evaluation import (\n37\t    CandidateSet,\n38\t    RankingValidationError,\n39\t    ranked_items,\n40\t    read_ranking,\n41\t    score_rankings,\n42\t    validate_ranking,\n43\t)\n44\t\n45\t\n46\tHERE = Path(__file__).resolve().parent\n47\tDATA = HERE / \"data\"\n48\tLISTEN_STRONG = 80\n49\t\n50\t\n51\tdef validation_truth(\n52\t    eligibility: CandidateSet, targets: set[int]\n53\t) -> dict[int, dict[int, int]]:\n54\t    \"\"\"Build cutoff-correct graded relevance from public validation events.\n55\t\n56\t    Only target-user events for eligible candidates are retained. Organic\n57\t    listens completed to at least 80 percent receive grade 1, and organic likes\n58\t    receive grade 3. Repeated user-item events are collapsed, and processing\n59\t    likes second ensures that grade 3 wins over grade 1.\n60\t\n61\t    Args:\n62\t        eligibility: Validation-cutoff candidate membership service.\n63\t        targets: Complete set of target user IDs.\n64\t\n65\t    Returns:\n66\t        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n67\t        relevant event are omitted.\n68\t    \"\"\"\n69\t    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n70\t    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n71\t    strong = listens[\n72\t        listens.uid.isin(targets)\n73\t        & (listens.is_organic == 1)\n74\t        & (listens.played_ratio_pct >= LISTEN_STRONG)\n75\t    ][[\"uid\", \"item_id\"]].drop_duplicates()\n76\t    organic_likes = likes[\n77\t        likes.uid.isin(targets) & (likes.is_organic == 1)\n78\t    ][[\"uid\", \"item_id\"]].drop_duplicates()\n79\t\n80\t    graded: dict[int, dict[int, int]] = {}\n81\t    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n82\t        uid, item = int(raw_uid), int(raw_item)\n83\t        if eligibility.is_eligible(uid, item):\n84\t            graded.setdefault(uid, {})[item] = 1\n85\t    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n86\t        uid, item = int(raw_uid), int(raw_item)\n87\t        if eligibility.is_eligible(uid, item):\n88\t            graded.setdefault(uid, {})[item] = 3\n89\t    return graded\n90\t\n91\t\n92\tdef load_submission(\n93\t    path: Path, eligibility: CandidateSet, targets: set[int]\n94\t) -> dict[int, list[int]]:\n95\t    \"\"\"Load and validate a public validation ranking.\n96\t\n97\t    Files ending in ``.parquet`` are read as parquet; every other suffix is\n98\t    read as CSV. The required columns must be integer-valued, cover exactly the\n99\t    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n100\t    duplicate user-item pairs, and contain only cutoff-eligible items.\n101\t\n102\t    Args:\n103\t        path: Ranking file to read.\n104\t        eligibility: Validation-cutoff candidate membership service.\n105\t        targets: Exact set of users the ranking must cover.\n106\t\n107\t    Returns:\n108\t        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n109\t\n110\t    Raises:\n111\t        ValueError: If the ranking violates the required schema or invariants.\n112\t    \"\"\"\n113\t    frame = validate_ranking(read_ranking(path), targets)\n114\t    if not all(\n115\t        eligibility.is_eligible(uid, item)\n116\t        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n117\t    ):\n118\t        raise RankingValidationError(\n119\t            \"submission contains an item rejected by eligibility service\"\n120\t        )\n121\t    return ranked_items(frame)\n122\t\n123\t\n124\tdef score(\n125\t    ranked: dict[int, list[int]],\n126\t    graded: dict[int, dict[int, int]],\n127\t) -> dict[str, float | int]:\n128\t    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n129\t\n130\t    Args:\n131\t        ranked: Ranked item IDs for every target user.\n132\t        graded: Eligible validation relevance keyed by user and item.\n133\t\n134\t    Returns:\n135\t        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n136\t    \"\"\"\n137\t    return score_rankings(ranked, graded)\n138\t\n139\t\n140\tdef main() -> None:\n141\t    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n142\t    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n143\t    targets = set(targets_frame.uid.astype(int))\n144\t    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n145\t    graded = validation_truth(eligibility, targets)\n146\t\n147\t    if len(sys.argv) != 2:\n148\t        raise SystemExit(\n149\t            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n150\t        )\n151\t    path = Path(sys.argv[1])\n152\t    ranked = load_submission(path, eligibility, targets)\n153\t    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n154\t\n155\t\n156\tif __name__ == \"__main__\":\n157\t    main()\n158\t",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:37:15.393Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14935044 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 5,
      "role": "agent",
      "timestamp": "2026-09-24T17:37:19.421Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:37:19.421Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:20.768Z",
          "callId": "toolu_01TunZHxYHgwAtPEG73XXzE4",
          "name": "Bash",
          "input": {
            "command": "cd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/production_validation.parquet > /app/solution/baseline_val.log 2>&1 &\necho started $!",
            "run_in_background": false
          },
          "result": {
            "timestamp": "2026-09-24T17:37:20.789Z",
            "content": "started 8130",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:37:20.791Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14931993 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 6,
      "role": "agent",
      "timestamp": "2026-09-24T17:38:13.760Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:38:13.760Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:38:14.172Z",
          "callId": "toolu_01T9t1AKNoTVVNV52FWV6b6j",
          "name": "Bash",
          "input": {
            "command": "sleep 60; tail -5 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:39:14.199Z",
            "content": "saved public-derived full-sequence cache: /app/solution/cache_validation.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",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:39:14.202Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14928623 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 7,
      "role": "agent",
      "timestamp": "2026-09-24T17:39:55.913Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:39:55.913Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:39:56.226Z",
          "callId": "toolu_01Fr9vBPeLx56stx462UHJie",
          "name": "Bash",
          "input": {
            "command": "tail -3 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:39:56.271Z",
            "content": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:39:56.273Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14925361 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 8,
      "role": "agent",
      "timestamp": "2026-09-24T17:39:59.930Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:39:59.930Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:40:00.715Z",
          "text": "Now writing the improved negative sampling. First the config and distribution:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:03.416Z",
          "callId": "toolu_018THRtq8tQcnp1yxYWuj8Mx",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24",
            "new_string": "    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 4_096\n    loss_objective: str = \"sampled-softmax\"\n    negative_pool_size: int = 2_048\n    negative_pool_alpha: float = 0.75\n    negative_pool_uniform_mix: float = 0.30\n    uniform_negatives: int = 24"
          },
          "result": {
            "timestamp": "2026-09-24T17:40:03.447Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:03.450Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14924803 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 9,
      "role": "agent",
      "timestamp": "2026-09-24T17:40:08.261Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:40:08.261Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:19.035Z",
          "callId": "toolu_01PjySb5MEKH4dmGPyEXCFpu",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "def build_known_positive_filter(",
            "new_string": "def selected_target_counts(\n    prepared: SequenceData,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Count how often each catalog item appears as a selected training target.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        start = offset + int(target_starts[user_row]) + 1\n        counts += np.bincount(\n            prepared.sequence_items[start : start + int(count)].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    return counts\n\n\ndef build_negative_sampling_distribution(\n    counts: np.ndarray, config: Config\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Mix a popularity-tempered proposal with uniform coverage.\n\n    Uniform draws over a 30k catalog are almost always deep-tail tracks that the\n    scorer separates after a handful of updates, so the production objective\n    spends nearly all of its negative budget on examples that carry no gradient.\n    Sampling proportional to ``popularity ** alpha`` puts the negatives where the\n    ranking decision actually happens, and the retained uniform mixture keeps the\n    tail from collapsing.  The exact proposal is returned so the sampled-softmax\n    loss can apply an unbiased log-Q correction.\n    \"\"\"\n    n_items = len(counts)\n    popularity = np.power(counts, config.negative_pool_alpha)\n    total = float(popularity.sum())\n    if total <= 0:\n        popularity = np.ones(n_items, dtype=np.float64)\n        total = float(n_items)\n    mix = config.negative_pool_uniform_mix\n    probabilities = (1.0 - mix) * (popularity / total) + mix / n_items\n    probabilities /= probabilities.sum()\n    log(\n        f\"negative proposal: alpha={config.negative_pool_alpha} \"\n        f\"uniform_mix={mix} min={probabilities.min():.3e} \"\n        f\"max={probabilities.max():.3e} \"\n        f\"mass_top1000={probabilities[np.argsort(-probabilities)][:1000].sum():.3f}\"\n    )\n    return (\n        torch.from_numpy(probabilities.astype(np.float64)),\n        torch.from_numpy(np.log(probabilities).astype(np.float32)),\n    )\n\n\ndef draw_shared_negative_pool(\n    probabilities: torch.Tensor,\n    pool_size: int,\n    generator: torch.Generator,\n) -> torch.Tensor:\n    \"\"\"Sample a block-shared negative pool without replacement (Gumbel top-k).\"\"\"\n    keys = torch.log(probabilities) - torch.log(\n        -torch.log(\n            torch.rand(len(probabilities), generator=generator, dtype=torch.float64)\n        )\n    )\n    return torch.topk(keys, pool_size).indices.to(torch.int64)\n\n\ndef build_known_positive_filter("
          },
          "result": {
            "timestamp": "2026-09-24T17:40:19.097Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:19.104Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14923468 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 10,
      "role": "agent",
      "timestamp": "2026-09-24T17:40:27.506Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:40:27.506Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:48.017Z",
          "callId": "toolu_01AK6dMGRKsxyuH4ccxXd8p5",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "def release_accelerator_cache(device: torch.device) -> None:",
            "new_string": "def pool_false_negative_mask(\n    pool: np.ndarray,\n    pool_position: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Mark shared-pool entries that are known positives for each target row.\"\"\"\n    byte_columns = pool >> 3\n    bit_columns = pool & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns[None, :]]\n    invalid = ((known >> bit_columns[None, :]) & 1).astype(bool)\n    rows = np.arange(len(targets), dtype=np.int64)\n    target_columns = pool_position[targets]\n    hit = target_columns >= 0\n    invalid[rows[hit], target_columns[hit]] = True\n    liked_columns = pool_position[liked_items]\n    liked_hit = liked_columns >= 0\n    if np.any(liked_hit):\n        row_index = np.broadcast_to(rows[:, None], liked_columns.shape)\n        invalid[row_index[liked_hit], liked_columns[liked_hit]] = True\n    return invalid\n\n\ndef score_pool_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    pool: torch.Tensor,\n    pool_log_q: torch.Tensor,\n    pool_position: np.ndarray,\n    user_rows: np.ndarray,\n    liked_items: np.ndarray,\n    known_positive_packed: np.ndarray,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Sampled-softmax over a block-shared popularity-tempered negative pool.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    pool_numpy = pool.numpy()\n    positives_numpy = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    # Bengio-Senecal correction: subtract log(K * q) from every sampled logit.\n    correction = (pool_log_q + float(np.log(len(pool_numpy)))).to(device)\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        unique_positives, inverse = torch.unique(\n            positive_items[begin:end], return_inverse=True\n        )\n        positive_vectors = model.encode_items(unique_positives)[inverse]\n        pool_vectors = model.encode_items(pool.to(device))\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = query_vectors @ pool_vectors.T\n        invalid = pool_false_negative_mask(\n            pool_numpy,\n            pool_position,\n            positives_numpy[begin:end],\n            user_rows[begin:end],\n            known_positive_packed,\n            liked_items[begin:end],\n        )\n        corrected = negative_logits - correction[None, :]\n        corrected = corrected.masked_fill(\n            torch.from_numpy(invalid).to(device), -1.0e30\n        )\n        shifted = corrected - positive_logits[:, None]\n        row_loss = torch.logsumexp(\n            torch.cat(\n                [torch.zeros_like(positive_logits[:, None]), shifted], dim=1\n            ),\n            dim=1,\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (selected_weights * row_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"sampled-softmax training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        with torch.no_grad():\n            valid = torch.from_numpy(~invalid).to(device)\n            statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n            statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n            statistics[\"negative_sum\"] += float(\n                (negative_logits.detach() * valid).sum().cpu()\n            ) / max(int(valid.sum().cpu()), 1) * (end - begin)\n            statistics[\"pair_correct\"] += float(\n                (\n                    (positive_logits.detach()[:, None] > negative_logits.detach())\n                    & valid\n                ).sum().cpu()\n            ) / max(int(valid.sum().cpu()), 1) * (end - begin)\n            statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:"
          },
          "result": {
            "timestamp": "2026-09-24T17:40:48.040Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:48.051Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14920626 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 11,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:00.625Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:00.625Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:03.051Z",
          "callId": "toolu_012a1sb3v17xHobeuyiUv1yv",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()",
            "new_string": "    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    pool_probabilities: torch.Tensor | None,\n    pool_log_q: torch.Tensor | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:03.182Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:03.190Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14919549 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 12,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:08.061Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:08.061Z",
          "callId": "toolu_019xrM7Jhv4B5v7xkNqXmPHU",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives",
            "new_string": "    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    pooled = config.loss_objective == \"sampled-softmax\"\n    negative_count = 1 if pooled else (\n        config.uniform_negatives + config.inbatch_negatives\n    )"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:08.109Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:08.121Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14919206 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 13,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:16.510Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:41:16.510Z",
          "text": "Now the training inner loop dispatch:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:25.588Z",
          "callId": "toolu_01UadRKTJgZFB2PQiKP5Bi4y",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )",
            "new_string": "            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            optimizer.zero_grad(set_to_none=True)\n            if pooled:\n                if pool_probabilities is None or known_positive_packed is None:\n                    raise RuntimeError(\"sampled-softmax requires the pool proposal\")\n                pool = draw_shared_negative_pool(\n                    pool_probabilities, config.negative_pool_size, generator\n                )\n                pool_position = np.full(len(prepared.item_ids), -1, dtype=np.int64)\n                pool_position[pool.numpy()] = np.arange(len(pool), dtype=np.int64)\n                statistics, sequence_gradient = score_pool_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    pool,\n                    pool_log_q[pool],\n                    pool_position,\n                    flat_user_rows,\n                    prepared.liked_states[flat_states].astype(np.int64),\n                    known_positive_packed,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=True,\n                )\n            else:\n                negatives, negative_weights = draw_training_negatives(\n                    positive_items,\n                    known_history,\n                    liked_items,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared,\n                    config,\n                    item_correction,\n                    generator,\n                    device,\n                )\n                statistics, sequence_gradient = score_target_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    negatives,\n                    negative_weights,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=True,\n                )"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:25.611Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:25.619Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14917985 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 14,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:32.046Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:32.046Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:33.664Z",
          "callId": "toolu_01CxPCeZEwRBmk1fKe44RvEg",
          "name": "Bash",
          "input": {
            "command": "cd /app/solution && python - <<'EOF'\nimport re\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(\"pool_position = np.full(len(prepared.item_ids), -1, dtype=np.int64)\",\n            \"pool_position = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int64)\")\nopen(p,'w').write(s)\nprint(s.count(\"len(prepared.item_ids) + 1, -1\"))\nEOF\ntail -3 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:33.993Z",
            "content": "Traceback (most recent call last):\n  File \"<stdin>\", line 6, in <module>\nPermissionError: [Errno 13] Permission denied: 'train_current_model.py'\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",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:34.000Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14917491 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 15,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:38.442Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:38.442Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:40.118Z",
          "callId": "toolu_01Vdip6BYEft9qhEvUUTKnDn",
          "name": "Bash",
          "input": {
            "command": "chmod u+w /app/solution/*.py && python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(\"pool_position = np.full(len(prepared.item_ids), -1, dtype=np.int64)\",\n            \"pool_position = np.full(len(prepared.item_ids) + 1, -1, dtype=np.int64)\")\nopen(p,'w').write(s)\nprint(s.count(\"len(prepared.item_ids) + 1, -1\"))\nEOF\nps aux | grep -c train_current; tail -2 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:40.325Z",
            "content": "1\n5\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",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:40.424Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14916904 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 16,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:47.107Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:47.107Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:49.650Z",
          "callId": "toolu_01KMsbvJzT89QzxaTEyMsTEv",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"",
            "new_string": "    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    pool_probabilities: torch.Tensor | None,\n    pool_log_q: torch.Tensor | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic loss on each user's chronological target suffix.\"\"\""
          },
          "result": {
            "timestamp": "2026-09-24T17:41:49.693Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:49.710Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14916283 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 17,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:00.651Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:00.651Z",
          "callId": "toolu_01Hevfgo7sPDg31ZH8W81Qka",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )",
            "new_string": "            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            if config.loss_objective == \"sampled-softmax\":\n                if pool_probabilities is None or known_positive_packed is None:\n                    raise RuntimeError(\"sampled-softmax requires the pool proposal\")\n                pool = draw_shared_negative_pool(\n                    pool_probabilities, config.negative_pool_size, generator\n                )\n                pool_position = np.full(\n                    len(prepared.item_ids) + 1, -1, dtype=np.int64\n                )\n                pool_position[pool.numpy()] = np.arange(len(pool), dtype=np.int64)\n                statistics, _ = score_pool_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    pool,\n                    pool_log_q[pool],\n                    pool_position,\n                    flat_user_rows,\n                    prepared.liked_states[flat_states].astype(np.int64),\n                    known_positive_packed,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=False,\n                )\n            else:\n                negatives, negative_weights = draw_training_negatives(\n                    positive_items,\n                    known_history,\n                    liked_items,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared,\n                    config,\n                    item_correction,\n                    generator,\n                    device,\n                )\n                statistics, _ = score_target_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    negatives,\n                    negative_weights,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=False,\n                )"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:00.711Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:00.719Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14915111 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 18,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:03.861Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:42:03.861Z",
          "text": "Now wire the proposal into `train_model` and the CLI:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:06.845Z",
          "callId": "toolu_01Ex4fFMMhQA6injPADmR1sb",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )",
            "new_string": "    target_item_counts = selected_target_counts(\n        prepared, partition.window_starts, training_counts\n    )\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    pool_probabilities: torch.Tensor | None = None\n    pool_log_q: torch.Tensor | None = None\n    if config.loss_objective == \"sampled-softmax\":\n        pool_probabilities, pool_log_q = build_negative_sampling_distribution(\n            target_item_counts, config\n        )"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:06.881Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:06.895Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14914664 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 19,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:13.637Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:13.637Z",
          "callId": "toolu_01PfsxUe7LzJSeQdQXe5Up11",
          "name": "Bash",
          "input": {
            "command": "python - <<'PY'\np='train_current_model.py'\ns=open(p).read()\nold_calls = [\n\"\"\"            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",\n]\ns = s.replace(old_calls[0], \"\"\"            item_correction,\n            known_positive_packed,\n            pool_probabilities,\n            pool_log_q,\n            generator,\n            device,\n            epoch,\n        )\"\"\")\n# three call sites of evaluate_validation_bce\ns = s.replace(\"\"\"                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\", \"\"\"                    item_correction,\n                    known_positive_packed,\n                    pool_probabilities,\n                    pool_log_q,\n                    device,\n                )\"\"\")\ns = s.replace(\"\"\"            item_correction,\n            known_positive_packed,\n            device,\n        )\"\"\", \"\"\"            item_correction,\n            known_positive_packed,\n            pool_probabilities,\n            pool_log_q,\n            device,\n        )\"\"\")\nopen(p,'w').write(s)\nPY\ngrep -n \"pool_probabilities,$\" train_current_model.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:13.683Z",
            "content": "1959:                    pool_probabilities,\n1984:            pool_probabilities,\n2000:            pool_probabilities,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:13.778Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14914173 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 20,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:22.591Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:22.591Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:23.587Z",
          "callId": "toolu_01Dwf2yJh6omEK2ytQeMb5Fc",
          "name": "Bash",
          "input": {
            "command": "sed -n 1950,2010p train_current_model.py | grep -n \"evaluate_validation_bce\\|run_training_epoch\\|pool_probabilities\"; echo ---; tail -2 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:23.704Z",
            "content": "10:                    pool_probabilities,\n25:        diagnostics = run_training_epoch(\n35:            pool_probabilities,\n41:        validation = evaluate_validation_bce(\n51:            pool_probabilities,\n---\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=235.2s",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:23.709Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14913869 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 21,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:30.535Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:30.535Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:33.379Z",
          "callId": "toolu_01UvYoShVKxinxC658seEU6V",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    parser.add_argument(\n        \"--negative-filter-scope\",",
            "new_string": "    parser.add_argument(\n        \"--loss-objective\",\n        choices=(\"sampled-softmax\", \"bce\"),\n        default=Config.loss_objective,\n    )\n    parser.add_argument(\n        \"--negative-pool-size\", type=int, default=Config.negative_pool_size\n    )\n    parser.add_argument(\n        \"--negative-pool-alpha\", type=float, default=Config.negative_pool_alpha\n    )\n    parser.add_argument(\n        \"--negative-pool-uniform-mix\",\n        type=float,\n        default=Config.negative_pool_uniform_mix,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\","
          },
          "result": {
            "timestamp": "2026-09-24T17:42:33.404Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:33.414Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14913234 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 22,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:38.069Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:38.069Z",
          "callId": "toolu_01ETChvYJepcmkVAS7jZpk8d",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,",
            "new_string": "        loss_microbatch_size=args.loss_microbatch_size,\n        loss_objective=args.loss_objective,\n        negative_pool_size=args.negative_pool_size,\n        negative_pool_alpha=args.negative_pool_alpha,\n        negative_pool_uniform_mix=args.negative_pool_uniform_mix,\n        negative_filter_scope=args.negative_filter_scope,"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:38.118Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:38.125Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14912856 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 23,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:44.366Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:44.366Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:45.494Z",
          "callId": "toolu_0192ZGDbNb9pHLzSNChLWR4z",
          "name": "Bash",
          "input": {
            "command": "python -c \"import ast,sys; ast.parse(open('train_current_model.py').read())\" && echo OK && python -c \"\nimport sys; sys.argv=['x']\n\" && grep -n \"def score_pool_microbatches\" -A2 train_current_model.py | head"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:45.800Z",
            "content": "OK\n1294:def score_pool_microbatches(\n1295-    model: base.SequentialTwoTower,\n1296-    prepared: SequenceData,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:45.803Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14912379 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 24,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:52.432Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:52.432Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:54.090Z",
          "callId": "toolu_01GZq5yLQFYPYzHHgXWtYBsj",
          "name": "Bash",
          "input": {
            "command": "timeout 900 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --max-train-targets-per-user 3 --max-validation-targets-per-user 1 --negative-pool-size 512 --threads 3 --output /app/solution/smoke.parquet > smoke.log 2>&1; echo EXIT=$?; tail -30 smoke.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:44:54.150Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:44:54.156Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911909 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 25,
      "role": "agent",
      "timestamp": "2026-09-24T17:44:58.105Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:44:58.105Z",
          "callId": "toolu_01TW1ZXFWGBACDZ5gv4YRhvi",
          "name": "Bash",
          "input": {
            "command": "tail -20 /app/solution/smoke.log; echo ===; tail -2 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:44:58.149Z",
            "content": "loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=27,079 validation=9,053 selected=36,132 discarded_old_targets=20,011,062 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.000 max=1.982\nnegative proposal: alpha=0.75 uniform_mix=0.3 min=1.000e-05 max=9.786e-04 mass_top1000=0.173\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,561,593 bytes=34,256,250\nepoch=1/1 train_loss=9.65656 validation_loss=9.63640 pair_accuracy=0.620 train_targets=27,079 validation_targets=9,053 train_seconds=54.3 validation_seconds=3.7\nrestored best validation-BCE checkpoint: epoch=1 loss=9.636400\n===\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=251.3 validation_seconds=22.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:44:58.151Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911777 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 26,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:08.468Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:45:08.468Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:09.220Z",
          "callId": "toolu_01XwbnQgEfrzbeQKsRrCf2ay",
          "name": "Bash",
          "input": {
            "command": "pkill -f \"smoke.parquet\" ; python /task/evaluate_public.py /app/solution/production_validation.parquet"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:09.277Z",
            "content": "Exit code 144",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:09.282Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14910671 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 27,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:12.674Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:12.674Z",
          "callId": "toolu_012bcVV3nsvRXGvSw97k6kB1",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:16.075Z",
            "content": "Exit code 1\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/production_validation.parquet'",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:16.077Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14910558 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 28,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:21.077Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:45:21.077Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:21.510Z",
          "callId": "toolu_019uKSTWKzWKXshd1Mkg63cw",
          "name": "Bash",
          "input": {
            "command": "ps aux | grep train_current | grep -v grep; echo ===; tail -2 /app/solution/baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:21.593Z",
            "content": "agent       8130  0.0  0.0   4492  1816 ?        S    17:37   0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790271398325-eezjt2.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \\builtin unalias -- 'unsetenv'; \\builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'cd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/production_validation.parquet > /app/solution/baseline_val.log 2>&1 & echo started $!' < /dev/null && pwd -P >| /tmp/claude-803c-cwd\nagent       8131  533 32.0 7753436 5117752 ?     Rl   17:37  42:45 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/production_validation.parquet\n===\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=251.3 validation_seconds=22.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:21.602Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909653 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 29,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:34.401Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:45:34.401Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:36.002Z",
          "callId": "toolu_013BiFCwn21t26VhiJJCVc4o",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --threads 6 --output /app/solution/improved_validation.parquet > /app/solution/improved_val.log 2>&1 &\nsleep 2\nnohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --prepare-only > /app/solution/prep_sub.log 2>&1 &\necho launched"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:38.124Z",
            "content": "launched",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:38.135Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14908267 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 30,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:41.451Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:41.451Z",
          "callId": "toolu_01WCmdAwuEDkhVLZwoXwmJb7",
          "name": "Bash",
          "input": {
            "command": "sleep 150; tail -3 /app/solution/baseline_val.log; echo === IMPROVED; tail -4 /app/solution/improved_val.log; echo === PREP; tail -2 /app/solution/prep_sub.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:47:41.530Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:47:41.536Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14908120 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 31,
      "role": "agent",
      "timestamp": "2026-09-24T17:47:45.006Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:47:45.006Z",
          "callId": "toolu_015XfaAskW68hRAAJ2sBusXj",
          "name": "Bash",
          "input": {
            "command": "tail -2 /app/solution/baseline_val.log; echo === IMPROVED; tail -4 /app/solution/improved_val.log; echo === PREP; tail -2 /app/solution/prep_sub.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:47:45.076Z",
            "content": "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=251.3 validation_seconds=22.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n=== IMPROVED\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nnegative proposal: alpha=0.75 uniform_mix=0.3 min=1.000e-05 max=7.475e-04 mass_top1000=0.138\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=== PREP\n                              [--prepare-only]\ntrain_current_model.py: error: the following arguments are required: --output",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:47:45.081Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14907948 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 32,
      "role": "agent",
      "timestamp": "2026-09-24T17:47:49.510Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:47:49.510Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:47:50.564Z",
          "callId": "toolu_01EHkKzn7GuCyXiivRqXWnRf",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --prepare-only --output /app/solution/unused.parquet > /app/solution/prep_sub.log 2>&1 &\ngrep -c \"ranked users\" /app/solution/baseline_val.log; grep \"ranked users\" /app/solution/baseline_val.log | tail -2"
          },
          "result": {
            "timestamp": "2026-09-24T17:47:50.697Z",
            "content": "0",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:47:50.704Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14907359 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 33,
      "role": "agent",
      "timestamp": "2026-09-24T17:48:13.201Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:48:13.201Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:48:14.084Z",
          "callId": "toolu_01Fam2iZw2CbPtW7W2eh5DVo",
          "name": "Bash",
          "input": {
            "command": "sleep 100; tail -2 /app/solution/prep_sub.log; echo === IMP; tail -2 /app/solution/improved_val.log; echo === BASE; tail -1 /app/solution/baseline_val.log; ls -la /app/solution/"
          },
          "result": {
            "timestamp": "2026-09-24T17:49:54.378Z",
            "content": "prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=91.9s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=104.2s\n=== IMP\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=== BASE\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\ntotal 522824\ndrwxr-xr-x 1 agent agent      4096 Sep 24 17:45 .\ndrwxr-xr-x 1 agent agent      4096 Sep 24 17:32 ..\n-rw-r--r-- 1 agent agent      1560 Sep 24 17:42 baseline_val.log\n-rw-r--r-- 1 agent agent 535225808 Sep 24 17:38 cache_validation.npz\n-rw-r--r-- 1 agent agent       504 Sep 24 17:45 improved_val.log\n-rw-r--r-- 1 agent agent       588 Sep 24 17:49 prep_sub.log\n-rw-r--r-- 1 agent agent       720 Sep 24 17:44 smoke.log\n-rw-r--r-- 1 agent agent     92907 Sep 24 17:42 train_current_model.py\n-rw-r--r-- 1 agent agent     15985 Sep 24 17:37 two_tower_model.py",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:49:54.420Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14905980 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 34,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:02.555Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:50:02.555Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:03.478Z",
          "callId": "toolu_01Grc7e4erbVt12uTP3cmpDF",
          "name": "Bash",
          "input": {
            "command": "for i in $(seq 1 22); do if ls /app/solution/cache_submission.npz >/dev/null 2>&1; then echo READY; break; fi; sleep 5; done; tail -1 /app/solution/prep_sub.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:03.576Z",
            "content": "READY\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:03.578Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14904951 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 35,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:07.655Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:07.655Z",
          "callId": "toolu_01VgBZbcE51Kz1kQ47jBJAAK",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --threads 6 --output /app/solution/submission.parquet > /app/solution/submission_run.log 2>&1 &\nsleep 5; echo launched; tail -1 /app/solution/improved_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:12.680Z",
            "content": "launched\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:12.682Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14904751 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 36,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:22.020Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:50:22.020Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:25.576Z",
          "callId": "toolu_01AMiwyfq3zpw6hjohRGEabj",
          "name": "Bash",
          "input": {
            "command": "python - <<'PY'\nimport numpy as np, pandas as pd\nd='/task/data/'\ncat=pd.read_parquet(d+'candidate_catalog.parquet').sort_values('popularity_rank')\nitems=cat.item_id.to_numpy(np.int64)\nz=np.load(d+'test_eligible_candidates.npz')\nuids=z['uids']; iid=z['item_ids']; packed=z['packed_eligible']\nassert np.array_equal(iid, items)\nrows=[]\nfor r,u in enumerate(uids):\n    m=np.unpackbits(packed[r],count=len(iid),bitorder='little').astype(bool)\n    sel=items[m][:100]\n    assert len(sel)==100, (u,len(sel))\n    for k,it in enumerate(sel,1): rows.append((int(u),int(it),k))\nf=pd.DataFrame(rows,columns=['uid','item_id','rank'])\nf.to_parquet('/app/solution/fallback_submission.parquet',index=False)\nprint(f.shape, f.uid.nunique())\nPY"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:30.188Z",
            "content": "(845600, 3) 8456",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:30.191Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14904021 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 37,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:33.692Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:33.692Z",
          "callId": "toolu_01Wf3D4FQVYKjksT26UrspYU",
          "name": "Bash",
          "input": {
            "command": "cp /app/solution/fallback_submission.parquet /app/solution/submission.parquet; ls -la /app/solution/*.parquet; tail -1 /app/solution/improved_val.log; tail -1 /app/solution/submission_run.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:33.733Z",
            "content": "-rw-r--r-- 1 agent agent 735840 Sep 24 17:50 /app/solution/fallback_submission.parquet\n-rw-r--r-- 1 agent agent 735840 Sep 24 17:50 /app/solution/submission.parquet\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:33.736Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14903862 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 38,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:37.184Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:50:37.184Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:37.964Z",
          "callId": "toolu_01JvpuRYtFQpXfVZNsf1vLtw",
          "name": "Bash",
          "input": {
            "command": "sleep 170; echo BASE; tail -1 /app/solution/baseline_val.log; echo IMP; tail -1 /app/solution/improved_val.log; echo SUB; tail -1 /app/solution/submission_run.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:52:38.009Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:52:38.013Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14903491 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 39,
      "role": "agent",
      "timestamp": "2026-09-24T17:52:43.209Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:52:43.209Z",
          "callId": "toolu_01MgDAD9eZ6fj7erV1Gz7wK8",
          "name": "Bash",
          "input": {
            "command": "echo BASE; tail -1 /app/solution/baseline_val.log; echo IMP; tail -1 /app/solution/improved_val.log; echo SUB; tail -1 /app/solution/submission_run.log; ls /app/solution/*.parquet"
          },
          "result": {
            "timestamp": "2026-09-24T17:52:43.260Z",
            "content": "BASE\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\nIMP\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nSUB\nepoch=1/1 batches=250 targets=994,228 batch_bce=8.662842 running_bce=9.366468 elapsed=132.2s\n/app/solution/fallback_submission.parquet\n/app/solution/submission.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:52:43.263Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14903305 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 40,
      "role": "agent",
      "timestamp": "2026-09-24T17:53:08.975Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:53:08.975Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:53:09.305Z",
          "callId": "toolu_01KUxoo6P9wCcNzBQaYAmA4z",
          "name": "Bash",
          "input": {
            "command": "top -bn1 | head -14"
          },
          "result": {
            "timestamp": "2026-09-24T17:53:09.513Z",
            "content": "top - 17:53:09 up 26 min,  0 user,  load average: 6.94, 8.75, 6.01\nTasks:   9 total,   2 running,   7 sleeping,   0 stopped,   0 zombie\n%Cpu(s): 76.9 us,  0.0 sy,  0.0 ni, 11.5 id, 11.5 wa,  0.0 hi,  0.0 si,  0.0 st \nMiB Mem :  15614.2 total,   4464.5 free,   4503.2 used,   6999.7 buff/cache     \nMiB Swap:      0.0 total,      0.0 free,      0.0 used.  11111.1 avail Mem \n\n    PID USER      PR  NI    VIRT    RES    SHR S  %CPU  %MEM     TIME+ COMMAND\n   8349 agent     20   0 5047752   3.5g 139108 R 600.0  22.6  16:03.52 python\n   8034 agent     20   0 5572408 208540  52472 S   6.7   1.3   0:16.36 claude\n      1 root      20   0    2592   1640   1532 S   0.0   0.0   0:00.02 sh\n      7 root      20   0    2920   1676   1576 S   0.0   0.0   0:00.00 sleep\n   8027 agent     20   0    4480   3344   2996 S   0.0   0.0   0:00.01 bash\n   8035 agent     20   0    2924   1624   1512 S   0.0   0.0   0:00.02 tee\n   8408 agent     20   0    4492   3240   2872 S   0.0   0.0   0:00.00 bash",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:53:09.517Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14901504 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 41,
      "role": "agent",
      "timestamp": "2026-09-24T17:53:18.095Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:53:18.095Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:53:18.931Z",
          "callId": "toolu_01NjEpb8zvtcsWzds5PrfeUL",
          "name": "Bash",
          "input": {
            "command": "ps -o pid,cmd -p 8349; echo ===; tail -3 /app/solution/baseline_val.log; echo ===IMP; tail -3 /app/solution/improved_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:53:18.973Z",
            "content": "    PID CMD\n   8349 python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --threads 6 --output /app/solution/submission.parquet\n===\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=235.2s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=251.3 validation_seconds=22.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n===IMP\nnegative proposal: alpha=0.75 uniform_mix=0.3 min=1.000e-05 max=7.475e-04 mass_top1000=0.138\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",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:53:18.976Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14900683 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 42,
      "role": "agent",
      "timestamp": "2026-09-24T17:53:31.030Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:53:31.030Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:53:31.680Z",
          "text": "The submission run survived (the two validation runs were OOM-killed by memory contention). Writing the README while it ranks:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:53:53.900Z",
          "callId": "toolu_01HcdoQzFnMG78srHH1boe2H",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/README.md",
            "content": "# Made-for-You two-tower: negative-sampling fix\n\n## Diagnosis\n\nThe production trainer's data protocol, sequence encoder and indexed serving path\nare fine. The weak point is the *training objective's negative distribution*.\n\nPer target the baseline draws 32 negatives (`train_current_model.py`,\n`draw_full_history_filtered_negatives`):\n\n1. **24 uniform draws over the 30,000-track catalog.** In a Zipf-shaped music\n   catalog a uniform draw is almost always a deep-tail track. After a few hundred\n   updates those are separated from the positive by a wide margin, `softplus(s)`\n   saturates near zero, and they contribute essentially no gradient. The negative\n   budget is spent where the ranking decision is *not* being made.\n2. **8 \"in-batch\" draws that are not in-batch.** They are sampled *uniformly from\n   `torch.unique(targets)`* — the de-duplication destroys exactly the\n   popularity-proportional property that makes in-batch negatives hard. A\n   `((1/N)/p)^0.5` propensity correction is then applied on top, which tilts the\n   already-uniform sample *further* toward the tail. The two steps compound\n   instead of cancelling, so all 32 negatives are effectively near-uniform.\n\nNet effect: the model learns to separate a user's next track from random tail\ntracks, but is never asked to separate it from the plausible head/mid-catalog\ntracks that actually compete for the top-10 slots the metric scores. NDCG@10 is\ndecided among near-neighbours, which the objective never shows the model.\n\nA secondary, mechanical limitation: negatives are materialised **per row**\n(`B x 32 x D` item-tower gathers), which is what caps the count at 32.\n\n## Change\n\nReplaced per-row BCE-with-32-near-uniform-negatives by a **sampled softmax over a\nblock-shared, popularity-tempered negative pool** (`--loss-objective\nsampled-softmax`, the new default):\n\n- **Proposal** `q(i) = 0.7 * popularity_i^0.75 / Z + 0.3 / N`, estimated from the\n  selected training targets (`build_negative_sampling_distribution`). The\n  tempered popularity term supplies genuinely hard negatives; the 30% uniform\n  mixture keeps tail coverage and bounds the importance weights.\n- **Pool** 2,048 items drawn without replacement per BPTT block with the Gumbel\n  top-k trick (`draw_shared_negative_pool`). Sharing the pool across the whole\n  block turns the negative term into a single `(B,192) x (192,2048)` GEMM, so\n  64x more negatives per target costs *less* wall-clock than the old per-row\n  `B x 32 x D` gather.\n- **Loss** `-log( e^{s+} / (e^{s+} + sum_j e^{s_j - log(K q_j)}) )`, i.e. an\n  unbiased Bengio–Senecal log-Q-corrected estimator of the full 30k-way softmax.\n  This optimises the ranking directly rather than per-pair calibration, and the\n  log-Q term prevents the popularity-skewed proposal from biasing the scores.\n- **False negatives** are still fully excluded: each row masks out its own target,\n  its complete public-training positive-listen history (the existing packed\n  filter) and its active explicit likes, via a pool-position lookup table\n  (`pool_false_negative_mask`).\n\nEverything else is untouched: chronological windows and cutoffs, target\nweighting, the 100-step truncated-BPTT protocol, the GRU user tower, the\ncatalog-aware item tower, raw dot-product scoring, and the precomputed-item-vector\nserving path. No cross-encoder, no per-candidate sequence model, no user-ID table.\n\nThe old objective remains available with `--loss-objective bce`.\n\n## Results\n\n| Run | public validation NDCG@10 |\n| --- | --- |\n| production baseline (`--loss-objective bce`) | see `baseline_val.log` / below |\n| sampled-softmax pool (`--loss-objective sampled-softmax`) | see below |\n\nTraining diagnostics for the two objectives are not comparable directly (BCE vs.\nsampled-softmax nats); compare the evaluator score.\n\n## Commands\n\nPublic backtest:\n\n```sh\npython /app/solution/train_current_model.py \\\n  --stage validation --public-dir /task \\\n  --prepared-cache /app/solution/cache_validation.npz \\\n  --output /app/solution/improved_validation.parquet\npython /task/evaluate_public.py /app/solution/improved_validation.parquet\n```\n\nFinal submission (refit on `train_* + val_*`):\n\n```sh\npython /app/solution/train_current_model.py \\\n  --stage submission --public-dir /task \\\n  --prepared-cache /app/solution/cache_submission.npz \\\n  --output /app/solution/submission.parquet\n```\n\nNote: the two stages need ~4 GB RSS each; run them sequentially.\n"
          },
          "result": {
            "timestamp": "2026-09-24T17:53:53.929Z",
            "content": "File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:53:53.932Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14897895 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 43,
      "role": "agent",
      "timestamp": "2026-09-24T17:53:59.302Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:53:59.302Z",
          "callId": "toolu_01SQud51sS6jt92yg2eKTSrU",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -2 /app/solution/submission_run.log; grep -c \"ranked users\" /app/solution/submission_run.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:55:54.352Z",
            "content": "  \"device\": \"cpu\"\n}\n9",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:55:54.354Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14897741 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 44,
      "role": "agent",
      "timestamp": "2026-09-24T17:55:58.721Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:55:58.721Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:56:03.392Z",
          "callId": "toolu_01W8cz6m5DWx9DpSh7vePcsu",
          "name": "Bash",
          "input": {
            "command": "ls -la submission.parquet && python - <<'PY'\nimport numpy as np, pandas as pd\nf=pd.read_parquet('/app/solution/submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nz=np.load('/task/data/test_eligible_candidates.npz')\nuids=z['uids']; iid=z['item_ids']; packed=z['packed_eligible']\nprint(f.shape, f.columns.tolist(), f.dtypes.to_dict())\nprint('users ok', set(f.uid.astype(int))==t, f.uid.nunique())\ng=f.groupby('uid')\nprint('rows100', (g.size()==100).all(), 'distinct', (g.item_id.nunique()==100).all())\nprint('ranks', f.groupby('uid')['rank'].apply(lambda s: sorted(s)==list(range(1,101))).all())\npos={int(u):r for r,u in enumerate(uids)}\nidx=pd.Index(iid)\nbad=0\nfor u,sub in g:\n    r=pos[int(u)]\n    m=np.unpackbits(packed[r],count=len(iid),bitorder='little').astype(bool)\n    cols=idx.get_indexer(sub.item_id.to_numpy())\n    if (cols<0).any() or not m[cols].all(): bad+=1\nprint('bad users',bad)\nPY"
          },
          "result": {
            "timestamp": "2026-09-24T17:56:07.114Z",
            "content": "-rw-r--r-- 1 agent agent 1174053 Sep 24 17:54 submission.parquet\n(845600, 3) ['uid', 'item_id', 'rank'] {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}\nusers ok True 8456\nrows100 True distinct True\nranks True\nbad users 0",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:07.116Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14897080 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 45,
      "role": "agent",
      "timestamp": "2026-09-24T17:56:14.177Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:56:14.177Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:56:14.636Z",
          "text": "Submission is valid. Now getting the before/after validation scores with the remaining time:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:56:16.766Z",
          "callId": "toolu_019bkfSzvnFcEgzy9fHMAbuz",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 4 --output improved_validation.parquet > improved_val.log 2>&1 &\nsleep 3\ncd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 4 --loss-objective bce --loss-microbatch-size 65536 --output production_validation.parquet > baseline_val.log 2>&1 &\nsleep 40; free -g | head -2; tail -1 improved_val.log; tail -1 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:56:59.836Z",
            "content": "               total        used        free      shared  buff/cache   available\nMem:              15           5           2           0           7           9\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:59.845Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14896316 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 46,
      "role": "agent",
      "timestamp": "2026-09-24T17:57:03.966Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:57:03.966Z",
          "callId": "toolu_01FRdrNGGhbjHjV4xd3M3PrY",
          "name": "Bash",
          "input": {
            "command": "sleep 115; free -g|sed -n 2p; echo IMP; tail -1 improved_val.log; echo BASE; tail -1 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T17:58:59.056Z",
            "content": "Mem:              15           7           1           0           7           8\nIMP\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nBASE\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:58:59.059Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14896083 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 47,
      "role": "agent",
      "timestamp": "2026-09-24T17:59:03.964Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:59:03.964Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:59:04.441Z",
          "callId": "toolu_01XgifHmTyhH78KduPXPkJbh",
          "name": "Bash",
          "input": {
            "command": "sleep 230; echo IMP; tail -1 improved_val.log; echo BASE; tail -1 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T18:01:04.562Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:01:04.594Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895749 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 48,
      "role": "agent",
      "timestamp": "2026-09-24T18:01:07.891Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:01:07.891Z",
          "callId": "toolu_01FGszPDw2iR8nniMW1uB7Mo",
          "name": "Bash",
          "input": {
            "command": "echo IMP; tail -1 improved_val.log; echo BASE; tail -1 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T18:01:07.965Z",
            "content": "IMP\nrestored best validation-BCE checkpoint: epoch=1 loss=9.100223\nBASE\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:01:07.968Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895613 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 49,
      "role": "agent",
      "timestamp": "2026-09-24T18:01:17.765Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:01:17.765Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:01:18.373Z",
          "callId": "toolu_01CxX33fm9B3hgrSJ9LZXXy8",
          "name": "Bash",
          "input": {
            "command": "sleep 115; ls -la improved_validation.parquet 2>/dev/null; echo IMP; tail -1 improved_val.log; echo BASE; tail -1 baseline_val.log"
          },
          "result": {
            "timestamp": "2026-09-24T18:03:13.450Z",
            "content": "-rw-r--r-- 1 agent agent 1179078 Sep 24 18:02 improved_validation.parquet\nIMP\n}\nBASE\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:03:13.461Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895351 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 50,
      "role": "agent",
      "timestamp": "2026-09-24T18:03:17.663Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:03:17.663Z",
          "callId": "toolu_0194fAGEAxFHr72DZaJa9eNi",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py /app/solution/improved_validation.parquet"
          },
          "result": {
            "timestamp": "2026-09-24T18:03:22.876Z",
            "content": "{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.020273,\n    \"quality_users\": 4263\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:03:22.878Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895170 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 51,
      "role": "agent",
      "timestamp": "2026-09-24T18:03:29.223Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:03:29.223Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:03:29.481Z",
          "callId": "toolu_017hMqHVzNxyyz2QJrmpVsw3",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -1 baseline_val.log; ls -la production_validation.parquet 2>/dev/null"
          },
          "result": {
            "timestamp": "2026-09-24T18:05:24.505Z",
            "content": "}\n-rw-r--r-- 1 agent agent 1244229 Sep 24 18:04 production_validation.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:05:24.507Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14894847 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 52,
      "role": "agent",
      "timestamp": "2026-09-24T18:05:30.190Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:05:30.190Z",
          "callId": "toolu_015DwsnxxiV3y9oUM4yFFE29",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
          },
          "result": {
            "timestamp": "2026-09-24T18:05:33.678Z",
            "content": "{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:05:33.680Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14894705 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 53,
      "role": "agent",
      "timestamp": "2026-09-24T18:05:43.095Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:05:43.095Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:05:44.034Z",
          "text": "The change measured slightly **worse** (0.02027 vs 0.02128). Shipping the baseline instead — launching a baseline refit now:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:05:45.459Z",
          "callId": "toolu_018Xg4AxMznQeZAm1GcbAkpN",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache cache_submission.npz --threads 8 --loss-objective bce --loss-microbatch-size 65536 --output submission_bce.parquet > submission_bce.log 2>&1 &\necho launched; date"
          },
          "result": {
            "timestamp": "2026-09-24T18:05:45.483Z",
            "content": "launched\nThu Sep 24 18:05:45 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:05:45.485Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14893916 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 54,
      "role": "agent",
      "timestamp": "2026-09-24T18:06:08.635Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:06:08.635Z",
          "callId": "toolu_01VVLZThGqz24zwx52cQ2XZn",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/README.md",
            "content": "# Made-for-You two-tower: negative-sampling investigation\n\n## Diagnosis\n\nThe production trainer's data protocol, sequence encoder and indexed serving path\nare sound. The suspect component is the *training objective's negative\ndistribution*. Per target the baseline draws 32 negatives\n(`draw_full_history_filtered_negatives`):\n\n1. **24 uniform draws over the 30,000-track catalog.** In a Zipf-shaped music\n   catalog a uniform draw is almost always a deep-tail track. After a few hundred\n   updates those are separated from the positive by a wide margin, `softplus(s)`\n   saturates near zero, and they contribute essentially no gradient.\n2. **8 \"in-batch\" draws that are not in-batch.** They are sampled *uniformly from\n   `torch.unique(targets)`* — the de-duplication destroys exactly the\n   popularity-proportional property that makes in-batch negatives hard. A\n   `((1/N)/p)^0.5` propensity correction is then applied on top, tilting the\n   already-uniform sample *further* toward the tail. The two steps compound\n   rather than cancel, so effectively all 32 negatives are near-uniform.\n3. Negatives are materialised **per row** (`B x 32 x D` item-tower gathers),\n   which is what caps the count at 32.\n\nHypothesis: the model is never asked to separate the next track from the\nplausible head/mid-catalog tracks that actually compete for the top-10 slots.\n\n## Change implemented\n\nA **sampled softmax over a block-shared, popularity-tempered negative pool**\n(`--loss-objective sampled-softmax`):\n\n- **Proposal** `q(i) = 0.7 * popularity_i^0.75 / Z + 0.3 / N`, estimated from the\n  selected training targets (`build_negative_sampling_distribution`).\n- **Pool** 2,048 items drawn without replacement per BPTT block via Gumbel top-k\n  (`draw_shared_negative_pool`). Sharing the pool turns the negative term into a\n  single `(B,192) x (192,2048)` GEMM, so 64x more negatives per target cost\n  *less* wall-clock than the old per-row `B x 32 x D` gather (measured: 250\n  training blocks in 132 s vs. 235 s for the baseline).\n- **Loss** `-log( e^{s+} / (e^{s+} + sum_j e^{s_j - log(K q_j)}) )` — an unbiased\n  Bengio–Senecal log-Q-corrected estimator of the full 30k-way softmax.\n- **False negatives** still fully excluded per row: own target, the complete\n  public-training positive-listen history (existing packed filter) and active\n  explicit likes, via a pool-position lookup (`pool_false_negative_mask`).\n\nData protocol, cutoffs, target weighting, truncated-BPTT protocol, GRU user\ntower, catalog-aware item tower, raw dot-product scoring and the\nprecomputed-item-vector serving path are untouched. No cross-encoder, no\nper-candidate sequence model, no user-ID table.\n\n## Measured result: the hypothesis did not hold\n\n| Objective | public validation NDCG@10 |\n| --- | --- |\n| production baseline (`--loss-objective bce`) | **0.021280** |\n| sampled-softmax pool (`--loss-objective sampled-softmax`) | 0.020273 |\n\nBoth scored over the same 4,263 labelled users. Harder, popularity-tempered\nnegatives made offline discovery quality **4.7% worse**, not better. The likely\nreason is that the eligible candidate set already excludes everything the user has\nencountered, so the surviving head-catalog tracks are largely *unseen popular*\ntracks — which are disproportionately the true grade-1/grade-3 outcomes. Training\nthe scorer to push head items down therefore removes signal the metric rewards.\nThe baseline's near-uniform negatives leave the popularity prior in the score,\nand on this objective that prior is worth more than fine-grained head/mid\ndiscrimination.\n\nBecause the measured change is a regression, **the shipped artifact is the\nproduction objective**, refit on `train_* + val_*`. The sampled-softmax path is\nretained behind `--loss-objective sampled-softmax` (default is `bce`) so the\nnegative-sampling experiment is reproducible; a follow-up should sweep\n`--negative-pool-alpha` downward (0.0–0.3) and re-test, since that interpolates\nback toward the baseline proposal while keeping the much cheaper shared-pool\nsoftmax and its logQ correction.\n\n## Commands\n\nPublic backtest (either objective):\n\n```sh\npython /app/solution/train_current_model.py \\\n  --stage validation --public-dir /task \\\n  --prepared-cache /app/solution/cache_validation.npz \\\n  --loss-objective bce --loss-microbatch-size 65536 \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\nFinal submission (refit on `train_* + val_*`):\n\n```sh\npython /app/solution/train_current_model.py \\\n  --stage submission --public-dir /task \\\n  --prepared-cache /app/solution/cache_submission.npz \\\n  --loss-objective bce --loss-microbatch-size 65536 \\\n  --output /app/solution/submission.parquet\n```\n\nEach stage needs ~4 GB RSS; run stages sequentially.\n"
          },
          "result": {
            "timestamp": "2026-09-24T18:06:08.667Z",
            "content": "The file /app/solution/README.md has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:06:08.673Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14891953 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 55,
      "role": "agent",
      "timestamp": "2026-09-24T18:06:13.496Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:06:13.496Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:06:14.759Z",
          "callId": "toolu_01RTJrX3CZUaQiVLm4b9Xq8t",
          "name": "Bash",
          "input": {
            "command": "python - <<'PY'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace('    loss_microbatch_size: int = 4_096\\n    loss_objective: str = \"sampled-softmax\"',\n            '    loss_microbatch_size: int = 65_536\\n    loss_objective: str = \"bce\"')\nopen(p,'w').write(s)\nPY\ngrep -n \"loss_objective: str\\|loss_microbatch_size: int\" train_current_model.py; tail -1 submission_bce.log"
          },
          "result": {
            "timestamp": "2026-09-24T18:06:14.893Z",
            "content": "55:    loss_microbatch_size: int = 65_536\n56:    loss_objective: str = \"bce\"\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:06:15.007Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14891560 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 56,
      "role": "agent",
      "timestamp": "2026-09-24T18:06:19.360Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:06:19.360Z",
          "callId": "toolu_01Jrj8dTdwckcnrMs6ystNbh",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -1 submission_bce.log; date"
          },
          "result": {
            "timestamp": "2026-09-24T18:08:14.389Z",
            "content": "full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nThu Sep 24 18:08:14 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:08:14.395Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14891399 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 57,
      "role": "agent",
      "timestamp": "2026-09-24T18:08:20.933Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:08:20.933Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:08:21.271Z",
          "callId": "toolu_01BbTXp7miCcihpbAUxeJ46C",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -1 submission_bce.log; date"
          },
          "result": {
            "timestamp": "2026-09-24T18:10:16.341Z",
            "content": "epoch=1/1 batches=250 targets=994,228 batch_bce=0.460538 running_bce=0.541708 elapsed=238.8s\nThu Sep 24 18:10:16 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:10:16.347Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14891058 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 58,
      "role": "agent",
      "timestamp": "2026-09-24T18:10:19.165Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:10:19.165Z",
          "callId": "toolu_01EpmRQc5RSdTERT3P3if2jN",
          "name": "Bash",
          "input": {
            "command": "sleep 118; tail -1 submission_bce.log; date"
          },
          "result": {
            "timestamp": "2026-09-24T18:12:17.204Z",
            "content": "}\nThu Sep 24 18:12:17 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:12:17.206Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14890891 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 59,
      "role": "agent",
      "timestamp": "2026-09-24T18:12:21.037Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:12:21.037Z",
          "text": "It finished. Swapping in the baseline-objective submission and validating:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:12:26.090Z",
          "callId": "toolu_01S7o1DarA7ku8BuY1YJJuCv",
          "name": "Bash",
          "input": {
            "command": "cp submission_bce.parquet submission.parquet && python - <<'PY'\nimport numpy as np, pandas as pd\nf=pd.read_parquet('/app/solution/submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nz=np.load('/task/data/test_eligible_candidates.npz')\nuids=z['uids']; iid=z['item_ids']; packed=z['packed_eligible']\ng=f.groupby('uid')\nidx=pd.Index(iid); pos={int(u):r for r,u in enumerate(uids)}\nbad=0\nfor u,sub in g:\n    m=np.unpackbits(packed[pos[int(u)]],count=len(iid),bitorder='little').astype(bool)\n    c=idx.get_indexer(sub.item_id.to_numpy())\n    if (c<0).any() or not m[c].all(): bad+=1\nprint(\"rows\",len(f),\"users\",f.uid.nunique(),\"users_match\",set(f.uid.astype(int))==t)\nprint(\"100rows\",(g.size()==100).all(),\"distinct\",(g.item_id.nunique()==100).all())\nprint(\"ranks\",g['rank'].apply(lambda s: sorted(s)==list(range(1,101))).all())\nprint(\"dtypes\",f.dtypes.to_dict(),\"finite\",np.isfinite(f.to_numpy()).all())\nprint(\"ineligible_users\",bad)\nPY\nls -la /app/solution/submission.parquet; [ -L /app/solution/submission.parquet ] && echo SYMLINK || echo REGULAR"
          },
          "result": {
            "timestamp": "2026-09-24T18:12:29.763Z",
            "content": "rows 845600 users 8456 users_match True\n100rows True distinct True\nranks True\ndtypes {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')} finite True\nineligible_users 0\n-rw-r--r-- 1 agent agent 1212473 Sep 24 18:12 /app/solution/submission.parquet\nREGULAR",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:12:29.764Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14890224 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 60,
      "role": "agent",
      "timestamp": "2026-09-24T18:12:33.623Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:12:33.623Z",
          "callId": "toolu_01Dm1jaohJkdgwYmSL5kSBkn",
          "name": "Bash",
          "input": {
            "command": "rm -f unused.parquet smoke.parquet fallback_submission.parquet cache_validation.npz cache_submission.npz && ls -la"
          },
          "result": {
            "timestamp": "2026-09-24T18:12:33.673Z",
            "content": "total 4904\ndrwxr-xr-x 1 agent agent    4096 Sep 24 18:12 .\ndrwxr-xr-x 1 agent agent    4096 Sep 24 17:32 ..\n-rw-r--r-- 1 agent agent    4808 Sep 24 18:06 README.md\n-rw-r--r-- 1 agent agent    4080 Sep 24 18:04 baseline_val.log\n-rw-r--r-- 1 agent agent    4177 Sep 24 18:02 improved_val.log\n-rw-r--r-- 1 agent agent 1179078 Sep 24 18:02 improved_validation.parquet\n-rw-r--r-- 1 agent agent    2315 Sep 24 17:49 prep_sub.log\n-rw-r--r-- 1 agent agent 1244229 Sep 24 18:04 production_validation.parquet\n-rw-r--r-- 1 agent agent     720 Sep 24 17:44 smoke.log\n-rw-r--r-- 1 agent agent 1212473 Sep 24 18:12 submission.parquet\n-rw-r--r-- 1 agent agent    4071 Sep 24 18:12 submission_bce.log\n-rw-r--r-- 1 agent agent 1212473 Sep 24 18:12 submission_bce.parquet\n-rw-r--r-- 1 agent agent    4168 Sep 24 17:54 submission_run.log\n-rw-r--r-- 1 agent agent   92896 Sep 24 18:06 train_current_model.py\n-rw-r--r-- 1 agent agent   15985 Sep 24 17:37 two_tower_model.py",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:12:33.675Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14889957 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 61,
      "role": "agent",
      "timestamp": "2026-09-24T18:12:47.847Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:12:47.847Z",
          "text": "Done. Submission is in place and passes every stated validity check.\n\n## What I found\n\nI investigated the negative-sampling path as directed and found three real defects in `draw_full_history_filtered_negatives`:\n\n1. **24 of 32 negatives are uniform over a 30k Zipf catalog** — almost always deep-tail tracks that saturate `softplus` and stop producing gradient.\n2. **The 8 \"in-batch\" negatives are not in-batch.** They're drawn uniformly from `torch.unique(targets)`, which destroys the popularity-proportional property, and then a `((1/N)/p)^0.5` propensity correction tilts them *further* toward the tail. The two steps compound instead of cancelling, so effectively all 32 negatives are near-uniform.\n3. Per-row `B×32×D` materialization is what caps the negative count at 32.\n\n## What I built\n\nA sampled softmax over a **block-shared, popularity-tempered negative pool** (`--loss-objective sampled-softmax`): proposal `0.7·pop^0.75 + 0.3·uniform`, 2,048 items per block via Gumbel top-k, unbiased `−log(K·q)` logQ correction, with the full false-negative masking preserved. It's 64× more negatives per target at *less* wall-clock than the baseline (132 s vs 235 s per 250 blocks), since the negative term collapses to one GEMM. Serving contract, data protocol, and evaluation boundary are untouched.\n\n## The honest result\n\n| Objective | public validation NDCG@10 |\n| --- | --- |\n| production baseline | **0.021280** |\n| sampled-softmax pool | 0.020273 |\n\n**The hypothesis did not hold** — harder negatives were 4.7% *worse*. The plausible reason: candidate eligibility already excludes everything the user has encountered, so surviving head-catalog tracks are disproportionately the true grade-1/3 outcomes. Training the scorer to push head items down removes signal the metric rewards.\n\nSo the shipped artifact is the **production objective refit on `train_* + val_*`**, not my change. I reverted the config defaults to `bce` so the left-behind `train_current_model.py` reproduces the submission with the plain command; the pool path stays available behind the flag, and the README records the negative result plus the follow-up I'd run next (sweep `--negative-pool-alpha` toward 0, which keeps the cheaper shared-pool softmax while interpolating back to the baseline proposal).\n\nCaveat worth flagging: I had budget for one epoch and one configuration of the new objective. A single-setting regression is weak evidence that hard negatives can't help here — it rules out *this* proposal, not the family."
        }
      ]
    }
  ]
}