{
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  "taskId": "yambda-discovery-ranking-two-tower-case-1",
  "attemptId": "aws-opus-high-2026-08-28T12-51-33",
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  "model": "claude-opus-5",
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      "timestamp": "2026-08-28T20:02:00.018Z",
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          "timestamp": "2026-08-28T20:02:00.018Z",
          "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"
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            "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. 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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",
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          "timestamp": "2026-08-28T20:02:02.076Z",
          "text": "I'll start by investigating the production model and data."
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:02.978Z",
          "callId": "toolu_01HZx3HxW4fUWabmYWnxMGkm",
          "name": "Bash",
          "input": {
            "command": "ls -la /task/ /task/data/ /task/current_model/ && cat /task/requirements.txt",
            "description": "List task files"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:03.036Z",
            "content": "/task/:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Aug 28 19:57 .\ndrwxr-xr-x 1 root root 4096 Aug 28 20:00 ..\n-r--r--r-- 1 root root    0 Aug 28 19:55 .keep\ndr-xr-xr-x 1 root root 4096 Aug 28 19:55 current_model\ndr-xr-xr-x 1 root root 4096 Aug 28 19:56 data\n-r--r--r-- 1 root root 5774 Aug 28 19:55 evaluate_public.py\n-r--r--r-- 1 root root 8265 Aug 28 19:55 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Aug 28 19:55 requirements.txt\n\n/task/current_model/:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Aug 28 19:55 .\ndr-xr-xr-x 1 root root  4096 Aug 28 19:57 ..\n-r--r--r-- 1 root root 80058 Aug 28 19:55 train_current_model.py\n-r--r--r-- 1 root root 15985 Aug 28 19:55 two_tower_model.py\n\n/task/data/:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Aug 28 19:56 .\ndr-xr-xr-x 1 root root      4096 Aug 28 19:57 ..\n-r--r--r-- 1 root root    349352 Aug 28 19:55 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Aug 28 19:55 target_users.parquet\n-r--r--r-- 1 root root   7736341 Aug 28 19:55 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Aug 28 19:56 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Aug 28 19:55 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Aug 28 19:55 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Aug 28 19:55 train_listens.parquet\n-r--r--r-- 1 root root    155988 Aug 28 19:55 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Aug 28 19:55 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Aug 28 19:55 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Aug 28 19:55 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Aug 28 19:55 val_listens.parquet\n-r--r--r-- 1 root root      7845 Aug 28 19:55 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Aug 28 19:55 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Aug 28 19:55 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Aug 28 19:56 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1",
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          "timestamp": "2026-08-28T20:02:03.351Z",
          "callId": "toolu_01Wxq2LDaMe1aaBokXhHr4hQ",
          "name": "Read",
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            "timestamp": "2026-08-28T20:02:03.743Z",
            "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
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        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:02:03.743Z",
          "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_01Wxq2LDaMe1aaBokXhHr4hQ"
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        {
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          "timestamp": "2026-08-28T20:02:03.747Z",
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            "text": "<total_tokens>14981246 tokens left</total_tokens>"
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    {
      "id": 3,
      "role": "agent",
      "timestamp": "2026-08-28T20:02:08.116Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:02:08.116Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:08.727Z",
          "callId": "toolu_01EpJWJbpb3unTP9HNt3ctej",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/train_current_model.py",
            "offset": 1561,
            "limit": 610
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          "result": {
            "timestamp": "2026-08-28T20:02:08.732Z",
            "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": "2026-08-28T20:02:08.735Z",
          "callId": "toolu_01C45zMSTwhJFRyxurg1bUDu",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/two_tower_model.py"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:08.738Z",
            "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": "2026-08-28T20:02:08.745Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14953930 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 4,
      "role": "agent",
      "timestamp": "2026-08-28T20:02:20.637Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:02:20.637Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:21.845Z",
          "callId": "toolu_01EnriD6p8QqZajRaAomV64z",
          "name": "Bash",
          "input": {
            "command": "nproc; python -c \"import torch;print(torch.__version__)\"; mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && ls /app/solution",
            "description": "Check CPUs and copy model source"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:24.836Z",
            "content": "8\n2.8.0+cpu\ntrain_current_model.py\ntwo_tower_model.py",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:21.864Z",
          "callId": "toolu_01C2E5DiHUNcZUfedmMf1qVh",
          "name": "Read",
          "input": {
            "file_path": "/task/evaluate_public.py"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:24.840Z",
            "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": "2026-08-28T20:02:24.842Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14935433 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 5,
      "role": "agent",
      "timestamp": "2026-08-28T20:02:30.999Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:02:30.999Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:32.425Z",
          "callId": "toolu_019D32fnj32oRwNXQWDpri5j",
          "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 --threads 6 > /app/solution/base_val.log 2>&1 &\necho started; sleep 5; tail -2 /app/solution/base_val.log",
            "description": "Launch baseline validation run"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:37.448Z",
            "content": "started",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:02:33.527Z",
          "callId": "toolu_01KwbM22L3fNgvhRy9epedcz",
          "name": "Bash",
          "input": {
            "command": "cd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --output /tmp/x.parquet --prepare-only --threads 2 > /app/solution/prep_sub.log 2>&1 &\necho started",
            "description": "Prebuild submission-stage cache"
          },
          "result": {
            "timestamp": "2026-08-28T20:02:37.466Z",
            "content": "started",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:02:37.468Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14932114 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 6,
      "role": "agent",
      "timestamp": "2026-08-28T20:03:11.143Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:03:11.143Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:03:11.349Z",
          "callId": "toolu_01PqtPU5CaTkaTjwH4VNCVCR",
          "name": "Bash",
          "input": {
            "command": "sleep 60; tail -3 /app/solution/base_val.log; echo ---; tail -2 /app/solution/prep_sub.log",
            "description": "Check run progress"
          },
          "result": {
            "timestamp": "2026-08-28T20:04:11.388Z",
            "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\n---\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:04:11.403Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14929550 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 7,
      "role": "agent",
      "timestamp": "2026-08-28T20:04:14.755Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:04:14.755Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:04:15.609Z",
          "callId": "toolu_014t5eZGm4piW8Ctz2cFB5WL",
          "name": "Bash",
          "input": {
            "command": "head -20 /app/solution/base_val.log; echo ===; ls -la /app/solution/; echo ===; grep -E \"prepared stage|selected\" /app/solution/prep_sub.log | head",
            "description": "Inspect logs"
          },
          "result": {
            "timestamp": "2026-08-28T20:04:15.676Z",
            "content": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.2s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.7s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.5s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.3s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.8s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.2s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.1s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/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\n===\ntotal 1062544\ndrwxr-xr-x 1 agent agent      4096 Aug 28 20:03 .\ndrwxr-xr-x 1 agent agent      4096 Aug 28 19:57 ..\n-rw-r--r-- 1 agent agent      1236 Aug 28 20:03 base_val.log\n-rw-r--r-- 1 agent agent 552686232 Aug 28 20:03 cache_submission.npz\n-rw-r--r-- 1 agent agent 535225808 Aug 28 20:03 cache_validation.npz\n-rw-r--r-- 1 agent agent      2169 Aug 28 20:03 prep_sub.log\n-r--r--r-- 1 agent agent     80058 Aug 28 20:02 train_current_model.py\n-r--r--r-- 1 agent agent     15985 Aug 28 20:02 two_tower_model.py\n===\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:04:15.681Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14929178 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 8,
      "role": "agent",
      "timestamp": "2026-08-28T20:05:16.231Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:05:16.231Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:05:16.724Z",
          "callId": "toolu_012Aq96T6JzKaAZesai9jr91",
          "name": "Bash",
          "input": {
            "command": "chmod u+w /app/solution/*.py; tail -3 /app/solution/base_val.log",
            "description": "Make files writable, check progress"
          },
          "result": {
            "timestamp": "2026-08-28T20:05:16.749Z",
            "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": "2026-08-28T20:05:16.753Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14923558 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 9,
      "role": "agent",
      "timestamp": "2026-08-28T20:05:20.904Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:05:20.904Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-08-28T20:05:20.990Z",
          "text": "Now let me implement the improved negative sampling + sampled-softmax objective while the baseline trains."
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:05:23.171Z",
          "callId": "toolu_01R7maVtjsU4ScoAEKg4NXdu",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0",
            "new_string": "    objective: str = \"sampled-softmax\"\n    uniform_negatives: int = 8\n    popularity_negatives: int = 24\n    popularity_sampling_power: float = 0.75\n    inbatch_negatives: int = 0\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0"
          },
          "result": {
            "timestamp": "2026-08-28T20:05:23.197Z",
            "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": "2026-08-28T20:05:23.200Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14922938 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 10,
      "role": "agent",
      "timestamp": "2026-08-28T20:05:27.270Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:05:27.270Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:05:53.739Z",
          "callId": "toolu_01Q6VWgwHnnTusktDwXUCAsL",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "def build_known_positive_filter(",
            "new_string": "@dataclass(frozen=True)\nclass NegativeSampler:\n    \"\"\"Explicit mixture proposal with an exactly known per-item density.\"\"\"\n\n    cdf: np.ndarray\n    log_q_popularity: np.ndarray\n    log_q_uniform: float\n    uniform_negatives: int\n    popularity_negatives: int\n\n    @property\n    def total(self) -> int:\n        return self.uniform_negatives + self.popularity_negatives\n\n\ndef 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 is 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        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset + target_start + 1 : offset + target_start + int(count) + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    return counts\n\n\ndef build_negative_sampler(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> NegativeSampler:\n    \"\"\"Build the tempered-popularity half of the negative proposal.\n\n    The production trainer drew 24 of its 32 negatives uniformly from a 30,000\n    track catalog, so almost every negative was a long-tail track that the raw\n    dot product already scores far below the target.  The eight in-batch\n    negatives were the only popular ones, and the clipped power-law propensity\n    correction shrank exactly those contributions.  Here the popular region is\n    sampled on purpose, from ``p_empirical ** power``, and the resulting bias is\n    removed exactly by the log-Q term of the sampled-softmax loss rather than by\n    a heuristic reweighting.\n    \"\"\"\n    counts = selected_target_counts(prepared, target_starts, target_counts)\n    n_items = len(counts)\n    total = float(counts.sum())\n    empirical = (counts + 1.0) / (total + n_items)\n    tempered = np.power(empirical, config.popularity_sampling_power)\n    probabilities = tempered / tempered.sum()\n    cdf = np.cumsum(probabilities)\n    cdf[-1] = 1.0\n    head = float(probabilities[np.argsort(-counts)[:100]].sum())\n    log(\n        f\"popularity proposal: power={config.popularity_sampling_power} \"\n        f\"top100_mass={head:.4f} uniform_top100_mass={100.0 / n_items:.4f} \"\n        f\"min={probabilities.min():.3e} max={probabilities.max():.3e}\"\n    )\n    return NegativeSampler(\n        cdf=cdf,\n        log_q_popularity=np.log(probabilities).astype(np.float32),\n        log_q_uniform=float(-np.log(n_items)),\n        uniform_negatives=config.uniform_negatives,\n        popularity_negatives=config.popularity_negatives,\n    )\n\n\ndef invalid_mixture_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Reject only true false negatives; duplicates stay legal draws.\n\n    Sampling with replacement is what the log-Q estimator assumes, so repeated\n    popular draws are kept instead of being resampled into a different, unknown\n    proposal.\n    \"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    return invalid\n\n\ndef draw_mixture_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    sampler: NegativeSampler,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform plus tempered-popularity negatives with their log density.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform_count = sampler.uniform_negatives\n    popularity_count = sampler.popularity_negatives\n    negatives = np.empty((batch_size, sampler.total), dtype=np.int64)\n    if uniform_count:\n        negatives[:, :uniform_count] = torch.randint(\n            n_items, (batch_size, uniform_count), generator=generator, device=\"cpu\"\n        ).numpy()\n    if popularity_count:\n        draws = torch.rand(\n            (batch_size, popularity_count), generator=generator, device=\"cpu\"\n        ).numpy()\n        negatives[:, uniform_count:] = np.minimum(\n            np.searchsorted(sampler.cdf, draws.astype(np.float64), side=\"right\"),\n            n_items - 1,\n        )\n\n    for _ in range(32):\n        invalid = invalid_mixture_negatives(\n            negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu\n        )\n        if not np.any(invalid):\n            break\n        columns = np.nonzero(invalid)[1]\n        replacements = np.empty(len(columns), dtype=np.int64)\n        from_uniform = columns < uniform_count\n        uniform_needed = int(from_uniform.sum())\n        if uniform_needed:\n            replacements[from_uniform] = torch.randint(\n                n_items, (uniform_needed,), generator=generator, device=\"cpu\"\n            ).numpy()\n        popularity_needed = len(columns) - uniform_needed\n        if popularity_needed:\n            draws = torch.rand(\n                (popularity_needed,), generator=generator, device=\"cpu\"\n            ).numpy()\n            replacements[~from_uniform] = np.minimum(\n                np.searchsorted(sampler.cdf, draws.astype(np.float64), side=\"right\"),\n                n_items - 1,\n            )\n        negatives[invalid] = replacements\n    else:\n        if np.any(\n            invalid_mixture_negatives(\n                negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu\n            )\n        ):\n            raise RuntimeError(\"mixture negative resampling failed\")\n\n    log_q = np.empty(negatives.shape, dtype=np.float32)\n    if uniform_count:\n        log_q[:, :uniform_count] = sampler.log_q_uniform\n    if popularity_count:\n        log_q[:, uniform_count:] = sampler.log_q_popularity[\n            negatives[:, uniform_count:]\n        ]\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(log_q).to(device),\n    )\n\n\ndef sampled_softmax_loss(\n    positive_logits: torch.Tensor,\n    negative_logits: torch.Tensor,\n    log_q: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Log-Q-corrected sampled softmax over the positive and its negatives.\n\n    Subtracting ``log(K * q(i))`` turns the sampled negatives into an unbiased\n    importance-weighted estimate of the full-catalog softmax denominator, which\n    is the quantity the indexed top-K retrieval actually ranks by.  The\n    production pointwise BCE only calibrated each pair independently.\n    \"\"\"\n    sample_count = negative_logits.shape[1]\n    adjusted = negative_logits - log_q - float(np.log(sample_count))\n    logits = torch.cat([positive_logits[:, None], adjusted], dim=1)\n    return -F.log_softmax(logits, dim=1)[:, 0]\n\n\ndef build_known_positive_filter("
          },
          "result": {
            "timestamp": "2026-08-28T20:05:53.758Z",
            "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": "2026-08-28T20:05:53.760Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14919682 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 11,
      "role": "agent",
      "timestamp": "2026-08-28T20:05:57.317Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:05:57.317Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:01.181Z",
          "callId": "toolu_01ChU15ki7tiwrbqtNzk19wy",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":",
            "new_string": "    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n    sampler: NegativeSampler | None = None,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.objective == \"sampled-softmax\":\n        if sampler is None or known_positive_packed is None:\n            raise RuntimeError(\"sampled softmax requires the mixture sampler\")\n        return draw_mixture_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            sampler,\n            generator,\n            device,\n        )\n    if config.negative_filter_scope == \"full-training-history\":"
          },
          "result": {
            "timestamp": "2026-08-28T20:06:01.196Z",
            "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": "2026-08-28T20:06:01.201Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14918960 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 12,
      "role": "agent",
      "timestamp": "2026-08-28T20:06:06.427Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:06.427Z",
          "callId": "toolu_01DTwVMvfvX5bz3qp1ukNjcF",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()",
            "new_string": "        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        if config.objective == \"sampled-softmax\":\n            example_loss = sampled_softmax_loss(\n                positive_logits, negative_logits, negative_weights[begin:end]\n            )\n        else:\n            example_loss = (\n                F.softplus(-positive_logits)\n                + corrected_negative_loss(\n                    negative_logits, negative_weights[begin:end], config\n                )\n            ) / 2.0\n        loss_sum = (selected_weights * example_loss).sum()"
          },
          "result": {
            "timestamp": "2026-08-28T20:06:06.441Z",
            "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": "2026-08-28T20:06:06.445Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14918399 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 13,
      "role": "agent",
      "timestamp": "2026-08-28T20:06:09.114Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:06:09.114Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:09.745Z",
          "callId": "toolu_0173aqzczg74E9JLB9r2MbxB",
          "name": "Bash",
          "input": {
            "command": "cd /app/solution && grep -n \"item_correction\\|negative_count\\|known_positive_packed\" train_current_model.py | sed -n '1,200p'",
            "description": "Locate call sites"
          },
          "result": {
            "timestamp": "2026-08-28T20:06:09.782Z",
            "content": "537:def build_item_correction(\n661:    known_positive_packed: np.ndarray,\n672:    known = known_positive_packed[user_rows[:, None], byte_columns]\n683:    known_positive_packed: np.ndarray,\n711:            negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu\n736:                negatives, targets_cpu, user_rows, known_positive_packed, liked_cpu\n807:    known_positive_packed: np.ndarray,\n813:    known = known_positive_packed[user_rows[:, None], byte_columns]\n828:    known_positive_packed: np.ndarray,\n831:    item_correction: torch.Tensor,\n880:        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n886:            known_positive_packed,\n907:        known_positive_packed,\n923:    known_positive_packed: np.ndarray | None,\n926:    item_correction: torch.Tensor,\n932:        if sampler is None or known_positive_packed is None:\n938:            known_positive_packed,\n945:        if known_positive_packed is None:\n951:            known_positive_packed,\n954:            item_correction,\n965:        item_correction,\n1424:    item_correction: torch.Tensor,\n1425:    known_positive_packed: np.ndarray | None,\n1440:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1511:                known_positive_packed,\n1514:                item_correction,\n1566:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1568:        / max(total_targets * negative_count, 1),\n1585:    item_correction: torch.Tensor,\n1586:    known_positive_packed: np.ndarray | None,\n1667:                known_positive_packed,\n1670:                item_correction,\n1765:    item_correction = build_item_correction(\n1778:    known_positive_packed = (\n1889:                    item_correction,\n1890:                    known_positive_packed,\n1912:            item_correction,\n1913:            known_positive_packed,\n1926:            item_correction,\n1927:            known_positive_packed,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:06:09.785Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14918137 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 14,
      "role": "agent",
      "timestamp": "2026-08-28T20:06:20.537Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:20.537Z",
          "callId": "toolu_01PQPZz5FcPd2fgfQJV9VGJY",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\nimport re\np='train_current_model.py'\ns=open(p).read()\n\n# signatures: add sampler param\ns = s.replace(\"\"\"    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]:\"\"\",\"\"\"    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    sampler: NegativeSampler | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\"\"\",1)\n\ns = s.replace(\"\"\"    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\"\"\",\"\"\"    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    sampler: NegativeSampler | None,\n    device: torch.device,\n) -> dict[str, float]:\"\"\",1)\n\n# both draw_training_negatives call sites inside epoch/validation loops\nold_call = \"\"\"                config,\n                item_correction,\n                generator,\n                device,\n            )\"\"\"\nnew_call = \"\"\"                config,\n                item_correction,\n                generator,\n                device,\n                sampler,\n            )\"\"\"\nassert s.count(old_call)==2, s.count(old_call)\ns = s.replace(old_call, new_call)\n\ns = s.replace(\"\"\"    negative_count = config.uniform_negatives + config.inbatch_negatives\"\"\",\n\"\"\"    negative_count = (\n        sampler.total\n        if config.objective == \"sampled-softmax\" and sampler is not None\n        else config.uniform_negatives + config.inbatch_negatives\n    )\"\"\",1)\n\n# train_model: build sampler\ns = s.replace(\"\"\"    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\"\"\",\"\"\"    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    sampler = (\n        build_negative_sampler(\n            prepared, config, partition.window_starts, training_counts\n        )\n        if config.objective == \"sampled-softmax\"\n        else None\n    )\"\"\",1)\n\n# pass sampler at the three call sites in train_model\ns = s.replace(\"\"\"                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    item_correction,\n                    known_positive_packed,\n                    sampler,\n                    device,\n                )\"\"\",1)\ns = s.replace(\"\"\"            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",\"\"\"            item_correction,\n            known_positive_packed,\n            sampler,\n            generator,\n            device,\n            epoch,\n        )\"\"\",1)\ns = s.replace(\"\"\"            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\"\"\",\"\"\"            item_correction,\n            known_positive_packed,\n            sampler,\n            device,\n        )\n        diagnostics.update(validation)\"\"\",1)\n\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF",
            "description": "Wire sampler through call sites"
          },
          "result": {
            "timestamp": "2026-08-28T20:06:20.663Z",
            "content": "ok",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:06:20.776Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14916239 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 15,
      "role": "agent",
      "timestamp": "2026-08-28T20:06:24.047Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:06:24.047Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:32.600Z",
          "callId": "toolu_013PJVSh8zxVMRcQp9ZnbK2y",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns = s.replace(\"\"\"    parser.add_argument(\n        \"--negative-filter-scope\",\"\"\",\"\"\"    parser.add_argument(\n        \"--objective\",\n        choices=(\"sampled-softmax\", \"weighted-bce\"),\n        default=Config.objective,\n    )\n    parser.add_argument(\n        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n    )\n    parser.add_argument(\n        \"--popularity-negatives\", type=int, default=Config.popularity_negatives\n    )\n    parser.add_argument(\n        \"--popularity-sampling-power\",\n        type=float,\n        default=Config.popularity_sampling_power,\n    )\n    parser.add_argument(\n        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\"\"\",1)\n\ns = s.replace(\"\"\"    config = Config(\n        bptt_steps=args.bptt_steps,\"\"\",\"\"\"    if args.uniform_negatives + args.popularity_negatives < 1:\n        parser.error(\"at least one sampled negative is required\")\n    if not 0.0 <= args.popularity_sampling_power <= 1.0:\n        parser.error(\"popularity sampling power must lie in [0, 1]\")\n    config = Config(\n        objective=args.objective,\n        uniform_negatives=args.uniform_negatives,\n        popularity_negatives=args.popularity_negatives,\n        popularity_sampling_power=args.popularity_sampling_power,\n        inbatch_negatives=args.inbatch_negatives,\n        bptt_steps=args.bptt_steps,\"\"\",1)\n\ns = s.replace('''                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),''','''                    \"negative_sampling\": (\n                        f\"{config.uniform_negatives}-uniform-plus-\"\n                        f\"{config.popularity_negatives}-tempered-popularity\"\n                        if config.objective == \"sampled-softmax\"\n                        else f\"{config.uniform_negatives}-uniform-plus-\"\n                        f\"{config.inbatch_negatives}-propensity-corrected-inbatch\"\n                    ),''',1)\ns = s.replace('''                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",''',\n'''                    \"objective\": (\n                        \"sample-weighted-logQ-corrected-sampled-softmax\"\n                        if config.objective == \"sampled-softmax\"\n                        else \"sample-weighted-BCE-at-every-timestep\"\n                    ),''',1)\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF\npython -c \"import ast;ast.parse(open('/app/solution/train_current_model.py').read())\" && echo parsed; tail -3 /app/solution/base_val.log",
            "description": "Add CLI flags and validate syntax"
          },
          "result": {
            "timestamp": "2026-08-28T20:06:32.840Z",
            "content": "ok\nparsed\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": "2026-08-28T20:06:32.852Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14915017 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 16,
      "role": "agent",
      "timestamp": "2026-08-28T20:06:42.018Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:06:42.018Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:06:43.370Z",
          "callId": "toolu_015AuNfKLRZQobHZTPPtZCnr",
          "name": "Bash",
          "input": {
            "command": "timeout 900 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/smoke.parquet --threads 2 --max-train-targets-per-user 3 --max-validation-targets-per-user 1 > smoke.log 2>&1; echo \"exit=$?\"; tail -25 smoke.log",
            "description": "Smoke-test new objective"
          },
          "result": {
            "timestamp": "2026-08-28T20:08:43.469Z",
            "content": "Command did not complete within its 120s timeout and was moved to the background (ID: bkoonha5v). Output is being written to: /tmp/claude-1000/-app/436e8259-41fe-474b-bfa9-abd21fabfc32/tasks/bkoonha5v.output. You will be notified when it completes. To check interim output, use Read on that file path.",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:08:43.482Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14914177 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 17,
      "role": "agent",
      "timestamp": "2026-08-28T20:08:47.661Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:08:47.661Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:08:48.743Z",
          "callId": "toolu_01LpyUUqM9ccnudjj2wq33Tu",
          "name": "Bash",
          "input": {
            "command": "sleep 45; echo \"--- smoke:\"; tail -6 /app/solution/smoke.log; echo \"--- base:\"; tail -3 /app/solution/base_val.log; ps -o pid,etime,time,args -C python | head",
            "description": "Check both runs"
          },
          "result": {
            "timestamp": "2026-08-28T20:09:33.816Z",
            "content": "--- smoke:\ninbatch correction: min=0.250 mean_on_targets=1.000 max=1.982\npopularity proposal: power=0.75 top100_mass=0.0224 uniform_top100_mass=0.0033 min=2.164e-05 max=6.323e-04\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=9.65260 validation_loss=9.54206 pair_accuracy=0.684 train_targets=27,079 validation_targets=9,053 train_seconds=32.5 validation_seconds=3.1\nrestored best validation-BCE checkpoint: epoch=1 loss=9.542065\n--- base:\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n    PID     ELAPSED     TIME COMMAND\n   8132       02:50 00:05:18 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/smoke.parquet --threads 2 --max-train-targets-per-user 3 --max-validation-targets-per-user 1",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:09:33.818Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14913685 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 18,
      "role": "agent",
      "timestamp": "2026-08-28T20:09:37.148Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:09:37.148Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:09:38.114Z",
          "callId": "toolu_01JXYJWZXAM75joRYLBgNGo7",
          "name": "Bash",
          "input": {
            "command": "grep -E \"epoch=1/1 |runtime_seconds\" /app/solution/base_val.log | head -3; python /task/evaluate_public.py /app/solution/production_validation.parquet",
            "description": "Evaluate baseline validation ranking"
          },
          "result": {
            "timestamp": "2026-08-28T20:09:41.803Z",
            "content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=205.6s\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=221.0 validation_seconds=19.6\n  \"runtime_seconds\": 389.36,\n{\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": "2026-08-28T20:09:41.805Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14912959 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 19,
      "role": "agent",
      "timestamp": "2026-08-28T20:09:53.643Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:09:53.643Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-08-28T20:09:53.643Z",
          "text": "Baseline public NDCG@10 = **0.02128**. Now launching two improved variants in parallel."
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:09:55.806Z",
          "callId": "toolu_0183sS1sLdCPTbVAWiDAVyQH",
          "name": "Bash",
          "input": {
            "command": "pkill -f \"output /app/solution/smoke.parquet\"; sleep 1\nnohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_a.parquet --threads 4 --seed 29 > /app/solution/val_a.log 2>&1 &\nnohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_b.parquet --threads 4 --popularity-sampling-power 1.0 --seed 29 > /app/solution/val_b.log 2>&1 &\necho launched",
            "description": "Launch two improved validation variants"
          },
          "result": {
            "timestamp": "2026-08-28T20:09:55.845Z",
            "content": "Exit code 144",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:09:46.070Z",
          "data": {
            "type": "queued_command",
            "prompt": "<task-notification>\n<task-id>bkoonha5v</task-id>\n<tool-use-id>toolu_015AuNfKLRZQobHZTPPtZCnr</tool-use-id>\n<output-file>/tmp/claude-1000/-app/436e8259-41fe-474b-bfa9-abd21fabfc32/tasks/bkoonha5v.output</output-file>\n<status>completed</status>\n<summary>Background command \"Smoke-test new objective\" completed (exit code 0)</summary>\n</task-notification>",
            "commandMode": "task-notification",
            "timestamp": "2026-08-28T20:09:46.070Z"
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:09:55.847Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911635 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 20,
      "role": "agent",
      "timestamp": "2026-08-28T20:09:58.524Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:09:58.524Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:09:58.808Z",
          "callId": "toolu_01Ch1scSFfQVF2eG1Ws2iZ4E",
          "name": "Bash",
          "input": {
            "command": "ps -o pid,etime,args -C python | head; ls -la /app/solution/val_*.log 2>/dev/null",
            "description": "Check whether variant runs launched"
          },
          "result": {
            "timestamp": "2026-08-28T20:09:58.831Z",
            "content": "Exit code 2\n    PID     ELAPSED COMMAND",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:09:58.832Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911119 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 21,
      "role": "agent",
      "timestamp": "2026-08-28T20:10:02.795Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:10:02.795Z",
          "callId": "toolu_01VnrzAUfCgXxp7a15kghxPg",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_a.parquet --threads 4 --seed 29 > /app/solution/val_a.log 2>&1 &\nsleep 2\ncd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_b.parquet --threads 4 --popularity-sampling-power 1.0 --seed 29 > /app/solution/val_b.log 2>&1 &\nsleep 8; ps -o pid,etime,args -C python | head",
            "description": "Launch two improved validation variants"
          },
          "result": {
            "timestamp": "2026-08-28T20:10:12.840Z",
            "content": "    PID     ELAPSED COMMAND\n   8227       00:09 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_a.parquet --threads 4 --seed 29\n   8239       00:07 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_b.parquet --threads 4 --popularity-sampling-power 1.0 --seed 29",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:10:12.842Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14910745 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 22,
      "role": "agent",
      "timestamp": "2026-08-28T20:10:16.790Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:10:16.790Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:10:17.520Z",
          "callId": "toolu_019npUA8F12xDf7hrax6vsYZ",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py smoke.parquet 2>&1 | tail -5",
            "description": "Score smoke run"
          },
          "result": {
            "timestamp": "2026-08-28T20:10:22.512Z",
            "content": "  \"scores\": {\n    \"quality_ndcg@10\": 0.014107,\n    \"quality_users\": 4263\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:10:22.514Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14910288 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 23,
      "role": "agent",
      "timestamp": "2026-08-28T20:10:25.261Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:10:25.261Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:10:25.825Z",
          "callId": "toolu_01QFeSuTD16wremkt4H9XVnZ",
          "name": "Bash",
          "input": {
            "command": "sleep 240; echo \"== A\"; tail -2 /app/solution/val_a.log; echo \"== B\"; tail -2 /app/solution/val_b.log",
            "description": "Poll variant progress"
          },
          "result": {
            "timestamp": "2026-08-28T20:12:25.852Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:12:25.861Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14910034 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 24,
      "role": "agent",
      "timestamp": "2026-08-28T20:12:28.655Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:12:28.655Z",
          "callId": "toolu_01KznHikkGJs2GEhuyXb1hwB",
          "name": "Bash",
          "input": {
            "command": "sleep 110; echo \"== A\"; tail -2 /app/solution/val_a.log; echo \"== B\"; tail -2 /app/solution/val_b.log",
            "description": "Poll variant progress",
            "timeout": 130000
          },
          "result": {
            "timestamp": "2026-08-28T20:14:18.744Z",
            "content": "== A\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== B\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=9.588987 running_bce=9.376040 elapsed=224.3s",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:14:18.769Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909830 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 25,
      "role": "agent",
      "timestamp": "2026-08-28T20:14:21.906Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:14:21.906Z",
          "callId": "toolu_01EnG1ZaS6sFGntr7SrFbTjz",
          "name": "Bash",
          "input": {
            "command": "sleep 115; echo \"== A\"; tail -2 /app/solution/val_a.log; echo \"== B\"; tail -2 /app/solution/val_b.log",
            "description": "Poll variant progress",
            "timeout": 130000
          },
          "result": {
            "timestamp": "2026-08-28T20:16:16.958Z",
            "content": "== A\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== B\n  \"device\": \"cpu\"\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:16:16.960Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909488 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 26,
      "role": "agent",
      "timestamp": "2026-08-28T20:16:19.556Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:16:19.556Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:16:20.837Z",
          "callId": "toolu_017x3FmEwoko52UFH17nH4Fs",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py val_b.parquet 2>&1 | tail -5; grep \"epoch=1/1 train_loss\" val_b.log; echo ---; ps -o pid,etime,time,args -C python|head -3; tail -1 val_a.log",
            "description": "Evaluate variant B"
          },
          "result": {
            "timestamp": "2026-08-28T20:16:24.291Z",
            "content": "  \"scores\": {\n    \"quality_ndcg@10\": 0.015132,\n    \"quality_users\": 4263\n  }\n}\nepoch=1/1 train_loss=9.36985 validation_loss=9.10201 pair_accuracy=0.622 train_targets=1,052,953 validation_targets=130,103 train_seconds=236.5 validation_seconds=15.0\n---\n    PID     ELAPSED     TIME COMMAND\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:16:24.293Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909140 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 27,
      "role": "agent",
      "timestamp": "2026-08-28T20:16:27.810Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:16:27.810Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:16:28.114Z",
          "callId": "toolu_0149W3DG1ju8JACKeBDbivrQ",
          "name": "Bash",
          "input": {
            "command": "ls -la val_a.parquet 2>&1; wc -l val_a.log; tail -20 val_a.log",
            "description": "Inspect variant A"
          },
          "result": {
            "timestamp": "2026-08-28T20:16:28.130Z",
            "content": "ls: cannot access 'val_a.parquet': No such file or directory\n6 val_a.log\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\npopularity proposal: power=0.75 top100_mass=0.0374 uniform_top100_mass=0.0033 min=2.565e-06 max=1.024e-03\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": "2026-08-28T20:16:28.132Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14908707 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 28,
      "role": "agent",
      "timestamp": "2026-08-28T20:17:20.328Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:17:20.328Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-08-28T20:17:21.027Z",
          "text": "Variant B (α=1.0) scored 0.0151 — below baseline. Variant A was OOM-killed. Launching a corrected run at full threads while I check a popularity reference."
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:17:22.888Z",
          "callId": "toolu_01SQJu395ZUYXHnqJwj4u2Px",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_c.parquet --threads 8 --learning-rate 3e-3 --seed 29 > /app/solution/val_c.log 2>&1 &\nsleep 3; free -g | head -2; ps -o pid,etime,args -C python|head -3",
            "description": "Launch sampled-softmax with higher LR"
          },
          "result": {
            "timestamp": "2026-08-28T20:17:25.922Z",
            "content": "               total        used        free      shared  buff/cache   available\nMem:              15           1           8           0           6          14\n    PID     ELAPSED COMMAND\n   8429       00:03 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_c.parquet --threads 8 --learning-rate 3e-3 --seed 29",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:17:25.924Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14904517 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 29,
      "role": "agent",
      "timestamp": "2026-08-28T20:17:32.145Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:17:32.145Z",
          "callId": "toolu_01XtaiwatL96jSPmJQ9qRpMg",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\nimport numpy as np, pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')\nitems = cat.item_id.to_numpy(np.int64)\nz = np.load('/task/data/validation_eligible_candidates.npz')\nuids, iid, packed = z['uids'], z['item_ids'], z['packed_eligible']\nassert (iid==items).all()\nscore = -np.arange(len(items), dtype=np.float64)  # popularity order\nrows=[]\nfor r,u in enumerate(uids):\n    m = np.unpackbits(packed[r], count=len(items), bitorder='little').astype(bool)\n    idx = np.flatnonzero(m)[:100]\n    rows.append(pd.DataFrame({'uid':int(u),'item_id':items[idx],'rank':np.arange(1,len(idx)+1)}))\npd.concat(rows).to_parquet('/app/solution/pop_val.parquet', index=False)\nprint('done', len(rows))\nEOF\npython /task/evaluate_public.py /app/solution/pop_val.parquet 2>&1 | tail -4",
            "description": "Score a pure-popularity reference ranking"
          },
          "result": {
            "timestamp": "2026-08-28T20:17:40.124Z",
            "content": "done 8456\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:17:40.131Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14903868 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 30,
      "role": "agent",
      "timestamp": "2026-08-28T20:17:56.834Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:17:56.834Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-08-28T20:17:57.540Z",
          "text": "Popularity-only reference = 0.0107; baseline 0.0213. Launching a hedge submission with the baseline config while the experiment finishes."
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:17:59.558Z",
          "callId": "toolu_01DE5u1MqCyPPS61job2HqEC",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --output /app/solution/submission_baseline.parquet --threads 4 --objective weighted-bce --uniform-negatives 24 --popularity-negatives 0 --inbatch-negatives 8 --learning-rate 1e-3 > /app/solution/sub_baseline.log 2>&1 &\nsleep 3; ps -o pid,etime,args -C python | head -4; tail -1 val_c.log",
            "description": "Launch fallback baseline submission refit"
          },
          "result": {
            "timestamp": "2026-08-28T20:18:02.609Z",
            "content": "    PID     ELAPSED COMMAND\n   8429       00:39 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --output /app/solution/val_c.parquet --threads 8 --learning-rate 3e-3 --seed 29\n   8502       00:03 python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --output /app/solution/submission_baseline.parquet --threads 4 --objective weighted-bce --uniform-negatives 24 --popularity-negatives 0 --inbatch-negatives 8 --learning-rate 1e-3\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:18:02.614Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14902411 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 31,
      "role": "agent",
      "timestamp": "2026-08-28T20:18:05.160Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:18:05.160Z",
          "callId": "toolu_01Ui3AjhFFRNcsfg7GDz6mSZ",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -2 val_c.log; echo ===; tail -1 sub_baseline.log",
            "description": "Poll runs",
            "timeout": 130000
          },
          "result": {
            "timestamp": "2026-08-28T20:20:00.215Z",
            "content": "target 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===\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:20:00.216Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14901982 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 32,
      "role": "agent",
      "timestamp": "2026-08-28T20:20:02.653Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:20:02.653Z",
          "callId": "toolu_01LAnULTShWJrafD9L2yA2nq",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -2 val_c.log; echo ===; tail -1 sub_baseline.log; free -g|head -2",
            "description": "Poll runs",
            "timeout": 130000
          },
          "result": {
            "timestamp": "2026-08-28T20:21:57.731Z",
            "content": "target 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===\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n               total        used        free      shared  buff/cache   available\nMem:              15           7           0           0           6           7",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:21:57.736Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14901707 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 33,
      "role": "agent",
      "timestamp": "2026-08-28T20:22:04.127Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:22:04.127Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:22:11.168Z",
          "callId": "toolu_01HLyfaqAe4d3T9E9mSLUxa9",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/check_submission.py",
            "content": "#!/usr/bin/env python3\n\"\"\"Validate a hidden-test ranking against the deliverable invariants.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\nDATA = Path(\"/task/data\")\n\n\ndef main(path: str) -> int:\n    frame = pd.read_parquet(path)\n    targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n    with np.load(DATA / \"test_eligible_candidates.npz\") as artifact:\n        uids = artifact[\"uids\"].astype(np.int64)\n        item_ids = artifact[\"item_ids\"].astype(np.int64)\n        packed = artifact[\"packed_eligible\"]\n    rows = {int(uid): row for row, uid in enumerate(uids)}\n    column = pd.Index(item_ids)\n\n    assert {\"uid\", \"item_id\", \"rank\"} <= set(frame.columns), frame.columns\n    for name in (\"uid\", \"item_id\", \"rank\"):\n        values = frame[name].to_numpy()\n        assert np.isfinite(values).all(), name\n        assert np.array_equal(values, values.astype(np.int64)), name\n    assert set(frame.uid.astype(int)) == targets, \"target user set differs\"\n    expected = np.arange(1, 101)\n    for uid, group in frame.groupby(\"uid\", sort=False):\n        assert len(group) == 100, uid\n        assert group.item_id.nunique() == 100, uid\n        assert np.array_equal(np.sort(group[\"rank\"].to_numpy()), expected), uid\n        mask = np.unpackbits(\n            packed[rows[int(uid)]], count=len(item_ids), bitorder=\"little\"\n        ).astype(bool)\n        columns = column.get_indexer(group.item_id.to_numpy(np.int64))\n        assert (columns >= 0).all() and mask[columns].all(), uid\n    print(f\"OK {path}: users={frame.uid.nunique():,} rows={len(frame):,}\")\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main(sys.argv[1]))\n"
          },
          "result": {
            "timestamp": "2026-08-28T20:22:11.212Z",
            "content": "File created successfully at: /app/solution/check_submission.py (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:22:11.215Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14900435 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 34,
      "role": "agent",
      "timestamp": "2026-08-28T20:22:14.168Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:22:14.168Z",
          "callId": "toolu_01MUEdujtw6zcjRZUsJa5sxo",
          "name": "Bash",
          "input": {
            "command": "sleep 90; tail -2 val_c.log; echo ===; tail -1 sub_baseline.log; ls -la val_c.parquet submission_baseline.parquet 2>&1|tail -3",
            "description": "Poll runs",
            "timeout": 120000
          },
          "result": {
            "timestamp": "2026-08-28T20:23:44.212Z",
            "content": "target 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===\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nls: cannot access 'val_c.parquet': No such file or directory\nls: cannot access 'submission_baseline.parquet': No such file or directory",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:23:44.220Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14900208 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 35,
      "role": "agent",
      "timestamp": "2026-08-28T20:23:54.045Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:23:54.045Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:23:54.551Z",
          "callId": "toolu_01DRqzpaevMdtE57DEnSmtUv",
          "name": "Bash",
          "input": {
            "command": "kill 8429 2>/dev/null; sleep 2; ps -o pid,etime,args -C python|head -3; free -g|head -2",
            "description": "Free resources for the submission run"
          },
          "result": {
            "timestamp": "2026-08-28T20:23:56.584Z",
            "content": "    PID     ELAPSED COMMAND\n   8502       05:57 python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_submission.npz --output /app/solution/submission_baseline.parquet --threads 4 --objective weighted-bce --uniform-negatives 24 --popularity-negatives 0 --inbatch-negatives 8 --learning-rate 1e-3\n               total        used        free      shared  buff/cache   available\nMem:              15           5           4           0           6          10",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:23:56.586Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14899341 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 36,
      "role": "agent",
      "timestamp": "2026-08-28T20:24:02.797Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:24:02.797Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:24:05.555Z",
          "callId": "toolu_01C8yJGEzaA3uaba8dH293kG",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace('''    objective: str = \"sampled-softmax\"\n    uniform_negatives: int = 8\n    popularity_negatives: int = 24\n    popularity_sampling_power: float = 0.75\n    inbatch_negatives: int = 0''','''    objective: str = \"weighted-bce\"\n    uniform_negatives: int = 24\n    popularity_negatives: int = 0\n    popularity_sampling_power: float = 0.75\n    inbatch_negatives: int = 8''',1)\nopen(p,'w').write(s)\nEOF\npython -c \"import ast;ast.parse(open('train_current_model.py').read())\" && echo ok; tail -1 sub_baseline.log",
            "description": "Restore shipped defaults"
          },
          "result": {
            "timestamp": "2026-08-28T20:24:05.726Z",
            "content": "ok\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:24:05.727Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14898464 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 37,
      "role": "agent",
      "timestamp": "2026-08-28T20:24:08.161Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:24:08.161Z",
          "callId": "toolu_015xWpbsS57re9vA9zSMhEdm",
          "name": "Bash",
          "input": {
            "command": "sleep 105; tail -2 sub_baseline.log; ls -la submission_baseline.parquet 2>&1|tail -1",
            "description": "Poll submission run",
            "timeout": 120000
          },
          "result": {
            "timestamp": "2026-08-28T20:25:53.188Z",
            "content": "epoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=430.6 validation_seconds=17.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nls: cannot access 'submission_baseline.parquet': No such file or directory",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:25:53.190Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14898262 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 38,
      "role": "agent",
      "timestamp": "2026-08-28T20:25:54.901Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:25:54.901Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:25:55.958Z",
          "callId": "toolu_01Pm7QUrJy22sxShX3rWT7Dx",
          "name": "Bash",
          "input": {
            "command": "sleep 75; tail -2 sub_baseline.log; ls -la submission_baseline.parquet 2>&1|tail -1",
            "description": "Poll ranking",
            "timeout": 100000
          },
          "result": {
            "timestamp": "2026-08-28T20:27:10.983Z",
            "content": "  \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 1212472 Aug 28 20:26 submission_baseline.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:27:10.984Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14897932 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 39,
      "role": "agent",
      "timestamp": "2026-08-28T20:27:12.968Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:27:12.968Z",
          "callId": "toolu_01VX7rwbnbJcQKrC7gmu3mvP",
          "name": "Bash",
          "input": {
            "command": "cp submission_baseline.parquet submission.parquet && python check_submission.py submission.parquet",
            "description": "Install and validate submission"
          },
          "result": {
            "timestamp": "2026-08-28T20:27:16.361Z",
            "content": "OK submission.parquet: users=8,456 rows=845,600",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:27:16.363Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14897745 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 40,
      "role": "agent",
      "timestamp": "2026-08-28T20:27:20.337Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-08-28T20:27:20.337Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:27:47.214Z",
          "callId": "toolu_01AvtrcAK8hcMzUkDnmhULWJ",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/README.md",
            "content": "# Made-for-You two-tower: negative-sampling investigation\n\n## Diagnosis\n\nThe production objective is a sample-weighted pointwise BCE with 32 negatives\nper target: 24 drawn uniformly from the 30,000-track catalog and 8 in-batch\nnegatives whose contribution is rescaled by a clipped power-law propensity\ncorrection (`(1/(N q))**0.5`, clipped to `[0.25, 4]`).\n\nTwo limitations follow:\n\n1. **The negatives are almost all trivial.** A uniform draw from a catalog\n   ordered by popularity is a long-tail track that the raw dot product already\n   scores far below the target, so 24 of 32 negatives contribute nearly no\n   gradient. The top of the ranked list is contested by *popular* candidates,\n   which the loss almost never sees.\n2. **The only popular negatives are the ones the trainer discounts.** The\n   in-batch block is the sole source of head-of-catalog negatives, and the\n   propensity correction shrinks exactly those terms toward the 0.25 floor.\n   The in-batch proposal is also batch-dependent, so its density is never\n   actually known — the correction is a heuristic stand-in for it.\n\nReference points on the public backtest (all with the supplied stage cutoff\n`24786800`):\n\n| ranking | public NDCG@10 |\n| --- | --- |\n| catalog popularity order only | 0.010710 |\n| **production model (baseline)** | **0.021280** |\n\nSo the production model carries roughly 2x the popularity prior's quality; the\nremaining headroom is within-head discrimination, which is the region its\nnegatives do not cover.\n\n## Change implemented\n\n`train_current_model.py` gains a second, selectable training objective\n(`--objective sampled-softmax`) that replaces both flawed pieces at once and\nkeeps the per-target negative budget at 32:\n\n* `build_negative_sampler` / `draw_mixture_negatives` — negatives are drawn\n  from an **explicit mixture proposal**: `--uniform-negatives` uniform draws for\n  coverage plus `--popularity-negatives` draws from\n  `p_empirical ** --popularity-sampling-power` over the selected training\n  targets. The popular region is now sampled on purpose and the proposal\n  density is known exactly per item.\n* `sampled_softmax_loss` — a **log-Q-corrected sampled softmax** over the\n  positive and its negatives (`s_neg - log q(i) - log K`). This removes the\n  proposal bias exactly instead of with clipped reweighting, and it optimizes\n  the ordering the indexed top-K retrieval actually uses rather than\n  calibrating each pair independently.\n* False-negative rejection (full training-history positives, the current\n  target, active likes) is preserved; cross-column de-duplication is dropped\n  because the log-Q estimator assumes sampling with replacement.\n\nThe data protocol, chronological target partition, two-tower serving contract\n(no cross-encoder, no user-ID table, precomputable item vectors, raw dot\nproduct), and the evaluation boundary are unchanged.\n\n## Measured result — the change is *not* shipped\n\nUnder the one-epoch CPU budget the new objective did **not** beat production on\nthe public backtest:\n\n| configuration | public NDCG@10 |\n| --- | --- |\n| production BCE, 24 uniform + 8 corrected in-batch (`weighted-bce`) | **0.021280** |\n| sampled softmax, 8 uniform + 24 popularity (power 1.0), lr 1e-3 | 0.015132 |\n| sampled softmax, 8 uniform + 24 popularity (power 0.75), lr 3e-3 | not finished in budget |\n\nThe softmax variant lands between the popularity-only reference (0.0107) and\nproduction (0.0213), and its pairwise accuracy drops from 0.755 to 0.622 — the\nsignature of an under-trained model that has learned mostly the popularity\nprior. The loss scale changes from ~0.54 to ~9.4 while the gradient clip stays\nat 2.0, so one epoch at the production learning rate is very likely not enough\nto converge. The higher-learning-rate run that would test this was still\ntraining when the time budget ran out and was stopped so the submission refit\ncould complete.\n\nBecause the only completed measurement was a regression, the shipped artifact\nuses the production objective, refit on `train_* + val_*`. The `Config`\ndefaults were restored to the production values so the script reproduces the\nsubmitted artifact with no flags. The sampled-softmax path remains available\nbehind `--objective sampled-softmax` and is the recommended next experiment\n(higher learning rate and/or a second epoch, then a sweep over\n`--popularity-sampling-power`).\n\n## Commands\n\nPublic backtest (production objective):\n\n```sh\npython train_current_model.py --stage validation --public-dir /task \\\n  --prepared-cache /app/solution/cache_validation.npz \\\n  --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n```\n\nSubmitted artifact (`submission.parquet`, refit on `train_* + val_*`):\n\n```sh\npython train_current_model.py --stage submission --public-dir /task \\\n  --prepared-cache /app/solution/cache_submission.npz \\\n  --output /app/solution/submission.parquet\npython check_submission.py /app/solution/submission.parquet\n```\n\nCandidate experiment (not shipped):\n\n```sh\npython train_current_model.py --stage validation --public-dir /task \\\n  --prepared-cache /app/solution/cache_validation.npz \\\n  --objective sampled-softmax --uniform-negatives 8 \\\n  --popularity-negatives 24 --popularity-sampling-power 0.75 \\\n  --learning-rate 3e-3 --output /app/solution/val_c.parquet\n```\n\n## Files\n\n* `train_current_model.py` — trainer/ranker used for the artifact.\n* `two_tower_model.py` — unchanged model primitives.\n* `check_submission.py` — deliverable-invariant checker for the ranking.\n* `submission.parquet` — final hidden-test ranking (copy of\n  `submission_baseline.parquet`).\n"
          },
          "result": {
            "timestamp": "2026-08-28T20:27:47.223Z",
            "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": "2026-08-28T20:27:47.228Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895407 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 41,
      "role": "agent",
      "timestamp": "2026-08-28T20:27:50.167Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-08-28T20:27:50.167Z",
          "callId": "toolu_01Vb6RnM6cCQgtXiKva4oqXR",
          "name": "Bash",
          "input": {
            "command": "rm -f smoke.parquet smoke.log /tmp/x.parquet && ls -la *.py *.md *.parquet && python -c \"\nimport pandas as pd;d=pd.read_parquet('submission.parquet');print(d.dtypes.to_dict());print(d.head(3))\"",
            "description": "Final tidy and check"
          },
          "result": {
            "timestamp": "2026-08-28T20:27:51.824Z",
            "content": "-rw-r--r-- 1 agent agent    5663 Aug 28 20:27 README.md\n-rw-r--r-- 1 agent agent    1723 Aug 28 20:22 check_submission.py\n-rw-r--r-- 1 agent agent  728599 Aug 28 20:17 pop_val.parquet\n-rw-r--r-- 1 agent agent 1244229 Aug 28 20:09 production_validation.parquet\n-rw-r--r-- 1 agent agent 1212472 Aug 28 20:27 submission.parquet\n-rw-r--r-- 1 agent agent 1212472 Aug 28 20:26 submission_baseline.parquet\n-rw-r--r-- 1 agent agent   90265 Aug 28 20:24 train_current_model.py\n-rw-r--r-- 1 agent agent   15985 Aug 28 20:02 two_tower_model.py\n-rw-r--r-- 1 agent agent 1208569 Aug 28 20:15 val_b.parquet\n{'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}\n   uid  item_id  rank\n0  100  7958357     1\n1  100  6511826     2\n2  100  8460142     3",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-08-28T20:27:51.826Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895167 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 42,
      "role": "agent",
      "timestamp": "2026-08-28T20:28:04.448Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-08-28T20:28:04.448Z",
          "text": "Time is up. Here's where things landed — honestly reported.\n\n## Deliverables in place\n- `/app/solution/submission.parquet` — regular file, validated against every stated invariant (8,456 users, 100 rows/distinct items each, ranks 1–100, all items test-eligible).\n- `/app/solution/train_current_model.py` + `two_tower_model.py` + `check_submission.py` + `README.md`.\n\n## Diagnosis\nThe production objective spends 24 of its 32 negatives on uniform draws from a 30k catalog ordered by popularity — long-tail tracks the raw dot product already ranks far below the target. The only head-of-catalog negatives are the 8 in-batch ones, and the clipped power-law propensity correction shrinks exactly those toward its 0.25 floor. So the loss almost never trains on the region that actually contests the top-10, and the in-batch proposal density is never truly known.\n\nReference points I measured:\n| ranking | public NDCG@10 |\n| --- | --- |\n| pure popularity order | 0.010710 |\n| production baseline | **0.021280** |\n\n## What I built\nA selectable `--objective sampled-softmax` path: negatives drawn from an explicit mixture (uniform for coverage + `p_empirical**α` for the popular head), with exact log-Q correction inside a sampled softmax, replacing both the easy-negative proposal and the heuristic reweighting. Same 32-negative budget, same data protocol, serving contract, and evaluation boundary.\n\n## What I could not show\nThe one improved configuration that finished regressed: sampled softmax at α=1.0 scored **0.015132**, between popularity-only and production, with pairwise accuracy dropping 0.755 → 0.622 — the signature of under-training (loss scale moves 0.54 → 9.4 while the gradient clip stays at 2.0, at one epoch). The follow-up run at lr 3e-3 that would test that hypothesis was still training when the clock forced me to free CPU for the submission refit, so I killed it. A second variant was OOM-killed earlier when two runs shared the box.\n\n**So the shipped artifact uses the production objective**, refit on `train_* + val_*`, and I restored the `Config` defaults to production values so the script reproduces the artifact with no flags. Shipping the unvalidated softmax path on a single regressing measurement would have been a guess. The improved path stays behind the flag as the documented next experiment."
        }
      ]
    }
  ]
}