{
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  "model": "claude-opus-5",
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          "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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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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          "text": "I'll start by exploring the provided code and data."
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          "callId": "toolu_019rJUG7esPpzDmDAXri3ptP",
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            "command": "ls -la /task /task/current_model /task/data; cat /task/requirements.txt; nproc; free -g",
            "description": "List task files"
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            "timestamp": "2026-09-24T17:36:42.172Z",
            "content": "/task:\ntotal 40\ndr-xr-xr-x 1 root root 4096 Sep 24 17:32 .\ndrwxr-xr-x 1 root root 4096 Sep 24 17:35 ..\n-r--r--r-- 1 root root    0 Sep 24 17:30 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 17:30 current_model\ndr-xr-xr-x 1 root root 4096 Sep 24 17:30 data\n-r--r--r-- 1 root root 5774 Sep 24 17:30 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 17:30 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 17:30 requirements.txt\n\n/task/current_model:\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 24 17:30 .\ndr-xr-xr-x 1 root root  4096 Sep 24 17:32 ..\n-r--r--r-- 1 root root 80058 Sep 24 17:30 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 24 17:30 two_tower_model.py\n\n/task/data:\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 24 17:30 .\ndr-xr-xr-x 1 root root      4096 Sep 24 17:32 ..\n-r--r--r-- 1 root root    349352 Sep 24 17:30 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 24 17:30 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 24 17:30 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 24 17:30 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 24 17:30 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 24 17:30 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 24 17:30 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 24 17:30 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 24 17:30 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 24 17:30 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 24 17:30 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 24 17:30 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 24 17:30 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 24 17:30 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 24 17:30 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 24 17:30 validation_preference_state.parquet\n# 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\n8\n               total        used        free      shared  buff/cache   available\nMem:              15           1           0           0          13          14\nSwap:              0           0           0",
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            "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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          "timestamp": "2026-09-24T17:36:42.243Z",
          "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_01XRGmED73DpaSCwxHpELp97"
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          "timestamp": "2026-09-24T17:36:42.247Z",
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    {
      "id": 3,
      "role": "agent",
      "timestamp": "2026-09-24T17:36:57.567Z",
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          "timestamp": "2026-09-24T17:36:57.567Z",
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        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:58.395Z",
          "callId": "toolu_01HdxY6tKBreFJcYb83ehfJ6",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/train_current_model.py",
            "offset": 1561,
            "limit": 610
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          "result": {
            "timestamp": "2026-09-24T17:36:58.401Z",
            "content": "1561\t        prepared,\n1562\t        partition.history_training_counts,\n1563\t        config,\n1564\t    )\n1565\t    known_positive_packed = (\n1566\t        build_known_positive_filter(prepared, partition.history_training_counts)\n1567\t        if config.negative_filter_scope == \"full-training-history\"\n1568\t        else None\n1569\t    )\n1570\t    history: list[dict[str, float]] = []\n1571\t    best_loss = float(\"inf\")\n1572\t    best_epoch = 0\n1573\t    best_state: dict[str, torch.Tensor] | None = None\n1574\t    epochs_without_improvement = 0\n1575\t    start_epoch = 0\n1576\t\n1577\t    if resume_checkpoint is not None:\n1578\t        payload = torch.load(\n1579\t            resume_checkpoint, map_location=device, weights_only=False\n1580\t        )\n1581\t        stored_config = payload.get(\"config\", {})\n1582\t        architecture_fields = (\n1583\t            \"bptt_steps\",\n1584\t            \"embedding_dim\",\n1585\t            \"hidden_dim\",\n1586\t            \"user_hidden_1\",\n1587\t            \"user_hidden_2\",\n1588\t            \"item_hidden_1\",\n1589\t            \"item_hidden_2\",\n1590\t        )\n1591\t        mismatches = {\n1592\t            name: (stored_config.get(name), getattr(config, name))\n1593\t            for name in architecture_fields\n1594\t            if stored_config.get(name) != getattr(config, name)\n1595\t        }\n1596\t        if mismatches:\n1597\t            raise RuntimeError(\n1598\t                f\"resume checkpoint architecture differs: {mismatches}\"\n1599\t            )\n1600\t        stored_filter_scope = stored_config.get(\n1601\t            \"negative_filter_scope\", \"last-100\"\n1602\t        )\n1603\t        stored_target_weighting = stored_config.get(\n1604\t            \"target_weighting\", \"uniform\"\n1605\t        )\n1606\t        stored_weight_values = (\n1607\t            stored_config.get(\"ordinary_target_weight\", 1.0),\n1608\t            stored_config.get(\"organic_strong_target_weight\", 1.0),\n1609\t            stored_config.get(\"organic_like_target_weight\", 3.0),\n1610\t            stored_config.get(\"discovery_horizon_days\", 7),\n1611\t        )\n1612\t        current_weight_values = (\n1613\t            config.ordinary_target_weight,\n1614\t            config.organic_strong_target_weight,\n1615\t            config.organic_like_target_weight,\n1616\t            config.discovery_horizon_days,\n1617\t        )\n1618\t        stored_window_values = (\n1619\t            stored_config.get(\"validation_fraction\", 0.10),\n1620\t            stored_config.get(\"max_train_targets_per_user\", 0),\n1621\t            stored_config.get(\"max_validation_targets_per_user\", 0),\n1622\t        )\n1623\t        current_window_values = (\n1624\t            config.validation_fraction,\n1625\t            config.max_train_targets_per_user,\n1626\t            config.max_validation_targets_per_user,\n1627\t        )\n1628\t        objective_changed = (\n1629\t            stored_filter_scope != config.negative_filter_scope\n1630\t            or stored_target_weighting != config.target_weighting\n1631\t            or stored_weight_values != current_weight_values\n1632\t            or stored_window_values != current_window_values\n1633\t        )\n1634\t        model.load_state_dict(payload[\"model_state\"])\n1635\t        if \"optimizer_state\" in payload and not objective_changed:\n1636\t            optimizer.load_state_dict(payload[\"optimizer_state\"])\n1637\t            for group in optimizer.param_groups:\n1638\t                group[\"lr\"] = config.learning_rate\n1639\t                group[\"weight_decay\"] = config.weight_decay\n1640\t            log(f\"restored optimizer state from {resume_checkpoint}\")\n1641\t        else:\n1642\t            log(\n1643\t                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n1644\t            )\n1645\t        if \"generator_state\" in payload and not objective_changed:\n1646\t            generator.set_state(payload[\"generator_state\"])\n1647\t        start_epoch = int(payload[\"epoch\"])\n1648\t        if start_epoch >= config.epochs:\n1649\t            raise RuntimeError(\n1650\t                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n1651\t            )\n1652\t        prior = {\n1653\t            str(name): float(value)\n1654\t            for name, value in payload.get(\"diagnostics\", {}).items()\n1655\t        }\n1656\t        if objective_changed:\n1657\t            prior.pop(\"validation_loss\", None)\n1658\t            log(\n1659\t                \"BCE objective changed: filter \"\n1660\t                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1661\t                \"target weighting \"\n1662\t                f\"{stored_target_weighting}->{config.target_weighting}, \"\n1663\t                f\"target window {stored_window_values}->{current_window_values}; \"\n1664\t                \"recomputing the resume checkpoint's validation BCE\"\n1665\t            )\n1666\t        if \"validation_loss\" not in prior:\n1667\t            prior.update(\n1668\t                evaluate_validation_bce(\n1669\t                    model,\n1670\t                    prepared,\n1671\t                    config,\n1672\t                    target_weights,\n1673\t                    partition.window_starts,\n1674\t                    training_counts,\n1675\t                    validation_counts,\n1676\t                    item_correction,\n1677\t                    known_positive_packed,\n1678\t                    device,\n1679\t                )\n1680\t            )\n1681\t        history.append(prior)\n1682\t        best_loss = float(prior[\"validation_loss\"])\n1683\t        best_epoch = start_epoch\n1684\t        best_state = snapshot_model(model)\n1685\t        log(\n1686\t            f\"resumed validation-BCE training: epoch={start_epoch} \"\n1687\t            f\"loss={best_loss:.6f}\"\n1688\t        )\n1689\t\n1690\t    for epoch in range(start_epoch + 1, config.epochs + 1):\n1691\t        diagnostics = run_training_epoch(\n1692\t            model,\n1693\t            optimizer,\n1694\t            prepared,\n1695\t            config,\n1696\t            target_weights,\n1697\t            partition.window_starts,\n1698\t            training_counts,\n1699\t            item_correction,\n1700\t            known_positive_packed,\n1701\t            generator,\n1702\t            device,\n1703\t            epoch,\n1704\t        )\n1705\t        validation = evaluate_validation_bce(\n1706\t            model,\n1707\t            prepared,\n1708\t            config,\n1709\t            target_weights,\n1710\t            partition.window_starts,\n1711\t            training_counts,\n1712\t            validation_counts,\n1713\t            item_correction,\n1714\t            known_positive_packed,\n1715\t            device,\n1716\t        )\n1717\t        diagnostics.update(validation)\n1718\t        history.append(diagnostics)\n1719\t        current_state = snapshot_model(model)\n1720\t        if checkpoint_dir is not None:\n1721\t            save_epoch_checkpoint(\n1722\t                checkpoint_dir,\n1723\t                epoch,\n1724\t                current_state,\n1725\t                config,\n1726\t                diagnostics,\n1727\t                optimizer,\n1728\t                generator,\n1729\t            )\n1730\t        improved = (\n1731\t            diagnostics[\"validation_loss\"]\n1732\t            < best_loss - config.early_stopping_min_delta\n1733\t        )\n1734\t        if improved:\n1735\t            best_loss = diagnostics[\"validation_loss\"]\n1736\t            best_epoch = epoch\n1737\t            best_state = current_state\n1738\t            epochs_without_improvement = 0\n1739\t            if checkpoint_dir is not None:\n1740\t                torch.save(\n1741\t                    {\n1742\t                        \"epoch\": epoch,\n1743\t                        \"config\": asdict(config),\n1744\t                        \"diagnostics\": diagnostics,\n1745\t                        \"model_state\": current_state,\n1746\t                        \"optimizer_state\": optimizer.state_dict(),\n1747\t                        \"generator_state\": generator.get_state(),\n1748\t                    },\n1749\t                    checkpoint_dir / \"best.pt\",\n1750\t                )\n1751\t        else:\n1752\t            epochs_without_improvement += 1\n1753\t        log(\n1754\t            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n1755\t            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n1756\t            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n1757\t            f\"train_targets={int(diagnostics['targets']):,} \"\n1758\t            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n1759\t            f\"train_seconds={diagnostics['seconds']:.1f} \"\n1760\t            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n1761\t        )\n1762\t        release_accelerator_cache(device)\n1763\t        if epochs_without_improvement >= config.early_stopping_patience:\n1764\t            log(\n1765\t                f\"early stopping after epoch={epoch}; \"\n1766\t                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n1767\t            )\n1768\t            break\n1769\t\n1770\t    if best_state is None:\n1771\t        raise RuntimeError(\"training did not produce a valid checkpoint\")\n1772\t    model.load_state_dict(best_state)\n1773\t    model.to(device).eval()\n1774\t    log(\n1775\t        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n1776\t        f\"loss={best_loss:.6f}\"\n1777\t    )\n1778\t    return model, history\n1779\t\n1780\t\n1781\t@torch.inference_mode()\n1782\tdef inference_queries(\n1783\t    model: base.SequentialTwoTower,\n1784\t    prepared: SequenceData,\n1785\t    config: Config,\n1786\t    device: torch.device,\n1787\t) -> torch.Tensor:\n1788\t    sequence_rows = prepared.inference_user_rows\n1789\t    lengths = np.where(\n1790\t        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n1791\t    )\n1792\t    ordered = np.argsort(lengths, kind=\"stable\")\n1793\t    result = torch.zeros(\n1794\t        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n1795\t    )\n1796\t    for group_start in range(0, len(ordered), config.user_batch_size):\n1797\t        output_rows = ordered[group_start : group_start + config.user_batch_size]\n1798\t        rows = sequence_rows[output_rows]\n1799\t        group_lengths = lengths[output_rows]\n1800\t        hidden = torch.zeros(\n1801\t            1, len(output_rows), config.embedding_dim, device=device\n1802\t        )\n1803\t        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n1804\t            steps = min(\n1805\t                config.bptt_steps,\n1806\t                int(group_lengths.max(initial=0)) - start,\n1807\t            )\n1808\t            items = np.full(\n1809\t                (len(output_rows), steps),\n1810\t                len(prepared.item_ids),\n1811\t                dtype=np.uint16,\n1812\t            )\n1813\t            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n1814\t            counts = np.zeros(len(output_rows), dtype=np.int32)\n1815\t            for local_row, sequence_row in enumerate(rows):\n1816\t                if sequence_row < 0:\n1817\t                    continue\n1818\t                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n1819\t                if not count:\n1820\t                    continue\n1821\t                offset = int(prepared.offsets[sequence_row])\n1822\t                selected = slice(offset + start, offset + start + count)\n1823\t                items[local_row, :count] = prepared.sequence_items[selected]\n1824\t                features[local_row, :count] = prepared.event_features[selected]\n1825\t                counts[local_row] = count\n1826\t            outputs, _ = event_sequence(\n1827\t                model,\n1828\t                torch.from_numpy(items.astype(np.int64)).to(device),\n1829\t                torch.from_numpy(features.astype(np.float32)).to(device),\n1830\t                hidden,\n1831\t            )\n1832\t            next_rows: list[torch.Tensor] = []\n1833\t            for local_row, count in enumerate(counts):\n1834\t                next_rows.append(\n1835\t                    outputs[local_row, count - 1]\n1836\t                    if count\n1837\t                    else hidden[0, local_row]\n1838\t                )\n1839\t            hidden = torch.stack(next_rows).unsqueeze(0)\n1840\t\n1841\t        liked_items = torch.from_numpy(\n1842\t            prepared.inference_liked_items[output_rows].astype(np.int64)\n1843\t        ).to(device)\n1844\t        disliked_items = torch.from_numpy(\n1845\t            prepared.inference_disliked_items[output_rows].astype(np.int64)\n1846\t        ).to(device)\n1847\t        queries = user_queries(\n1848\t            model,\n1849\t            hidden.squeeze(0),\n1850\t            model._mean_context_channel(liked_items),\n1851\t            model._mean_context_channel(disliked_items),\n1852\t            torch.from_numpy(\n1853\t                prepared.inference_user_features[output_rows].astype(np.float32)\n1854\t            ).to(device),\n1855\t        )\n1856\t        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n1857\t    return result\n1858\t\n1859\t\n1860\t@torch.inference_mode()\n1861\tdef rank_candidates(\n1862\t    model: base.SequentialTwoTower,\n1863\t    prepared: SequenceData,\n1864\t    eligibility: CandidateSet,\n1865\t    config: Config,\n1866\t    device: torch.device,\n1867\t    ranking_batch_size: int,\n1868\t) -> tuple[pd.DataFrame, dict[str, float]]:\n1869\t    model.eval()\n1870\t    queries = inference_queries(model, prepared, config, device)\n1871\t    item_parts: list[torch.Tensor] = []\n1872\t    for start in range(0, len(prepared.item_ids), 2_048):\n1873\t        rows = torch.arange(\n1874\t            start,\n1875\t            min(start + 2_048, len(prepared.item_ids)),\n1876\t            dtype=torch.long,\n1877\t            device=device,\n1878\t        )\n1879\t        item_parts.append(model.encode_items(rows).cpu())\n1880\t    items = torch.cat(item_parts)\n1881\t    rows: list[tuple[int, int, int]] = []\n1882\t    for start in range(0, len(eligibility.uids), ranking_batch_size):\n1883\t        end = min(start + ranking_batch_size, len(eligibility.uids))\n1884\t        scores = queries[start:end] @ items.T\n1885\t        eligible = np.unpackbits(\n1886\t            eligibility.packed[start:end],\n1887\t            axis=1,\n1888\t            count=len(prepared.item_ids),\n1889\t            bitorder=\"little\",\n1890\t        ).astype(bool, copy=False)\n1891\t        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n1892\t        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n1893\t        for local_row, columns in enumerate(selected):\n1894\t            uid = int(eligibility.uids[start + local_row])\n1895\t            rows.extend(\n1896\t                (uid, int(prepared.item_ids[column]), rank)\n1897\t                for rank, column in enumerate(columns, start=1)\n1898\t            )\n1899\t        if end == len(eligibility.uids) or end % 1_024 == 0:\n1900\t            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n1901\t    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n1902\t    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n1903\t    diagnostics = {\n1904\t        \"query_norm_mean\": float(query_norm.mean()),\n1905\t        \"query_norm_std\": float(query_norm.std()),\n1906\t        \"item_norm_mean\": float(item_norm.mean()),\n1907\t        \"item_norm_std\": float(item_norm.std()),\n1908\t    }\n1909\t    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n1910\t\n1911\t\n1912\tdef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1913\t    output.parent.mkdir(parents=True, exist_ok=True)\n1914\t    if output.suffix == \".parquet\":\n1915\t        frame.to_parquet(output, index=False, compression=\"zstd\")\n1916\t    elif output.suffix == \".csv\":\n1917\t        frame.to_csv(output, index=False)\n1918\t    else:\n1919\t        raise ValueError(\"--output must end in .parquet or .csv\")\n1920\t\n1921\t\n1922\tdef main() -> int:\n1923\t    parser = argparse.ArgumentParser(description=__doc__)\n1924\t    parser.add_argument(\n1925\t        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n1926\t    )\n1927\t    parser.add_argument(\"--output\", type=Path, required=True)\n1928\t    parser.add_argument(\"--public-dir\", type=Path, required=True)\n1929\t    parser.add_argument(\"--prepared-cache\", type=Path)\n1930\t    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n1931\t    parser.add_argument(\"--threads\", type=int, default=8)\n1932\t    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n1933\t    parser.add_argument(\n1934\t        \"--user-batch-size\", type=int, default=Config.user_batch_size\n1935\t    )\n1936\t    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n1937\t    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n1938\t    parser.add_argument(\n1939\t        \"--loss-microbatch-size\",\n1940\t        type=int,\n1941\t        default=Config.loss_microbatch_size,\n1942\t    )\n1943\t    parser.add_argument(\n1944\t        \"--negative-filter-scope\",\n1945\t        choices=(\"full-training-history\", \"last-100\"),\n1946\t        default=Config.negative_filter_scope,\n1947\t    )\n1948\t    parser.add_argument(\n1949\t        \"--target-weighting\",\n1950\t        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n1951\t        default=Config.target_weighting,\n1952\t    )\n1953\t    parser.add_argument(\n1954\t        \"--ordinary-target-weight\",\n1955\t        type=float,\n1956\t        default=Config.ordinary_target_weight,\n1957\t    )\n1958\t    parser.add_argument(\n1959\t        \"--organic-strong-target-weight\",\n1960\t        type=float,\n1961\t        default=Config.organic_strong_target_weight,\n1962\t    )\n1963\t    parser.add_argument(\n1964\t        \"--organic-like-target-weight\",\n1965\t        type=float,\n1966\t        default=Config.organic_like_target_weight,\n1967\t    )\n1968\t    parser.add_argument(\n1969\t        \"--discovery-horizon-days\",\n1970\t        type=int,\n1971\t        default=Config.discovery_horizon_days,\n1972\t    )\n1973\t    parser.add_argument(\n1974\t        \"--validation-fraction\",\n1975\t        type=float,\n1976\t        default=Config.validation_fraction,\n1977\t    )\n1978\t    parser.add_argument(\n1979\t        \"--max-train-targets-per-user\",\n1980\t        type=int,\n1981\t        default=Config.max_train_targets_per_user,\n1982\t        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n1983\t    )\n1984\t    parser.add_argument(\n1985\t        \"--max-validation-targets-per-user\",\n1986\t        type=int,\n1987\t        default=Config.max_validation_targets_per_user,\n1988\t        help=\"retain this many final validation targets per user; 0 is uncapped\",\n1989\t    )\n1990\t    parser.add_argument(\n1991\t        \"--early-stopping-patience\",\n1992\t        type=int,\n1993\t        default=Config.early_stopping_patience,\n1994\t    )\n1995\t    parser.add_argument(\n1996\t        \"--early-stopping-min-delta\",\n1997\t        type=float,\n1998\t        default=Config.early_stopping_min_delta,\n1999\t    )\n2000\t    parser.add_argument(\"--checkpoint-dir\", type=Path)\n2001\t    parser.add_argument(\"--resume-checkpoint\", type=Path)\n2002\t    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n2003\t    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n2004\t    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n2005\t    args = parser.parse_args()\n2006\t\n2007\t    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n2008\t        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n2009\t    if (\n2010\t        args.epochs < 1\n2011\t        or args.learning_rate <= 0\n2012\t        or args.loss_microbatch_size < 1\n2013\t        or args.early_stopping_patience < 1\n2014\t        or args.early_stopping_min_delta < 0\n2015\t        or args.ordinary_target_weight <= 0\n2016\t        or args.organic_strong_target_weight <= 0\n2017\t        or args.organic_like_target_weight <= 0\n2018\t        or args.discovery_horizon_days < 1\n2019\t        or args.max_train_targets_per_user < 0\n2020\t        or args.max_validation_targets_per_user < 0\n2021\t    ):\n2022\t        parser.error(\"training counts, learning rate, and patience are invalid\")\n2023\t    if not 0.0 < args.validation_fraction < 1.0:\n2024\t        parser.error(\"validation fraction must be strictly between zero and one\")\n2025\t    public = args.public_dir.resolve()\n2026\t    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n2027\t        parser.error(f\"public task data not found under {public}\")\n2028\t    config = Config(\n2029\t        bptt_steps=args.bptt_steps,\n2030\t        user_batch_size=args.user_batch_size,\n2031\t        epochs=args.epochs,\n2032\t        learning_rate=args.learning_rate,\n2033\t        loss_microbatch_size=args.loss_microbatch_size,\n2034\t        negative_filter_scope=args.negative_filter_scope,\n2035\t        target_weighting=args.target_weighting,\n2036\t        ordinary_target_weight=args.ordinary_target_weight,\n2037\t        organic_strong_target_weight=args.organic_strong_target_weight,\n2038\t        organic_like_target_weight=args.organic_like_target_weight,\n2039\t        discovery_horizon_days=args.discovery_horizon_days,\n2040\t        validation_fraction=args.validation_fraction,\n2041\t        max_train_targets_per_user=args.max_train_targets_per_user,\n2042\t        max_validation_targets_per_user=args.max_validation_targets_per_user,\n2043\t        early_stopping_patience=args.early_stopping_patience,\n2044\t        early_stopping_min_delta=args.early_stopping_min_delta,\n2045\t        seed=args.seed,\n2046\t    )\n2047\t    torch.set_num_threads(args.threads)\n2048\t    torch.set_num_interop_threads(1)\n2049\t    seed_everything(config.seed)\n2050\t    device = torch.device(args.device)\n2051\t    if device.type == \"mps\" and not torch.backends.mps.is_available():\n2052\t        parser.error(\"MPS was requested but is unavailable\")\n2053\t\n2054\t    started = time.monotonic()\n2055\t    prepared, eligibility = load_or_prepare(\n2056\t        public,\n2057\t        args.stage,\n2058\t        config,\n2059\t        args.prepared_cache.resolve() if args.prepared_cache else None,\n2060\t    )\n2061\t    target_partition = chronological_target_partition(\n2062\t        prepared,\n2063\t        config.validation_fraction,\n2064\t        config.max_train_targets_per_user,\n2065\t        config.max_validation_targets_per_user,\n2066\t    )\n2067\t    if args.prepare_only:\n2068\t        print(\n2069\t            json.dumps(\n2070\t                {\n2071\t                    \"stage\": args.stage,\n2072\t                    \"sequence_users\": len(prepared.user_ids),\n2073\t                    \"sequence_tokens\": len(prepared.sequence_items),\n2074\t                    \"next_item_targets\": prepared.target_count,\n2075\t                    \"selected_training_targets\": int(\n2076\t                        target_partition.training_counts.sum()\n2077\t                    ),\n2078\t                    \"selected_validation_targets\": int(\n2079\t                        target_partition.validation_counts.sum()\n2080\t                    ),\n2081\t                    \"discarded_old_targets\": int(\n2082\t                        target_partition.window_starts.sum()\n2083\t                    ),\n2084\t                    \"target_users\": len(eligibility.uids),\n2085\t                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n2086\t                    \"config\": asdict(config),\n2087\t                },\n2088\t                indent=2,\n2089\t            )\n2090\t        )\n2091\t        return 0\n2092\t\n2093\t    model, training_history = train_model(\n2094\t        public,\n2095\t        args.stage,\n2096\t        prepared,\n2097\t        config,\n2098\t        device,\n2099\t        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n2100\t        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n2101\t    )\n2102\t    ranking, embedding_diagnostics = rank_candidates(\n2103\t        model,\n2104\t        prepared,\n2105\t        eligibility,\n2106\t        config,\n2107\t        device,\n2108\t        args.ranking_batch_size,\n2109\t    )\n2110\t    write_ranking(ranking, args.output.resolve())\n2111\t    print(\n2112\t        json.dumps(\n2113\t            {\n2114\t                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n2115\t                \"stage\": args.stage,\n2116\t                \"output\": str(args.output.resolve()),\n2117\t                \"rows\": len(ranking),\n2118\t                \"users\": int(ranking.uid.nunique()),\n2119\t                \"runtime_seconds\": round(time.monotonic() - started, 3),\n2120\t                \"public_inputs_only\": True,\n2121\t                \"architecture\": {\n2122\t                    \"family\": \"pure-windowed-sequence-two-tower\",\n2123\t                    \"sequence_training\": (\n2124\t                        \"every-adjacent-target-within-recent-chronological-window\"\n2125\t                    ),\n2126\t                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n2127\t                    \"bptt_hidden_state\": \"carried-and-detached\",\n2128\t                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n2129\t                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n2130\t                    \"user_id_embedding\": False,\n2131\t                    \"user_mlp\": \"582-512-256-192\",\n2132\t                    \"item_mlp\": \"196-512-256-192\",\n2133\t                    \"normalization\": \"none\",\n2134\t                    \"score\": \"raw-dot-product\",\n2135\t                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n2136\t                    \"negative_sampling\": (\n2137\t                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n2138\t                    ),\n2139\t                    \"false_negative_filter\": config.negative_filter_scope,\n2140\t                    \"target_weighting\": config.target_weighting,\n2141\t                },\n2142\t                \"config\": asdict(config),\n2143\t                \"sequence_tokens\": len(prepared.sequence_items),\n2144\t                \"all_next_item_targets\": prepared.target_count,\n2145\t                \"selected_training_targets\": int(\n2146\t                    target_partition.training_counts.sum()\n2147\t                ),\n2148\t                \"selected_validation_targets\": int(\n2149\t                    target_partition.validation_counts.sum()\n2150\t                ),\n2151\t                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n2152\t                \"training_history\": training_history,\n2153\t                \"embedding_diagnostics\": embedding_diagnostics,\n2154\t                \"torch_version\": torch.__version__,\n2155\t                \"device\": str(device),\n2156\t            },\n2157\t            indent=2,\n2158\t        )\n2159\t    )\n2160\t    return 0\n2161\t\n2162\t\n2163\tif __name__ == \"__main__\":\n2164\t    raise SystemExit(main())\n2165\t",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:36:58.725Z",
          "callId": "toolu_01R9p8skCL19ZvFsEYXg1s3m",
          "name": "Read",
          "input": {
            "file_path": "/task/current_model/two_tower_model.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:36:58.730Z",
            "content": "1\t\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\n2\tfrom __future__ import annotations\n3\t\n4\timport sys\n5\tfrom dataclasses import dataclass\n6\tfrom pathlib import Path\n7\tfrom typing import Iterator\n8\t\n9\timport numpy as np\n10\timport pandas as pd\n11\timport pyarrow.parquet as pq\n12\timport torch\n13\tfrom torch import nn\n14\t\n15\t\n16\tDAY = 86_400\n17\tTRAIN_END = 24_786_800\n18\tVALIDATION_END = 25_393_400\n19\tLISTEN_POSITIVE_THRESHOLD = 50\n20\tPROFILE_LOOKBACK_DAYS = 120\n21\tLIKE_ITEMS = 48\n22\tDISLIKE_ITEMS = 24\n23\tTOP_N = 100\n24\t\n25\t\n26\t@dataclass(frozen=True)\n27\tclass TowerConfig:\n28\t    sequence_length: int = 100\n29\t    embedding_dim: int = 192\n30\t    hidden_dim: int = 192\n31\t    user_hidden_1: int = 512\n32\t    user_hidden_2: int = 256\n33\t    item_hidden_1: int = 512\n34\t    item_hidden_2: int = 256\n35\t    dropout: float = 0.05\n36\t    epochs: int = 1\n37\t    learning_rate: float = 1.0e-3\n38\t    weight_decay: float = 1.0e-5\n39\t    gradient_clip: float = 2.0\n40\t    uniform_negatives: int = 24\n41\t    inbatch_negatives: int = 8\n42\t    inbatch_correction_power: float = 0.5\n43\t    inbatch_correction_min: float = 0.25\n44\t    inbatch_correction_max: float = 4.0\n45\t    seed: int = 29\n46\t\n47\t\n48\t@dataclass(frozen=True)\n49\tclass ListenEvents:\n50\t    timestamps: np.ndarray\n51\t    items: np.ndarray\n52\t    organic: np.ndarray\n53\t    completion_pct: np.ndarray\n54\t\n55\t\n56\t@dataclass(frozen=True)\n57\tclass ExplicitEvents:\n58\t    timestamps: np.ndarray\n59\t    items: np.ndarray\n60\t    channel: np.ndarray\n61\t    active: np.ndarray\n62\t    organic_like: np.ndarray\n63\t\n64\t\n65\tdef log(message: str) -> None:\n66\t    print(message, file=sys.stderr, flush=True)\n67\t\n68\t\n69\tdef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n70\t    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n71\t    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n72\t    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n73\t    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n74\t    features = np.column_stack(\n75\t        [\n76\t            np.log1p(positives),\n77\t            np.log1p(organic),\n78\t            organic / np.maximum(positives, 1.0),\n79\t            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n80\t        ]\n81\t    )\n82\t    means = features.mean(axis=0, keepdims=True)\n83\t    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n84\t    standardized = ((features - means) / scales).astype(np.float32)\n85\t    if not np.isfinite(standardized).all():\n86\t        raise RuntimeError(\"catalog features contain non-finite values\")\n87\t    return standardized\n88\t\n89\t\n90\tdef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n91\t    if len(parts) == 1:\n92\t        return parts[0]\n93\t    timestamps = np.concatenate([part.timestamps for part in parts])\n94\t    items = np.concatenate([part.items for part in parts])\n95\t    organic = np.concatenate([part.organic for part in parts])\n96\t    completion = np.concatenate([part.completion_pct for part in parts])\n97\t    order = np.argsort(timestamps, kind=\"stable\")\n98\t    return ListenEvents(\n99\t        timestamps=timestamps[order],\n100\t        items=items[order],\n101\t        organic=organic[order],\n102\t        completion_pct=completion[order],\n103\t    )\n104\t\n105\t\n106\tdef iter_listen_users(\n107\t    path: Path, item_index: pd.Index\n108\t) -> Iterator[tuple[int, ListenEvents]]:\n109\t    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n110\t    parquet = pq.ParquetFile(path)\n111\t    pending_uid: int | None = None\n112\t    pending: list[ListenEvents] = []\n113\t    previous_uid = -1\n114\t    for batch in parquet.iter_batches(\n115\t        batch_size=750_000,\n116\t        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n117\t    ):\n118\t        frame = batch.to_pandas()\n119\t        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n120\t        keep = mapped >= 0\n121\t        if not np.any(keep):\n122\t            continue\n123\t        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n124\t        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n125\t        items = mapped[keep].astype(np.int32, copy=False)\n126\t        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n127\t        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n128\t        if np.any(uids[1:] < uids[:-1]):\n129\t            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n130\t        boundaries = np.r_[\n131\t            0,\n132\t            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n133\t            len(uids),\n134\t        ]\n135\t        for start, end in zip(boundaries[:-1], boundaries[1:]):\n136\t            uid = int(uids[start])\n137\t            part = ListenEvents(\n138\t                timestamps[start:end].copy(),\n139\t                items[start:end].copy(),\n140\t                organic[start:end].copy(),\n141\t                completion[start:end].copy(),\n142\t            )\n143\t            if pending_uid is None:\n144\t                pending_uid = uid\n145\t            elif uid != pending_uid:\n146\t                if pending_uid < previous_uid:\n147\t                    raise RuntimeError(f\"{path.name} user order regressed\")\n148\t                yield pending_uid, _combine_listen_chunks(pending)\n149\t                previous_uid = pending_uid\n150\t                pending_uid = uid\n151\t                pending = []\n152\t            pending.append(part)\n153\t    if pending_uid is not None:\n154\t        yield pending_uid, _combine_listen_chunks(pending)\n155\t\n156\t\n157\tdef merge_listen_streams(\n158\t    paths: list[Path], item_index: pd.Index\n159\t) -> Iterator[tuple[int, ListenEvents]]:\n160\t    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n161\t    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n162\t    heads: list[tuple[int, ListenEvents] | None] = [\n163\t        next(stream, None) for stream in streams\n164\t    ]\n165\t    while any(head is not None for head in heads):\n166\t        uid = min(head[0] for head in heads if head is not None)\n167\t        parts: list[ListenEvents] = []\n168\t        for position, head in enumerate(heads):\n169\t            if head is not None and head[0] == uid:\n170\t                parts.append(head[1])\n171\t                heads[position] = next(streams[position], None)\n172\t        yield uid, _combine_listen_chunks(parts)\n173\t\n174\t\n175\tdef load_explicit_events(\n176\t    data: Path,\n177\t    prefixes: tuple[str, ...],\n178\t    item_index: pd.Index,\n179\t) -> dict[int, ExplicitEvents]:\n180\t    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n181\t    specifications = (\n182\t        (\"likes\", 0, 1, 0),\n183\t        (\"unlikes\", 0, 0, 1),\n184\t        (\"dislikes\", 1, 1, 0),\n185\t        (\"undislikes\", 1, 0, 1),\n186\t    )\n187\t    parts: list[pd.DataFrame] = []\n188\t    for family, channel, active, tie_priority in specifications:\n189\t        for prefix in prefixes:\n190\t            frame = pd.read_parquet(\n191\t                data / f\"{prefix}_{family}.parquet\",\n192\t                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n193\t            )\n194\t            mapped = item_index.get_indexer(\n195\t                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n196\t            )\n197\t            keep = mapped >= 0\n198\t            if not np.any(keep):\n199\t                continue\n200\t            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n201\t            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n202\t            retained[\"channel\"] = np.int8(channel)\n203\t            retained[\"active\"] = np.int8(active)\n204\t            retained[\"tie_priority\"] = np.int8(tie_priority)\n205\t            retained[\"organic_like\"] = np.int8(\n206\t                family == \"likes\"\n207\t            ) * retained.is_organic.astype(np.int8)\n208\t            parts.append(retained.drop(columns=\"is_organic\"))\n209\t    if not parts:\n210\t        return {}\n211\t    events = pd.concat(parts, ignore_index=True)\n212\t    events = events.sort_values(\n213\t        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n214\t    ).reset_index(drop=True)\n215\t    result: dict[int, ExplicitEvents] = {}\n216\t    for uid, rows in events.groupby(\"uid\", sort=False):\n217\t        result[int(uid)] = ExplicitEvents(\n218\t            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n219\t            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n220\t            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n221\t            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n222\t            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n223\t        )\n224\t    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n225\t    return result\n226\t\n227\t\n228\tdef empty_explicit_events() -> ExplicitEvents:\n229\t    return ExplicitEvents(\n230\t        timestamps=np.empty(0, dtype=np.int64),\n231\t        items=np.empty(0, dtype=np.int32),\n232\t        channel=np.empty(0, dtype=np.int8),\n233\t        active=np.empty(0, dtype=np.int8),\n234\t        organic_like=np.empty(0, dtype=np.int8),\n235\t    )\n236\t\n237\t\n238\tdef pack_active_items(\n239\t    active: dict[int, int], width: int, padding_item: int\n240\t) -> np.ndarray:\n241\t    packed = np.full(width, padding_item, dtype=np.uint16)\n242\t    if not active:\n243\t        return packed\n244\t    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n245\t    packed[: len(ordered)] = np.fromiter(\n246\t        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n247\t    )\n248\t    return packed\n249\t\n250\t\n251\tdef load_inference_state(\n252\t    path: Path,\n253\t    item_index: pd.Index,\n254\t    uids: np.ndarray,\n255\t    n_items: int,\n256\t) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n257\t    \"\"\"Pack the task-provided active explicit state.\"\"\"\n258\t    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n259\t    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n260\t    keep = mapped >= 0\n261\t    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n262\t    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n263\t    user_rows = pd.Index(uids)\n264\t    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n265\t    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n266\t    like_counts = np.zeros(len(uids), dtype=np.int32)\n267\t    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n268\t    for value, width, destination, counts in (\n269\t        (\"liked\", LIKE_ITEMS, liked, like_counts),\n270\t        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n271\t    ):\n272\t        selected = state[state.state == value]\n273\t        for uid, rows in selected.groupby(\"uid\", sort=False):\n274\t            row = int(user_rows.get_indexer([int(uid)])[0])\n275\t            if row < 0:\n276\t                continue\n277\t            items = np.sort(rows.item.unique())\n278\t            counts[row] = len(items)\n279\t            retained = items[:width]\n280\t            destination[row, : len(retained)] = retained.astype(np.uint16)\n281\t    return liked, disliked, like_counts, dislike_counts\n282\t\n283\t\n284\tclass SequentialTwoTower(nn.Module):\n285\t    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n286\t\n287\t    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n288\t        super().__init__()\n289\t        n_items, n_item_features = item_features.shape\n290\t        dim = config.embedding_dim\n291\t        self.n_items = n_items\n292\t        self.context_embedding = nn.Embedding(\n293\t            n_items + 1, dim, padding_idx=n_items\n294\t        )\n295\t        self.event_projection = nn.Sequential(\n296\t            nn.Linear(dim + 3, config.hidden_dim),\n297\t            nn.GELU(),\n298\t        )\n299\t        self.sequence_encoder = nn.GRU(\n300\t            input_size=config.hidden_dim,\n301\t            hidden_size=dim,\n302\t            num_layers=1,\n303\t            batch_first=True,\n304\t        )\n305\t        self.user_tower = nn.Sequential(\n306\t            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n307\t            nn.GELU(),\n308\t            nn.Dropout(config.dropout),\n309\t            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n310\t            nn.GELU(),\n311\t            nn.Dropout(config.dropout),\n312\t            nn.Linear(config.user_hidden_2, dim),\n313\t        )\n314\t        self.candidate_embedding = nn.Embedding(n_items, dim)\n315\t        self.register_buffer(\n316\t            \"item_features\", torch.from_numpy(item_features), persistent=True\n317\t        )\n318\t        self.item_tower = nn.Sequential(\n319\t            nn.Linear(dim + n_item_features, config.item_hidden_1),\n320\t            nn.GELU(),\n321\t            nn.Dropout(config.dropout),\n322\t            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n323\t            nn.GELU(),\n324\t            nn.Dropout(config.dropout),\n325\t            nn.Linear(config.item_hidden_2, dim),\n326\t        )\n327\t        nn.init.normal_(self.context_embedding.weight, std=0.02)\n328\t        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n329\t        with torch.no_grad():\n330\t            self.context_embedding.weight[n_items].zero_()\n331\t\n332\t    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n333\t        mask = items.ne(self.n_items)\n334\t        vectors = self.context_embedding(items)\n335\t        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n336\t            dim=1, keepdim=True\n337\t        ).clamp_min(1)\n338\t\n339\t    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n340\t        inputs = torch.cat(\n341\t            [self.candidate_embedding(items), self.item_features[items]], dim=1\n342\t        )\n343\t        return self.item_tower(inputs)\n344\t\n345\t\n346\tdef invalid_negative_mask(\n347\t    negatives: torch.Tensor,\n348\t    targets: torch.Tensor,\n349\t    context_items: torch.Tensor,\n350\t    liked_items: torch.Tensor,\n351\t) -> torch.Tensor:\n352\t    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n353\t    invalid = negatives.eq(targets[:, None])\n354\t    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n355\t    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n356\t    for column in range(1, negatives.shape[1]):\n357\t        invalid[:, column] |= negatives[:, column, None].eq(\n358\t            negatives[:, :column]\n359\t        ).any(dim=1)\n360\t    return invalid\n361\t\n362\t\n363\tdef draw_filtered_mixed_negatives(\n364\t    targets: torch.Tensor,\n365\t    context_items: torch.Tensor,\n366\t    liked_items: torch.Tensor,\n367\t    n_items: int,\n368\t    uniform_count: int,\n369\t    inbatch_count: int,\n370\t    item_correction: torch.Tensor,\n371\t    generator: torch.Generator,\n372\t    device: torch.device,\n373\t) -> tuple[torch.Tensor, torch.Tensor]:\n374\t    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n375\t    batch_size = len(targets)\n376\t    uniform = torch.randint(\n377\t        n_items,\n378\t        (batch_size, uniform_count),\n379\t        generator=generator,\n380\t        device=\"cpu\",\n381\t    )\n382\t    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n383\t    corrected_count = 0\n384\t    if inbatch_count:\n385\t        unique_targets = torch.unique(targets.detach().cpu())\n386\t        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n387\t        if corrected_count:\n388\t            priorities = torch.rand(\n389\t                (batch_size, len(unique_targets)), generator=generator\n390\t            )\n391\t            priorities.masked_fill_(\n392\t                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n393\t                -1.0,\n394\t            )\n395\t            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n396\t            inbatch = unique_targets[columns]\n397\t        if corrected_count < inbatch_count:\n398\t            filler = torch.randint(\n399\t                n_items,\n400\t                (batch_size, inbatch_count - corrected_count),\n401\t                generator=generator,\n402\t                device=\"cpu\",\n403\t            )\n404\t            inbatch = torch.cat([inbatch, filler], dim=1)\n405\t\n406\t    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n407\t    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n408\t    if corrected_count:\n409\t        correction_cpu[\n410\t            :, uniform_count : uniform_count + corrected_count\n411\t        ] = item_correction[inbatch[:, :corrected_count]]\n412\t    negatives = negatives_cpu.to(device)\n413\t    correction = correction_cpu.to(device)\n414\t    for _ in range(32):\n415\t        invalid = invalid_negative_mask(\n416\t            negatives, targets, context_items, liked_items\n417\t        )\n418\t        if not bool(invalid.any()):\n419\t            return negatives, correction\n420\t        replacements = torch.randint(\n421\t            n_items,\n422\t            (int(invalid.sum().detach().cpu()),),\n423\t            generator=generator,\n424\t            device=\"cpu\",\n425\t        ).to(device)\n426\t        negatives[invalid] = replacements\n427\t        correction[invalid] = 1.0\n428\t    invalid = invalid_negative_mask(\n429\t        negatives, targets, context_items, liked_items\n430\t    )\n431\t    if bool(invalid.any()):\n432\t        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n433\t    return negatives, correction\n434\t",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:36:58.737Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14953077 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 4,
      "role": "agent",
      "timestamp": "2026-09-24T17:37:36.203Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:37:36.203Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:38.147Z",
          "callId": "toolu_01S8FT9jnzEVC1z7J82qPF6V",
          "name": "Bash",
          "input": {
            "command": "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && cd /app/solution && ls && python -c \"\nimport pandas as pd\nt=pd.read_parquet('/task/data/target_users.parquet'); print('target users', len(t), t.columns.tolist())\nc=pd.read_parquet('/task/data/candidate_catalog.parquet'); print(c.columns.tolist()); print(c.head(3))\n\" ",
            "description": "Copy source and inspect data"
          },
          "result": {
            "timestamp": "2026-09-24T17:37:39.791Z",
            "content": "train_current_model.py\ntwo_tower_model.py\ntarget users 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\n['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3",
            "isError": false
          }
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:38.436Z",
          "callId": "toolu_011YZhvU3dbmyc7VHhjKTHcp",
          "name": "Read",
          "input": {
            "file_path": "/task/evaluate_public.py"
          },
          "result": {
            "timestamp": "2026-09-24T17:37:39.796Z",
            "content": "1\t#!/usr/bin/env python3\n2\t\"\"\"Score a validation-stage ranking against public discovery labels.\n3\t\n4\t``build_task.py`` publishes this module with the solver-visible task files. It\n5\tloads target users, validation eligibility, and validation-window events from\n6\tthe adjacent ``data`` directory, then evaluates one ranking supplied on the\n7\tcommand line. Stage-independent mechanics come from the adjacent public\n8\t``ranking_evaluation.py`` module. The builder installs the same checked-in\n9\tmodule in the private judge, so public and private scoring mechanics cannot\n10\tdrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\n11\tan organic listen with at least 80 percent completion; a like takes precedence\n12\twhen both events exist.\n13\t\n14\tBefore scoring, the ranking must contain exactly 100 distinct eligible items\n15\tfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\n16\tcolumns and ranks 1 through 100 exactly once per user. Extra columns are\n17\tignored. Quality is graded NDCG@10, averaged only over users who have at least\n18\tone eligible relevant item in the validation window.\n19\t\n20\tThe evaluator prints a JSON object containing the model path and scores. It\n21\tdoes not apply the task's pass threshold; its purpose is model development and\n22\tselection. The private judge applies the threshold to the final submission.\n23\t\n24\tUsage::\n25\t\n26\t    python evaluate_public.py <validation-ranking.parquet|csv>\n27\t\"\"\"\n28\tfrom __future__ import annotations\n29\t\n30\timport json\n31\timport sys\n32\tfrom pathlib import Path\n33\t\n34\timport pandas as pd\n35\t\n36\tfrom ranking_evaluation import (\n37\t    CandidateSet,\n38\t    RankingValidationError,\n39\t    ranked_items,\n40\t    read_ranking,\n41\t    score_rankings,\n42\t    validate_ranking,\n43\t)\n44\t\n45\t\n46\tHERE = Path(__file__).resolve().parent\n47\tDATA = HERE / \"data\"\n48\tLISTEN_STRONG = 80\n49\t\n50\t\n51\tdef validation_truth(\n52\t    eligibility: CandidateSet, targets: set[int]\n53\t) -> dict[int, dict[int, int]]:\n54\t    \"\"\"Build cutoff-correct graded relevance from public validation events.\n55\t\n56\t    Only target-user events for eligible candidates are retained. Organic\n57\t    listens completed to at least 80 percent receive grade 1, and organic likes\n58\t    receive grade 3. Repeated user-item events are collapsed, and processing\n59\t    likes second ensures that grade 3 wins over grade 1.\n60\t\n61\t    Args:\n62\t        eligibility: Validation-cutoff candidate membership service.\n63\t        targets: Complete set of target user IDs.\n64\t\n65\t    Returns:\n66\t        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n67\t        relevant event are omitted.\n68\t    \"\"\"\n69\t    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n70\t    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n71\t    strong = listens[\n72\t        listens.uid.isin(targets)\n73\t        & (listens.is_organic == 1)\n74\t        & (listens.played_ratio_pct >= LISTEN_STRONG)\n75\t    ][[\"uid\", \"item_id\"]].drop_duplicates()\n76\t    organic_likes = likes[\n77\t        likes.uid.isin(targets) & (likes.is_organic == 1)\n78\t    ][[\"uid\", \"item_id\"]].drop_duplicates()\n79\t\n80\t    graded: dict[int, dict[int, int]] = {}\n81\t    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n82\t        uid, item = int(raw_uid), int(raw_item)\n83\t        if eligibility.is_eligible(uid, item):\n84\t            graded.setdefault(uid, {})[item] = 1\n85\t    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n86\t        uid, item = int(raw_uid), int(raw_item)\n87\t        if eligibility.is_eligible(uid, item):\n88\t            graded.setdefault(uid, {})[item] = 3\n89\t    return graded\n90\t\n91\t\n92\tdef load_submission(\n93\t    path: Path, eligibility: CandidateSet, targets: set[int]\n94\t) -> dict[int, list[int]]:\n95\t    \"\"\"Load and validate a public validation ranking.\n96\t\n97\t    Files ending in ``.parquet`` are read as parquet; every other suffix is\n98\t    read as CSV. The required columns must be integer-valued, cover exactly the\n99\t    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n100\t    duplicate user-item pairs, and contain only cutoff-eligible items.\n101\t\n102\t    Args:\n103\t        path: Ranking file to read.\n104\t        eligibility: Validation-cutoff candidate membership service.\n105\t        targets: Exact set of users the ranking must cover.\n106\t\n107\t    Returns:\n108\t        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n109\t\n110\t    Raises:\n111\t        ValueError: If the ranking violates the required schema or invariants.\n112\t    \"\"\"\n113\t    frame = validate_ranking(read_ranking(path), targets)\n114\t    if not all(\n115\t        eligibility.is_eligible(uid, item)\n116\t        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n117\t    ):\n118\t        raise RankingValidationError(\n119\t            \"submission contains an item rejected by eligibility service\"\n120\t        )\n121\t    return ranked_items(frame)\n122\t\n123\t\n124\tdef score(\n125\t    ranked: dict[int, list[int]],\n126\t    graded: dict[int, dict[int, int]],\n127\t) -> dict[str, float | int]:\n128\t    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n129\t\n130\t    Args:\n131\t        ranked: Ranked item IDs for every target user.\n132\t        graded: Eligible validation relevance keyed by user and item.\n133\t\n134\t    Returns:\n135\t        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n136\t    \"\"\"\n137\t    return score_rankings(ranked, graded)\n138\t\n139\t\n140\tdef main() -> None:\n141\t    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n142\t    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n143\t    targets = set(targets_frame.uid.astype(int))\n144\t    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n145\t    graded = validation_truth(eligibility, targets)\n146\t\n147\t    if len(sys.argv) != 2:\n148\t        raise SystemExit(\n149\t            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n150\t        )\n151\t    path = Path(sys.argv[1])\n152\t    ranked = load_submission(path, eligibility, targets)\n153\t    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n154\t\n155\t\n156\tif __name__ == \"__main__\":\n157\t    main()\n158\t",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:37:39.798Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14932742 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 5,
      "role": "agent",
      "timestamp": "2026-09-24T17:37:45.842Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:37:45.842Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:37:47.955Z",
          "callId": "toolu_019eBwFfG3suhWYfUptJARQE",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --prepare-only > prep_val.log 2>&1 &\nsleep 1\ncd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --prepare-only > prep_sub.log 2>&1 &\nsleep 20; tail -3 /app/solution/prep_val.log /app/solution/prep_sub.log",
            "description": "Start sequence prep caches for both stages"
          },
          "result": {
            "timestamp": "2026-09-24T17:38:08.999Z",
            "content": "Exit code 1\n[1]-  Exit 2                  nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --prepare-only > prep_val.log 2>&1\n[2]+  Exit 2                  cd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --prepare-only > prep_sub.log 2>&1\ntail: option used in invalid context -- 3",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:38:09.001Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14929502 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 6,
      "role": "agent",
      "timestamp": "2026-09-24T17:38:12.087Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:38:12.087Z",
          "callId": "toolu_01PXuYkf4g8LCZE5g2zN4d54",
          "name": "Bash",
          "input": {
            "command": "python train_current_model.py --prepare-only 2>&1 | head -5; ls",
            "description": "Check prep failure reason"
          },
          "result": {
            "timestamp": "2026-09-24T17:38:16.208Z",
            "content": "usage: train_current_model.py [-h] [--stage {validation,submission}] --output\n                              OUTPUT --public-dir PUBLIC_DIR\n                              [--prepared-cache PREPARED_CACHE]\n                              [--device {cpu,mps}] [--threads THREADS]\n                              [--bptt-steps BPTT_STEPS]\nprep_sub.log\nprep_val.log\ntrain_current_model.py\ntwo_tower_model.py",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:38:16.210Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14929187 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 7,
      "role": "agent",
      "timestamp": "2026-09-24T17:38:19.653Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:38:19.653Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:38:21.681Z",
          "callId": "toolu_01VNUmgf3LK65B3kjP6Q5NRf",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --prepare-only --output /app/solution/x.parquet > prep_val.log 2>&1 &\nsleep 2\ncd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --prepare-only --output /app/solution/y.parquet > prep_sub.log 2>&1 &\nsleep 25; cd /app/solution && tail -n3 prep_val.log; echo ---; tail -n3 prep_sub.log",
            "description": "Relaunch prep with required output arg"
          },
          "result": {
            "timestamp": "2026-09-24T17:38:48.725Z",
            "content": "prepared 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=10.0s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.2s\n---\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.9s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=16.4s",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:38:48.728Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14928680 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 8,
      "role": "agent",
      "timestamp": "2026-09-24T17:40:13.968Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:40:13.968Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:40:14.415Z",
          "text": "Data prep is running. Now let me implement the improved objective."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:15.837Z",
          "callId": "toolu_01QiXPFy14W2adnkRuGK12qf",
          "name": "Bash",
          "input": {
            "command": "grep -n \"negatives\\|score_target_microbatches\" train_current_model.py | sed -n '1,200p' | grep -n \"1293\\|1449\" ; tail -n2 prep_val.log; tail -n2 prep_sub.log",
            "description": "Check prep progress"
          },
          "result": {
            "timestamp": "2026-09-24T17:40:15.875Z",
            "content": "51:1293:            negatives, negative_weights = draw_training_negatives(\n54:1449:            negatives, negative_weights = draw_training_negatives(\n  }\n}\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:15.878Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14922341 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 9,
      "role": "agent",
      "timestamp": "2026-09-24T17:40:18.627Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:18.627Z",
          "callId": "toolu_014rsQNDd1aDFm8TPM7c13NT",
          "name": "Bash",
          "input": {
            "command": "grep -A25 '\"stage\"' prep_val.log | head -30; ls -la *.npz",
            "description": "Inspect prep summary"
          },
          "result": {
            "timestamp": "2026-09-24T17:40:18.655Z",
            "content": "  \"stage\": \"validation\",\n  \"sequence_users\": 9135,\n  \"sequence_tokens\": 20056329,\n  \"next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"target_users\": 8456,\n  \"runtime_seconds\": 51.776,\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n-rw-r--r-- 1 agent agent 552686232 Sep 24 17:39 cache_sub.npz\n-rw-r--r-- 1 agent agent 535225808 Sep 24 17:39 cache_val.npz",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:18.657Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14922146 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 10,
      "role": "agent",
      "timestamp": "2026-09-24T17:40:21.802Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:40:21.802Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:40:22.536Z",
          "text": "Caches are ready fast. Launching the baseline \"before\" run now while I write the improvement:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:40:24.022Z",
          "callId": "toolu_0128x9cSZtkTm46pBQAfBNKD",
          "name": "Bash",
          "input": {
            "command": "nohup python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 3 --output /app/solution/production_validation.parquet > base_val.log 2>&1 &\nsleep 30; grep -E \"batches|chronological|correction:\" base_val.log | tail -n 4",
            "description": "Launch baseline validation run"
          },
          "result": {
            "timestamp": "2026-09-24T17:40:54.060Z",
            "content": "chronological 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",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:40:54.062Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14921345 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 11,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:29.011Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:29.011Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:30.789Z",
          "callId": "toolu_014qzE6P3kEV7fXEATL8j5ME",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "    negative_filter_scope: str = \"full-training-history\"",
            "new_string": "    negative_objective: str = \"sampled-softmax\"\n    pool_size: int = 2_048\n    pool_uniform_fraction: float = 0.5\n    pool_popularity_power: float = 0.75\n    negative_filter_scope: str = \"full-training-history\""
          },
          "result": {
            "timestamp": "2026-09-24T17:41:30.803Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:30.806Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14918076 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 12,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:34.342Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:41:34.342Z",
          "text": "Now the pool sampling and softmax objective:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:36.623Z",
          "callId": "toolu_01UCmD3sbDhjYqnECdXvqtpr",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "def build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)",
            "new_string": "def training_target_counts(\n    prepared: SequenceData,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Count how often each catalog item appears as a selected training target.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:36.635Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:36.638Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14917605 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 13,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:41.518Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:41:41.518Z",
          "callId": "toolu_013V6LMP3jnAKdZfzkggMPR7",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())",
            "new_string": "                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    return counts\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = training_target_counts(prepared, target_starts, target_counts)\n    total = float(counts.sum())"
          },
          "result": {
            "timestamp": "2026-09-24T17:41:41.534Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:41:41.537Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14917175 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 14,
      "role": "agent",
      "timestamp": "2026-09-24T17:41:44.488Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:41:44.488Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:13.493Z",
          "callId": "toolu_01WnjKMYR9MDFYQFvPaS2QT8",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "def build_known_positive_filter(",
            "new_string": "def build_pool_distribution(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Build the shared negative-pool sampling distribution.\n\n    The production objective drew every negative uniformly over the catalog, so\n    almost every negative was an unpopular tail track that the item tower\n    separates from a positive after a handful of updates.  A mixture of a\n    uniform floor and a popularity-tempered component instead concentrates the\n    negatives on the plausible, frequently served tracks that actually compete\n    with the positive inside the eligible candidate set.\n    \"\"\"\n    counts = training_target_counts(prepared, target_starts, target_counts)\n    n_items = len(counts)\n    tempered = np.power(counts, config.pool_popularity_power)\n    total = float(tempered.sum())\n    popularity = (\n        tempered / total if total > 0.0 else np.full(n_items, 1.0 / n_items)\n    )\n    uniform = np.full(n_items, 1.0 / n_items)\n    fraction = float(np.clip(config.pool_uniform_fraction, 0.0, 1.0))\n    probabilities = fraction * uniform + (1.0 - fraction) * popularity\n    probabilities = probabilities / probabilities.sum()\n    share = float(np.sort(probabilities)[::-1][:1_000].sum())\n    log(\n        f\"negative pool distribution: uniform_fraction={fraction:.2f} \"\n        f\"power={config.pool_popularity_power:.2f} \"\n        f\"top1000_mass={share:.3f} min={probabilities.min():.3e} \"\n        f\"max={probabilities.max():.3e}\"\n    )\n    return torch.from_numpy(probabilities.astype(np.float64))\n\n\ndef draw_shared_pool(\n    probabilities: torch.Tensor,\n    config: Config,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw one block's shared negative pool and its inclusion corrections.\n\n    ``pool_size`` draws are taken with replacement and deduplicated.  Each\n    surviving item carries ``log`` of its inclusion probability so the sampled\n    negatives form a Horvitz-Thompson estimate of the full-catalog softmax\n    denominator.\n    \"\"\"\n    draws = torch.multinomial(\n        probabilities, config.pool_size, replacement=True, generator=generator\n    )\n    pool = torch.unique(draws)\n    selected = probabilities[pool].clamp(1.0e-12, 1.0 - 1.0e-9)\n    log_absent = config.pool_size * torch.log1p(-selected)\n    log_inclusion = torch.log(-torch.expm1(log_absent)).clamp_min(-20.0)\n    return pool.to(device), log_inclusion.to(torch.float32).to(device)\n\n\ndef pool_false_negative_mask(\n    pool: np.ndarray,\n    pool_columns: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n    n_items: int,\n) -> np.ndarray:\n    \"\"\"Mark pool entries that are known positives for each scored row.\"\"\"\n    byte_columns = (pool >> 3).astype(np.int64)\n    bit_columns = (pool & 7).astype(np.uint8)\n    known = known_positive_packed[user_rows[:, None], byte_columns[None, :]]\n    invalid = ((known >> bit_columns[None, :]) & 1).astype(bool)\n    target_columns = pool_columns[targets]\n    present = np.flatnonzero(target_columns >= 0)\n    if len(present):\n        invalid[present, target_columns[present]] = True\n    liked_columns = pool_columns[np.minimum(liked_items, n_items - 1)]\n    liked_rows, liked_slots = np.nonzero(\n        (liked_items < n_items) & (liked_columns >= 0)\n    )\n    if len(liked_rows):\n        invalid[liked_rows, liked_columns[liked_rows, liked_slots]] = True\n    return invalid\n\n\ndef score_pool_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    pool_items: torch.Tensor,\n    pool_log_inclusion: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one block's sampled-softmax loss against the shared pool.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    n_items = len(prepared.item_ids)\n    pool = pool_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    pool_columns = np.full(n_items, -1, dtype=np.int64)\n    pool_columns[pool] = np.arange(len(pool), dtype=np.int64)\n    targets_cpu = positive_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        unique_positives, inverse = torch.unique(\n            positive_items[begin:end], return_inverse=True\n        )\n        positive_vectors = model.encode_items(unique_positives)[inverse]\n        pool_vectors = model.encode_items(pool_items)\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = (\n            query_vectors @ pool_vectors.T\n        ) - pool_log_inclusion[None, :]\n        invalid = pool_false_negative_mask(\n            pool,\n            pool_columns,\n            targets_cpu[begin:end],\n            user_rows[begin:end],\n            known_positive_packed,\n            liked_items[begin:end],\n            n_items,\n        )\n        negative_logits = negative_logits.masked_fill(\n            torch.from_numpy(invalid).to(device), -1.0e9\n        )\n        logits = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n        row_loss = torch.logsumexp(logits, dim=1) - positive_logits\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (selected_weights * row_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"sampled-softmax training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        with torch.no_grad():\n            raw_negatives = negative_logits + pool_log_inclusion[None, :]\n            valid = torch.from_numpy(~invalid).to(device)\n            statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n            statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n            statistics[\"negative_sum\"] += float(\n                (raw_negatives * valid).sum().cpu() / valid.sum().clamp_min(1).cpu()\n            ) * (end - begin)\n            statistics[\"pair_correct\"] += float(\n                (\n                    (positive_logits.detach()[:, None] > raw_negatives) & valid\n                ).sum().cpu()\n                / valid.sum().clamp_min(1).cpu()\n            ) * (end - begin)\n            statistics[\"weight_sum\"] += float(selected_weights.sum().cpu())\n    return statistics, sequence_gradient\n\n\ndef build_known_positive_filter("
          },
          "result": {
            "timestamp": "2026-09-24T17:42:13.517Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:13.521Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14913580 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 15,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:18.855Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:18.855Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:26.128Z",
          "callId": "toolu_01D4xYihd9VfZ4QeuAVgWZqt",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )",
            "new_string": "            optimizer.zero_grad(set_to_none=True)\n            if config.negative_objective == \"sampled-softmax\":\n                pool_items, pool_log_inclusion = draw_shared_pool(\n                    pool_probabilities, config, generator, device\n                )\n                statistics, sequence_gradient = score_pool_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    pool_items,\n                    pool_log_inclusion,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared.liked_states[flat_states].astype(np.int64),\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=True,\n                )\n            else:\n                negatives, negative_weights = draw_training_negatives(\n                    positive_items,\n                    known_history,\n                    liked_items,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared,\n                    config,\n                    item_correction,\n                    generator,\n                    device,\n                )\n                statistics, sequence_gradient = score_target_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    negatives,\n                    negative_weights,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=True,\n                )"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:26.145Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:26.148Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14912340 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 16,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:35.090Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:35.090Z",
          "callId": "toolu_01QaJY9CQjf4cyBiu8a1rWgo",
          "name": "Edit",
          "input": {
            "replace_all": false,
            "file_path": "/app/solution/train_current_model.py",
            "old_string": "            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )",
            "new_string": "            if config.negative_objective == \"sampled-softmax\":\n                pool_items, pool_log_inclusion = draw_shared_pool(\n                    pool_probabilities, config, generator, device\n                )\n                statistics, _ = score_pool_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    pool_items,\n                    pool_log_inclusion,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared.liked_states[flat_states].astype(np.int64),\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=False,\n                )\n            else:\n                negatives, negative_weights = draw_training_negatives(\n                    positive_items,\n                    known_history,\n                    liked_items,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared,\n                    config,\n                    item_correction,\n                    generator,\n                    device,\n                )\n                statistics, _ = score_target_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    negatives,\n                    negative_weights,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=False,\n                )"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:35.103Z",
            "content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:35.106Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911402 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 17,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:45.717Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:42:45.717Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:46.220Z",
          "callId": "toolu_012j1RUvYYfQeeeD8ForbPuW",
          "name": "Bash",
          "input": {
            "command": "grep -n \"item_correction: torch.Tensor,\\|negative_count = \\|item_correction=\\|item_correction,\" train_current_model.py | head -40",
            "description": "Locate signature sites"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:46.239Z",
            "content": "848:    item_correction: torch.Tensor,\n943:    item_correction: torch.Tensor,\n957:            item_correction,\n968:        item_correction,\n1422:    item_correction: torch.Tensor,\n1438:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1535:                    item_correction,\n1605:    item_correction: torch.Tensor,\n1712:                    item_correction,\n1931:                    item_correction,\n1954:            item_correction,\n1968:            item_correction,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:46.241Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14911159 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 18,
      "role": "agent",
      "timestamp": "2026-09-24T17:42:58.720Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:42:58.720Z",
          "callId": "toolu_013QxuDEMAu5LDVZsKtwc97C",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\nimport re\np='train_current_model.py'\ns=open(p).read()\n\n# 1) run_training_epoch signature (line ~1422) and evaluate_validation_bce (~1605)\ns = s.replace(\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",1)\ns = s.replace(\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",1)\n\n# 2) negative diagnostics divisor\ns = s.replace(\"\"\"    negative_count = config.uniform_negatives + config.inbatch_negatives\"\"\",\n\"\"\"    negative_count = (\n        1\n        if config.negative_objective == \"sampled-softmax\"\n        else config.uniform_negatives + config.inbatch_negatives\n    )\"\"\",1)\n\n# 3) call sites: pass pool_probabilities after item_correction\ns = s.replace(\"\"\"                    training_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    training_counts,\n                    item_correction,\n                    pool_probabilities,\n                    known_positive_packed,\n                    device,\n                )\"\"\")\ns = s.replace(\"\"\"            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",\"\"\"            training_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",1)\ns = s.replace(\"\"\"            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\"\"\",\"\"\"            training_counts,\n            validation_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            device,\n        )\"\"\")\ns = s.replace(\"\"\"                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    pool_probabilities,\n                    known_positive_packed,\n                    device,\n                )\"\"\")\n\n# 4) build pool distribution in train_model\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    pool_probabilities = build_pool_distribution(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\"\"\",1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"pool_probabilities\" train_current_model.py",
            "description": "Wire pool distribution through call sites"
          },
          "result": {
            "timestamp": "2026-09-24T17:42:58.820Z",
            "content": "Traceback (most recent call last):\n  File \"<stdin>\", line 101, in <module>\nPermissionError: [Errno 13] Permission denied: 'train_current_model.py'\n1507:                    pool_probabilities, config, generator, device\n1684:                    pool_probabilities, config, generator, device",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:42:58.821Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909738 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 19,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:02.507Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:43:02.507Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:02.661Z",
          "callId": "toolu_0135jw6fwLzwMFZu9AtA3c83",
          "name": "Bash",
          "input": {
            "command": "chmod u+w train_current_model.py two_tower_model.py && ls -l",
            "description": "Make copies writable"
          },
          "result": {
            "timestamp": "2026-09-24T17:43:02.682Z",
            "content": "total 1062548\n-rw-r--r-- 1 agent agent       411 Sep 24 17:40 base_val.log\n-rw-r--r-- 1 agent agent 552686232 Sep 24 17:39 cache_sub.npz\n-rw-r--r-- 1 agent agent 535225808 Sep 24 17:39 cache_val.npz\n-rw-r--r-- 1 agent agent      2162 Sep 24 17:39 prep_sub.log\n-rw-r--r-- 1 agent agent      2162 Sep 24 17:39 prep_val.log\n-rw-r--r-- 1 agent agent     90865 Sep 24 17:42 train_current_model.py\n-rw-r--r-- 1 agent agent     15985 Sep 24 17:37 two_tower_model.py",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:43:02.683Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14909432 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 20,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:06.263Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:43:06.263Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:14.715Z",
          "callId": "toolu_01RtyHPiSKPPwfNpMxAPrHBx",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\nn=0\ndef rep(a,b,count=-1):\n    global s,n\n    before=s.count(a)\n    assert before>0, a[:60]\n    s=s.replace(a,b) if count<0 else s.replace(a,b,count)\n    n+=before\nrep(\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",1)\nrep(\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",1)\nrep(\"\"\"    negative_count = config.uniform_negatives + config.inbatch_negatives\"\"\",\n\"\"\"    negative_count = (\n        1\n        if config.negative_objective == \"sampled-softmax\"\n        else config.uniform_negatives + config.inbatch_negatives\n    )\"\"\",1)\nrep(\"\"\"                    training_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    training_counts,\n                    item_correction,\n                    pool_probabilities,\n                    known_positive_packed,\n                    device,\n                )\"\"\")\nrep(\"\"\"            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",\"\"\"            training_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",1)\nrep(\"\"\"            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\"\"\",\"\"\"            training_counts,\n            validation_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            device,\n        )\"\"\")\nrep(\"\"\"                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    pool_probabilities,\n                    known_positive_packed,\n                    device,\n                )\"\"\")\nrep(\"\"\"    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    pool_probabilities = build_pool_distribution(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\"\"\",1)\nopen(p,'w').write(s)\nprint('replacements', n)\nEOF\ngrep -n \"pool_probabilities\" train_current_model.py",
            "description": "Apply wiring patch"
          },
          "result": {
            "timestamp": "2026-09-24T17:43:14.761Z",
            "content": "Traceback (most recent call last):\n  File \"<stdin>\", line 32, in <module>\n  File \"<stdin>\", line 7, in rep\nAssertionError:                     training_counts,\n                    ite\n1507:                    pool_probabilities, config, generator, device\n1684:                    pool_probabilities, config, generator, device",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:43:14.762Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14907877 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 21,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:18.370Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:18.370Z",
          "callId": "toolu_01WyG5BLNJr35Q2crUs4WyVC",
          "name": "Bash",
          "input": {
            "command": "sed -n '1700,1730p;1925,1985p' train_current_model.py",
            "description": "Inspect resume-path call sites"
          },
          "result": {
            "timestamp": "2026-09-24T17:43:18.391Z",
            "content": "                    device,\n                    backward=False,\n                )\n            else:\n                negatives, negative_weights = draw_training_negatives(\n                    positive_items,\n                    known_history,\n                    liked_items,\n                    flat_user_rows,\n                    known_positive_packed,\n                    prepared,\n                    config,\n                    item_correction,\n                    generator,\n                    device,\n                )\n                statistics, _ = score_target_microbatches(\n                    model,\n                    prepared,\n                    config,\n                    sequence_vectors,\n                    flat_states,\n                    block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                    positive_items,\n                    negatives,\n                    negative_weights,\n                    block[8].reshape(-1)[flat_scored],\n                    device,\n                    backward=False,\n                )\n            total_loss += statistics[\"loss_sum\"]\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:43:18.393Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14907640 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 22,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:23.565Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:43:23.565Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:30.791Z",
          "callId": "toolu_01XbsPuoJpdDMCiBNhfYNzun",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ndef rep(a,b,count=-1):\n    global s\n    assert s.count(a)>0, a[:60]\n    s=s.replace(a,b) if count<0 else s.replace(a,b,count)\nrep(\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",\"\"\"    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\"\"\",1)\nrep(\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",\"\"\"    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    pool_probabilities: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\"\"\",1)\nrep(\"\"\"    negative_count = config.uniform_negatives + config.inbatch_negatives\"\"\",\n\"\"\"    negative_count = (\n        1\n        if config.negative_objective == \"sampled-softmax\"\n        else config.uniform_negatives + config.inbatch_negatives\n    )\"\"\",1)\nrep(\"\"\"            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",\"\"\"            training_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\"\"\",1)\nrep(\"\"\"            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\"\"\",\"\"\"            validation_counts,\n            item_correction,\n            pool_probabilities,\n            known_positive_packed,\n            device,\n        )\"\"\")\nrep(\"\"\"                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\"\"\",\"\"\"                    validation_counts,\n                    item_correction,\n                    pool_probabilities,\n                    known_positive_packed,\n                    device,\n                )\"\"\")\nrep(\"\"\"    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    pool_probabilities = build_pool_distribution(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\"\"\",1)\nopen(p,'w').write(s)\nprint('ok')\nEOF\ngrep -n \"pool_probabilities\" train_current_model.py",
            "description": "Apply wiring patch v2"
          },
          "result": {
            "timestamp": "2026-09-24T17:43:30.829Z",
            "content": "ok\n1423:    pool_probabilities: torch.Tensor,\n1512:                    pool_probabilities, config, generator, device\n1611:    pool_probabilities: torch.Tensor,\n1690:                    pool_probabilities, config, generator, device\n1819:    pool_probabilities = build_pool_distribution(\n1944:                    pool_probabilities,\n1968:            pool_probabilities,\n1983:            pool_probabilities,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:43:30.990Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14905602 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 23,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:33.603Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:43:33.603Z",
          "text": "Now add the CLI flags and metadata, then smoke-test:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:41.495Z",
          "callId": "toolu_017wF5aPwdnCDY7KALMFgkfw",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ndef rep(a,b,count=1):\n    global s\n    assert s.count(a)>0, a[:60]\n    s=s.replace(a,b,count)\nrep(\"\"\"    parser.add_argument(\n        \"--negative-filter-scope\",\"\"\",\"\"\"    parser.add_argument(\n        \"--negative-objective\",\n        choices=(\"sampled-softmax\", \"bce-mixed-negatives\"),\n        default=Config.negative_objective,\n    )\n    parser.add_argument(\"--pool-size\", type=int, default=Config.pool_size)\n    parser.add_argument(\n        \"--pool-uniform-fraction\",\n        type=float,\n        default=Config.pool_uniform_fraction,\n    )\n    parser.add_argument(\n        \"--pool-popularity-power\",\n        type=float,\n        default=Config.pool_popularity_power,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\"\"\")\nrep(\"\"\"        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\"\"\",\"\"\"        loss_microbatch_size=args.loss_microbatch_size,\n        negative_objective=args.negative_objective,\n        pool_size=args.pool_size,\n        pool_uniform_fraction=args.pool_uniform_fraction,\n        pool_popularity_power=args.pool_popularity_power,\n        negative_filter_scope=args.negative_filter_scope,\"\"\")\nrep(\"\"\"    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\"\"\",\n\"\"\"    if args.pool_size < 2 or not 0.0 <= args.pool_uniform_fraction <= 1.0:\n        parser.error(\"pool size must exceed one and the uniform fraction is a share\")\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\"\"\")\nrep('''                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),''','''                    \"negative_sampling\": (\n                        f\"shared-pool-{config.pool_size}-sampled-softmax-\"\n                        f\"uniform{config.pool_uniform_fraction:g}-\"\n                        f\"popularity-power{config.pool_popularity_power:g}\"\n                        if config.negative_objective == \"sampled-softmax\"\n                        else \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"objective_variant\": config.negative_objective,''')\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF\npython -c \"import ast;ast.parse(open('train_current_model.py').read())\" && echo SYNTAX_OK",
            "description": "Add CLI flags and metadata"
          },
          "result": {
            "timestamp": "2026-09-24T17:43:41.644Z",
            "content": "ok\nSYNTAX_OK",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:43:41.648Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14904392 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 24,
      "role": "agent",
      "timestamp": "2026-09-24T17:43:49.955Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:43:49.955Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:43:52.109Z",
          "callId": "toolu_018mXtSYrswSz54Lj8T9F2V9",
          "name": "Bash",
          "input": {
            "command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 2 --max-train-targets-per-user 4 --max-validation-targets-per-user 2 --pool-size 256 --ranking-batch-size 512 --output /app/solution/smoke.parquet > smoke.log 2>&1 &\nsleep 45; echo \"=== smoke ===\"; tail -n 12 smoke.log; echo \"=== base ===\"; tail -n 3 base_val.log",
            "description": "Smoke test improved trainer"
          },
          "result": {
            "timestamp": "2026-09-24T17:44:37.146Z",
            "content": "=== smoke ===\nloaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=36,027 validation=17,721 selected=53,748 discarded_old_targets=19,993,446 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.000 max=2.108\nnegative pool distribution: uniform_fraction=0.50 power=0.75 top1000_mass=0.128 min=1.667e-05 max=6.617e-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=5,558,741 bytes=34,256,250\nepoch=1/1 train_loss=9.63937 validation_loss=9.54382 pair_accuracy=0.653 train_targets=36,027 validation_targets=17,721 train_seconds=14.8 validation_seconds=1.5\nrestored best validation-BCE checkpoint: epoch=1 loss=9.543819\n=== base ===\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:44:37.149Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14903715 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 25,
      "role": "agent",
      "timestamp": "2026-09-24T17:44:47.530Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:44:47.530Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:44:48.177Z",
          "text": "Smoke test passes. Launching the improved runs (validation for measurement, submission for the deliverable) in parallel:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:44:50.534Z",
          "callId": "toolu_01VBotbdsFJ15pecmMjnn6ib",
          "name": "Bash",
          "input": {
            "command": "pkill -f \"smoke.parquet\" ; sleep 1\nnohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --output /app/solution/submission.parquet > improved_sub.log 2>&1 &\nsleep 2\nnohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 2 --output /app/solution/improved_validation.parquet > improved_val.log 2>&1 &\nsleep 5; ps -o pid,etime,pcpu,args -C python | cut -c1-140",
            "description": "Launch improved submission and validation runs"
          },
          "result": {
            "timestamp": "2026-09-24T17:44:50.607Z",
            "content": "Exit code 144",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:44:50.609Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14902252 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 26,
      "role": "agent",
      "timestamp": "2026-09-24T17:44:54.446Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:44:54.446Z",
          "callId": "toolu_01U7sQAoXtHsUw1UdA7rFvUT",
          "name": "Bash",
          "input": {
            "command": "sleep 20; ps aux | grep -c \"train_current_model\"; tail -n 2 improved_sub.log; echo ---; tail -n 2 improved_val.log; echo ---; tail -n2 base_val.log",
            "description": "Check running processes and logs"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:14.488Z",
            "content": "4\ntail: cannot open 'improved_sub.log' for reading: No such file or directory\n---\ntail: cannot open 'improved_val.log' for reading: No such file or directory\n---\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=249.2s",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:14.490Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14902064 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 27,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:19.799Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:45:19.799Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:20.576Z",
          "callId": "toolu_01U4AZaHaWUojuG7ok7fMqo2",
          "name": "Bash",
          "input": {
            "command": "pwd; ls /app/*.log /app/solution/*.log 2>/dev/null; ps -eo pid,etime,pcpu,args | grep train_curr | grep -v grep | cut -c1-150",
            "description": "Find logs and running processes"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:20.613Z",
            "content": "/app/solution\n/app/solution/base_val.log\n/app/solution/prep_sub.log\n/app/solution/prep_val.log\n/app/solution/smoke.log\n   8240       04:56  221 python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:20.615Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14901563 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 28,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:28.012Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:45:28.012Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:29.398Z",
          "callId": "toolu_01SxGhewFJrBWhH79gY6NeoX",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --output /app/solution/submission.parquet > improved_sub.log 2>&1 < /dev/null & \nsleep 3; echo launched; ls -l improved_sub.log",
            "description": "Launch improved submission run detached"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:32.422Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --output /app/solution/submission.parquet > improved_sub.log 2>&1 < /dev/null\nlaunched\n-rw-r--r-- 1 agent agent 0 Sep 24 17:45 improved_sub.log",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:32.423Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14900897 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 29,
      "role": "agent",
      "timestamp": "2026-09-24T17:45:36.258Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:45:36.258Z",
          "callId": "toolu_011cD1LfEHQdBLDYpNirfgSG",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 2 --output /app/solution/improved_validation.parquet > improved_val.log 2>&1 < /dev/null &\nsleep 20; ps -eo pid,etime,pcpu,args | grep train_curr | grep -v grep | cut -c1-120; echo ---; tail -n2 improved_sub.log; tail -n1 improved_val.log",
            "description": "Launch improved validation run detached"
          },
          "result": {
            "timestamp": "2026-09-24T17:45:56.318Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 2 --output /app/solution/improved_validation.parquet > improved_val.log 2>&1 < /dev/null\n   8240       05:32  228 python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepa\n   8335       00:26  227 python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solut\n   8352       00:20  119 python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solut\n---\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:45:56.321Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14900497 tokens left</total_tokens>"
          }
        }
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    },
    {
      "id": 30,
      "role": "agent",
      "timestamp": "2026-09-24T17:46:04.300Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:46:04.300Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:46:05.110Z",
          "callId": "toolu_015rUvaVGLmkH1YVnmceeKEy",
          "name": "Bash",
          "input": {
            "command": "sleep 150; tail -n2 base_val.log; echo === ; tail -n2 improved_sub.log; echo ===; tail -n2 improved_val.log",
            "description": "Monitor run progress"
          },
          "result": {
            "timestamp": "2026-09-24T17:48:05.300Z",
            "content": "Exit code 143\nCommand timed out after 2m 0s",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:48:05.329Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14899548 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 31,
      "role": "agent",
      "timestamp": "2026-09-24T17:48:08.995Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:48:08.995Z",
          "callId": "toolu_015o2QHomwtKMTky5dbejqkQ",
          "name": "Bash",
          "input": {
            "command": "tail -n2 base_val.log; echo === ; tail -n2 improved_sub.log; echo ===; tail -n2 improved_val.log",
            "description": "Check logs"
          },
          "result": {
            "timestamp": "2026-09-24T17:48:09.049Z",
            "content": "epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=266.5 validation_seconds=20.9\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n===\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n===\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:48:09.051Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14899376 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 32,
      "role": "agent",
      "timestamp": "2026-09-24T17:48:18.575Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:48:18.575Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:48:40.623Z",
          "callId": "toolu_0141FL7onkKtPtP73hUshYeD",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/README.md",
            "content": "# Made-for-You two-tower: negative-sampling fix\n\n## Diagnosis\n\nThe production trainer (`--negative-objective bce-mixed-negatives`, the original\nbehaviour) scores every target against **32 negatives**: 24 drawn uniformly over\nthe 30,000-track catalog plus 8 unique in-batch targets, then optimizes a\nsample-weighted binary cross entropy.\n\nTwo things go wrong with that choice of negatives, and both hurt exactly the\nmetric the shelf is graded on (graded NDCG@10 over an eligible candidate set\nthat is dominated by frequently served tracks):\n\n1. **The negatives are too easy and too few.** A uniform draw over a\n   popularity-ordered catalog of 30k almost always returns a tail track. After a\n   few updates the item tower separates \"tail track\" from \"track this user\n   actually played\" using little more than the catalog popularity features, so\n   the remaining gradient signal carries almost no information about the ordering\n   *among plausible candidates*. With only 32 independent BCE negatives per\n   target there is also no mechanism that concentrates the gradient on the few\n   candidates that are actually competing for the top-10 slots.\n2. **The in-batch propensity correction pushes in the wrong direction for this\n   objective.** The correction is `((1/N)/p_i)^0.5`, clipped to `[0.25, 4]`, so\n   the *popular* in-batch negatives — the ones that crowd out true positives at\n   rank 1..10 — are down-weighted to as little as 0.25, while rare negatives are\n   amplified up to 4x. The eight in-batch negatives therefore fail to repair the\n   easiness of the 24 uniform ones.\n\nNet effect: a retriever that is well calibrated against the tail but poorly\nordered inside the head, which is where the ranking is decided.\n\n## Change\n\nReplaced the per-row 32-negative BCE with a **shared-pool sampled softmax**\n(`--negative-objective sampled-softmax`, now the default). Per truncated-BPTT\nblock:\n\n- draw a pool of `--pool-size 2048` items (with replacement, then deduplicated)\n  from a mixture distribution: 50 % uniform floor + 50 % popularity-tempered\n  `count^0.75` over the selected training targets. The uniform floor keeps tail\n  coverage; the tempered component makes the typical negative a plausible,\n  frequently served competitor;\n- encode the pool **once** and score all of the block's targets against it with\n  one matmul, so every target now gets ~2000 negatives instead of 32 while the\n  item-tower cost per block actually *drops* (the old path encoded nearly the\n  whole catalog per block through `torch.unique` over 6,400x33 draws);\n- correct each sampled negative by its inclusion probability,\n  `s_j - log(1 - (1 - q_j)^S)`, i.e. a Horvitz-Thompson estimate of the\n  full-catalog softmax denominator. This is an unbiased popularity correction of\n  the right sign, unlike the previous clipped `p^-0.5` re-weighting;\n- keep the softmax cross entropy `logsumexp(...) - s_pos`, which automatically\n  concentrates the gradient on the hardest surviving negatives;\n- keep the production false-negative discipline: the existing packed\n  full-training-history positive filter, the row's target, and the row's active\n  likes are masked out of the pool per row before the softmax.\n\nEverything else is untouched: the chronological data protocol, the 50 %-completion\nsequence tokens, the organic-strong target weighting, the causal user features,\nthe explicit like/dislike channels, the GRU + two-MLP two-tower architecture, raw\ndot-product scoring, precomputed item vectors, and the indexed masked-top-100\nserving path. No cross-encoder, no per-candidate sequence model, no user-ID\nembedding.\n\n## Public backtest (stage `validation`, train on `train_*` only)\n\n| model | graded NDCG@10 |\n| --- | --- |\n| production baseline (`24 uniform + 8 in-batch BCE`) | BASELINE_SCORE |\n| shared-pool sampled softmax (2048, this repo) | IMPROVED_SCORE |\n\nInternal chronological diagnostics are not comparable across the two objectives\n(BCE vs. softmax loss units), so selection was done on the public backtest NDCG.\n\n## Commands\n\n```sh\n# public backtest of the improved model\npython /app/solution/train_current_model.py --stage validation \\\n  --public-dir /task --prepared-cache /app/solution/cache_val.npz \\\n  --output /app/solution/improved_validation.parquet\npython /task/evaluate_public.py /app/solution/improved_validation.parquet\n\n# final hidden-test artifact, refit on train_* + val_*\npython /app/solution/train_current_model.py --stage submission \\\n  --public-dir /task --prepared-cache /app/solution/cache_sub.npz \\\n  --output /app/solution/submission.parquet\n```\n\n`--negative-objective bce-mixed-negatives` reproduces the previous production\nobjective for A/B comparison.\n"
          },
          "result": {
            "timestamp": "2026-09-24T17:48:40.646Z",
            "content": "File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:48:40.648Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14896817 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 33,
      "role": "agent",
      "timestamp": "2026-09-24T17:48:45.203Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:48:45.203Z",
          "callId": "toolu_01FCFpUiNw42MPoyypdkxfnJ",
          "name": "Bash",
          "input": {
            "command": "sleep 110; tail -n3 base_val.log | cut -c1-120; echo === ; tail -n2 improved_sub.log|cut -c1-120; echo ===; tail -n1 improved_val.log|cut -c1-120; ls -l *.parquet 2>/dev/null",
            "description": "Monitor progress"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:35.254Z",
            "content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=249.2s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n===\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n===\n}\n-rw-r--r-- 1 agent agent 1217436 Sep 24 17:50 improved_validation.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:35.256Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14896587 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 34,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:41.346Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:50:41.346Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:42.392Z",
          "callId": "toolu_0139q5Xjq5fnoGhngwgnZh55",
          "name": "Bash",
          "input": {
            "command": "grep -E \"epoch=1/1|restored|ranked users=8|runtime_seconds|negative_sampling|pool\" improved_val.log | cut -c1-160; echo ===; python /task/evaluate_public.py /app/solution/improved_validation.parquet",
            "description": "Evaluate improved validation ranking"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:45.895Z",
            "content": "negative pool distribution: uniform_fraction=0.50 power=0.75 top1000_mass=0.108 min=1.667e-05 max=5.435e-04\nepoch=1/1 batches=250 targets=987,417 batch_bce=9.558113 running_bce=9.368252 elapsed=152.1s\nepoch=1/1 train_loss=9.36221 validation_loss=9.12709 pair_accuracy=0.714 train_targets=1,052,953 validation_targets=130,103 train_seconds=161.2 validation_secon\nrestored best validation-BCE checkpoint: epoch=1 loss=9.127094\nranked users=8,192/8,456\nranked users=8,456/8,456\n  \"runtime_seconds\": 289.344,\n    \"negative_sampling\": \"shared-pool-2048-sampled-softmax-uniform0.5-popularity-power0.75\",\n    \"pool_size\": 2048,\n    \"pool_uniform_fraction\": 0.5,\n    \"pool_popularity_power\": 0.75,\n===\n{\n  \"model\": \"/app/solution/improved_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01613,\n    \"quality_users\": 4263\n  }\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:45.897Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14895872 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 35,
      "role": "agent",
      "timestamp": "2026-09-24T17:50:53.346Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:50:53.346Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:50:54.163Z",
          "callId": "toolu_01MaCSztFmq8ZPPgHBwhmMyn",
          "name": "Bash",
          "input": {
            "command": "tail -n1 base_val.log|cut -c1-100; echo ===; tail -n2 improved_sub.log|cut -c1-140; ls -l submission.parquet 2>/dev/null",
            "description": "Check baseline and submission status"
          },
          "result": {
            "timestamp": "2026-09-24T17:50:54.185Z",
            "content": "Exit code 2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n===\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:50:54.188Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14894922 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 36,
      "role": "agent",
      "timestamp": "2026-09-24T17:51:00.994Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:51:00.994Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:51:01.841Z",
          "callId": "toolu_01QVPHtGBnP5ksdLwAmFNwPf",
          "name": "Bash",
          "input": {
            "command": "ps -eo pid,etime,pcpu,rss,args | grep train_curr | grep -v grep | cut -c1-110; free -g | head -2; ls -l improved_sub.log",
            "description": "Check process states and memory"
          },
          "result": {
            "timestamp": "2026-09-24T17:51:01.872Z",
            "content": "               total        used        free      shared  buff/cache   available\nMem:              15           1           8           0           6          14\n-rw-r--r-- 1 agent agent 519 Sep 24 17:45 improved_sub.log",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:51:01.874Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14894383 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 37,
      "role": "agent",
      "timestamp": "2026-09-24T17:51:17.190Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:51:17.190Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:51:18.520Z",
          "callId": "toolu_01NraNA8Gdv1pQTepW82sF7o",
          "name": "Bash",
          "input": {
            "command": "cat improved_sub.log; echo \"=== dmesg/oom ===\"; (dmesg 2>/dev/null | tail -5) || echo nodmesg; date",
            "description": "Inspect submission log tail for errors"
          },
          "result": {
            "timestamp": "2026-09-24T17:51:18.539Z",
            "content": "loaded full-sequence cache: tokens=20,712,971 targets=20,703,818\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nnegative pool distribution: uniform_fraction=0.50 power=0.75 top1000_mass=0.108 min=1.667e-05 max=5.378e-04\ntarget weighting=organic-strong min=0.250 mean=0.576 max=1.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n=== dmesg/oom ===\nThu Sep 24 17:51:18 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:51:18.541Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14893248 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 38,
      "role": "agent",
      "timestamp": "2026-09-24T17:51:27.066Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:51:27.066Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:51:28.586Z",
          "callId": "toolu_01QPxFzFrUQWvKg2ojUm1FdJ",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 5 --output /app/solution/submission.parquet > improved_sub.log 2>&1 < /dev/null &\nsleep 4; echo ok; ps -eo pid,etime,args|grep -c \"stage submission\"",
            "description": "Relaunch submission run"
          },
          "result": {
            "timestamp": "2026-09-24T17:51:32.609Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 5 --output /app/solution/submission.parquet > improved_sub.log 2>&1 < /dev/null\nok\n4",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:51:32.611Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14892326 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 39,
      "role": "agent",
      "timestamp": "2026-09-24T17:51:37.872Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:51:37.872Z",
          "callId": "toolu_01QTFTVNL5CZkSA6J7a2xzBc",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 3 --output /app/solution/production_validation.parquet > base_val.log 2>&1 < /dev/null &\nsleep 4; ps -eo pid,etime,args | grep train_curr | grep -v grep | cut -c1-90",
            "description": "Relaunch baseline validation run"
          },
          "result": {
            "timestamp": "2026-09-24T17:51:41.902Z",
            "content": "[1]+  Done                    setsid nohup python /task/current_model/train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 3 --output /app/solution/production_validation.parquet > base_val.log 2>&1 < /dev/null\n   8449       00:13 python train_current_model.py --stage submission --public-dir /task --\n   8469       00:04 python /task/current_model/train_current_model.py --stage validation -",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:51:41.906Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14891984 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 40,
      "role": "agent",
      "timestamp": "2026-09-24T17:51:46.406Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:51:46.406Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:51:56.009Z",
          "callId": "toolu_01H4mmEDHrGuCJh2kko4SPbV",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/check_submission.py",
            "content": "#!/usr/bin/env python3\n\"\"\"Validate a submission artifact against the hidden-test candidate contract.\"\"\"\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 = (\n        pd.read_parquet(path)\n        if path.endswith(\".parquet\")\n        else pd.read_csv(path)\n    )\n    targets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\n    with np.load(DATA / \"test_eligible_candidates.npz\", allow_pickle=False) as art:\n        uids = art[\"uids\"].astype(np.int64)\n        item_ids = art[\"item_ids\"].astype(np.int64)\n        packed = art[\"packed_eligible\"].astype(np.uint8)\n\n    problems: list[str] = []\n    for column in (\"uid\", \"item_id\", \"rank\"):\n        if column not in frame.columns:\n            problems.append(f\"missing column {column}\")\n    if problems:\n        print(\"\\n\".join(problems))\n        return 1\n    if not np.isfinite(frame[[\"uid\", \"item_id\", \"rank\"]].to_numpy(dtype=float)).all():\n        problems.append(\"non-finite entries\")\n    users = set(frame.uid.astype(int))\n    if users != targets:\n        problems.append(\n            f\"user mismatch: missing={len(targets - users)} extra={len(users - targets)}\"\n        )\n    counts = frame.groupby(\"uid\").rank.agg([\"count\", \"nunique\", \"min\", \"max\"])\n    if not (counts[\"count\"] == 100).all():\n        problems.append(\"not every user has exactly 100 rows\")\n    if not ((counts.nunique == 100) & (counts[\"min\"] == 1) & (counts[\"max\"] == 100)).all():\n        problems.append(\"ranks are not a 1..100 permutation for every user\")\n    if frame.groupby(\"uid\").item_id.nunique().ne(100).any():\n        problems.append(\"duplicate items within a user\")\n\n    rows = {int(uid): row for row, uid in enumerate(uids)}\n    columns = pd.Index(item_ids)\n    mapped = columns.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n    if (mapped < 0).any():\n        problems.append(f\"{int((mapped < 0).sum())} items outside the catalog\")\n    ineligible = 0\n    for uid, group in frame.groupby(\"uid\"):\n        row = rows[int(uid)]\n        mask = np.unpackbits(packed[row], count=len(item_ids), bitorder=\"little\")\n        local = columns.get_indexer(group.item_id.to_numpy(dtype=np.int64))\n        ineligible += int((local < 0).sum() + (mask[local[local >= 0]] == 0).sum())\n    if ineligible:\n        problems.append(f\"{ineligible} ineligible user-item pairs\")\n\n    print(\n        f\"rows={len(frame):,} users={len(users):,} \"\n        f\"status={'OK' if not problems else 'INVALID'}\"\n    )\n    for problem in problems:\n        print(f\"- {problem}\")\n    return 1 if problems else 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main(sys.argv[1]))\n"
          },
          "result": {
            "timestamp": "2026-09-24T17:51:56.019Z",
            "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": "2026-09-24T17:51:56.022Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14890455 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 41,
      "role": "agent",
      "timestamp": "2026-09-24T17:52:00.380Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:52:00.380Z",
          "callId": "toolu_011rtkdo8sM1q9BXL37MFAsp",
          "name": "Bash",
          "input": {
            "command": "sleep 90; tail -n1 improved_sub.log|cut -c1-130; echo ===; tail -n1 base_val.log|cut -c1-130",
            "description": "Monitor runs"
          },
          "result": {
            "timestamp": "2026-09-24T17:53:30.419Z",
            "content": "full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n===\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:53:30.425Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14890266 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 42,
      "role": "agent",
      "timestamp": "2026-09-24T17:53:33.152Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:53:33.152Z",
          "callId": "toolu_01KzQJEVBnranfG2jNkM7pm5",
          "name": "Bash",
          "input": {
            "command": "sleep 95; tail -n1 improved_sub.log|cut -c1-130; echo ===; tail -n1 base_val.log|cut -c1-130; date",
            "description": "Monitor runs"
          },
          "result": {
            "timestamp": "2026-09-24T17:55:08.197Z",
            "content": "restored best validation-BCE checkpoint: epoch=1 loss=9.174262\n===\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nThu Sep 24 17:55:08 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:55:08.199Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14890032 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 43,
      "role": "agent",
      "timestamp": "2026-09-24T17:55:15.039Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:55:15.039Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:55:15.904Z",
          "callId": "toolu_01G22TYY37ZquERukL6oiFNh",
          "name": "Bash",
          "input": {
            "command": "sleep 85; tail -n2 improved_sub.log|cut -c1-100; ls -l submission.parquet 2>/dev/null; echo ===; tail -n1 base_val.log|cut -c1-110",
            "description": "Wait for submission artifact"
          },
          "result": {
            "timestamp": "2026-09-24T17:56:40.944Z",
            "content": "  \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 1175318 Sep 24 17:56 submission.parquet\n===\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:40.946Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14889463 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 44,
      "role": "agent",
      "timestamp": "2026-09-24T17:56:44.137Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T17:56:44.137Z",
          "text": "Submission artifact exists. Validating it and launching a harder-pool ablation:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:56:44.324Z",
          "callId": "toolu_019apFidt9JSYv46CtS4t3Vb",
          "name": "Bash",
          "input": {
            "command": "python check_submission.py submission.parquet",
            "description": "Validate submission artifact"
          },
          "result": {
            "timestamp": "2026-09-24T17:56:46.265Z",
            "content": "Exit code 1\nTraceback (most recent call last):\n  File \"/app/solution/check_submission.py\", line 72, in <module>\n    raise SystemExit(main(sys.argv[1]))\n                     ^^^^^^^^^^^^^^^^^\n  File \"/app/solution/check_submission.py\", line 40, in main\n    counts = frame.groupby(\"uid\").rank.agg([\"count\", \"nunique\", \"min\", \"max\"])\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'function' object has no attribute 'agg'",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:46.267Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14889230 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 45,
      "role": "agent",
      "timestamp": "2026-09-24T17:56:49.321Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:56:49.321Z",
          "callId": "toolu_01BhoMJ33vpCz7oM8ZXqXVbj",
          "name": "Bash",
          "input": {
            "command": "sed -i 's/frame.groupby(\"uid\").rank.agg/frame.groupby(\"uid\")[\"rank\"].agg/' check_submission.py && python check_submission.py submission.parquet",
            "description": "Fix and rerun validator"
          },
          "result": {
            "timestamp": "2026-09-24T17:56:52.302Z",
            "content": "Exit code 1\nrows=845,600 users=8,456 status=INVALID\n- ranks are not a 1..100 permutation for every user",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:52.311Z",
          "data": {
            "type": "edited_text_file",
            "filename": "/app/solution/check_submission.py",
            "snippet": "32\t        return 1\n33\t    if not np.isfinite(frame[[\"uid\", \"item_id\", \"rank\"]].to_numpy(dtype=float)).all():\n34\t        problems.append(\"non-finite entries\")\n35\t    users = set(frame.uid.astype(int))\n36\t    if users != targets:\n37\t        problems.append(\n38\t            f\"user mismatch: missing={len(targets - users)} extra={len(users - targets)}\"\n39\t        )\n40\t    counts = frame.groupby(\"uid\")[\"rank\"].agg([\"count\", \"nunique\", \"min\", \"max\"])\n41\t    if not (counts[\"count\"] == 100).all():\n42\t        problems.append(\"not every user has exactly 100 rows\")\n43\t    if not ((counts.nunique == 100) & (counts[\"min\"] == 1) & (counts[\"max\"] == 100)).all():\n44\t        problems.append(\"ranks are not a 1..100 permutation for every user\")\n45\t    if frame.groupby(\"uid\").item_id.nunique().ne(100).any():\n46\t        problems.append(\"duplicate items within a user\")\n47\t\n48\t    rows = {int(uid): row for row, uid in enumerate(uids)}"
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:56:52.311Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14888852 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 46,
      "role": "agent",
      "timestamp": "2026-09-24T17:56:57.028Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:56:57.028Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:56:57.773Z",
          "callId": "toolu_015ieuRrABK8L7W859ppLcSq",
          "name": "Bash",
          "input": {
            "command": "sed -i 's/(counts.nunique == 100)/(counts[\"nunique\"] == 100)/' check_submission.py && sed -i 's/frame.groupby(\"uid\").item_id.nunique()/frame.groupby(\"uid\")[\"item_id\"].nunique()/' check_submission.py && python check_submission.py submission.parquet",
            "description": "Fix validator column access and rerun"
          },
          "result": {
            "timestamp": "2026-09-24T17:57:00.851Z",
            "content": "rows=845,600 users=8,456 status=OK",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:57:00.857Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14888047 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 47,
      "role": "agent",
      "timestamp": "2026-09-24T17:57:08.254Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T17:57:08.254Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:57:10.116Z",
          "callId": "toolu_01AxQ4hAuTnQWHhTUDLX33Zb",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --pool-uniform-fraction 0.2 --pool-popularity-power 1.0 --output /app/solution/improved_validation_hard.parquet > improved_val_hard.log 2>&1 < /dev/null &\nsleep 5; ps -eo pid,etime,args|grep train_curr|grep -v grep|cut -c1-80",
            "description": "Launch harder-pool ablation"
          },
          "result": {
            "timestamp": "2026-09-24T17:57:15.144Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --pool-uniform-fraction 0.2 --pool-popularity-power 1.0 --output /app/solution/improved_validation_hard.parquet > improved_val_hard.log 2>&1 < /dev/null\n   8469       05:37 python /task/current_model/train_current_model.py --stage va\n   8581       00:05 python train_current_model.py --stage validation --public-di",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:57:15.147Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14887395 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 48,
      "role": "agent",
      "timestamp": "2026-09-24T17:57:18.334Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:57:18.334Z",
          "callId": "toolu_01BaCxfdZX3TxwETpHj1M4Jo",
          "name": "Bash",
          "input": {
            "command": "sleep 95; tail -n1 base_val.log|cut -c1-110; ls -l production_validation.parquet 2>/dev/null; echo ===; tail -n1 improved_val_hard.log|cut -c1-110",
            "description": "Monitor baseline and ablation"
          },
          "result": {
            "timestamp": "2026-09-24T17:58:53.366Z",
            "content": "restored best validation-BCE checkpoint: epoch=1 loss=0.496636\n===\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T17:58:53.369Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14887027 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 49,
      "role": "agent",
      "timestamp": "2026-09-24T17:58:57.320Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T17:58:57.320Z",
          "callId": "toolu_01VGYVX1CVbfz7Cd6mPsRgmR",
          "name": "Bash",
          "input": {
            "command": "sleep 95; ls -l production_validation.parquet 2>/dev/null && python /task/evaluate_public.py /app/solution/production_validation.parquet; echo ===; tail -n1 improved_val_hard.log|cut -c1-110; date",
            "description": "Score baseline when ready"
          },
          "result": {
            "timestamp": "2026-09-24T18:00:37.297Z",
            "content": "-rw-r--r-- 1 agent agent 1244230 Sep 24 18:00 production_validation.parquet\n{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n===\nrestored best validation-BCE checkpoint: epoch=1 loss=9.113682\nThu Sep 24 18:00:37 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:00:37.300Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14886771 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 50,
      "role": "agent",
      "timestamp": "2026-09-24T18:01:42.331Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:01:42.331Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:01:43.364Z",
          "text": "Baseline is 0.02128 vs my 0.01613 — the softmax variant regressed. The likely culprit: CE loss gradients are ~10x larger, so `gradient_clip=2.0` truncates every step. Adding knobs and testing two fixes in parallel:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:01:48.422Z",
          "callId": "toolu_01X58C4e1oqU8Cts1YUCzmEc",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ndef rep(a,b,count=1):\n    global s\n    assert s.count(a)>0, a[:50]\n    s=s.replace(a,b,count)\nrep(\"\"\"    parser.add_argument(\"--pool-size\", type=int, default=Config.pool_size)\"\"\",\n\"\"\"    parser.add_argument(\"--pool-size\", type=int, default=Config.pool_size)\n    parser.add_argument(\n        \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n    )\n    parser.add_argument(\n        \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n    )\n    parser.add_argument(\n        \"--gradient-clip\", type=float, default=Config.gradient_clip\n    )\"\"\")\nrep(\"\"\"        negative_objective=args.negative_objective,\"\"\",\n\"\"\"        negative_objective=args.negative_objective,\n        uniform_negatives=args.uniform_negatives,\n        inbatch_negatives=args.inbatch_negatives,\n        gradient_clip=args.gradient_clip,\"\"\")\nrep(\"\"\"    if args.pool_size < 2 or\"\"\",\"\"\"    if args.gradient_clip <= 0 or args.uniform_negatives < 0:\n        parser.error(\"gradient clip must be positive and negative counts >= 0\")\n    if args.pool_size < 2 or\"\"\")\nopen(p,'w').write(s)\nprint('ok')\nEOF\npython -c \"import ast;ast.parse(open('train_current_model.py').read())\" && echo OK; tail -n1 improved_val_hard.log | cut -c1-90",
            "description": "Add negative-count and clip CLI flags"
          },
          "result": {
            "timestamp": "2026-09-24T18:01:48.572Z",
            "content": "ok\nOK\n}",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:01:48.574Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14881400 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 51,
      "role": "agent",
      "timestamp": "2026-09-24T18:01:52.739Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:01:52.739Z",
          "callId": "toolu_01VfPmtSNDmgt2kfbbgAPUfW",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py /app/solution/improved_validation_hard.parquet; grep -E \"train_loss|pair_acc\" improved_val_hard.log | tail -n1 | cut -c1-150",
            "description": "Score harder-pool ablation"
          },
          "result": {
            "timestamp": "2026-09-24T18:01:56.239Z",
            "content": "{\n  \"model\": \"/app/solution/improved_validation_hard.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.016147,\n    \"quality_users\": 4263\n  }\n}\n      \"pair_accuracy\": 0.6306062981486618,",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:01:56.240Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14881217 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 52,
      "role": "agent",
      "timestamp": "2026-09-24T18:02:16.286Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:02:16.286Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:02:24.176Z",
          "callId": "toolu_01WhomZJFAzncmhrZpQ2byFs",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ndef rep(a,b,count=1):\n    global s\n    assert s.count(a)>0, a[:50]\n    s=s.replace(a,b,count)\nrep(\"\"\"        logits = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n        row_loss = torch.logsumexp(logits, dim=1) - positive_logits\"\"\",\n\"\"\"        if config.negative_objective == \"pool-bce\":\n            # Same per-negative BCE the production trainer used, but averaged\n            # over the whole shared pool instead of 32 per-row draws.\n            valid_weights = (~torch.from_numpy(invalid).to(device)).to(\n                query_vectors.dtype\n            )\n            raw_negative = F.softplus(negative_logits + pool_log_inclusion[None, :])\n            row_loss = (\n                F.softplus(-positive_logits)\n                + (raw_negative * valid_weights).sum(dim=1)\n                / valid_weights.sum(dim=1).clamp_min(1.0)\n            ) / 2.0\n        else:\n            logits = torch.cat([positive_logits[:, None], negative_logits], dim=1)\n            row_loss = torch.logsumexp(logits, dim=1) - positive_logits\"\"\")\nrep(\"\"\"        choices=(\"sampled-softmax\", \"bce-mixed-negatives\"),\"\"\",\n\"\"\"        choices=(\"sampled-softmax\", \"pool-bce\", \"bce-mixed-negatives\"),\"\"\")\nrep(\"\"\"            if config.negative_objective == \"sampled-softmax\":\n                pool_items, pool_log_inclusion = draw_shared_pool(\"\"\",\n\"\"\"            if config.negative_objective in (\"sampled-softmax\", \"pool-bce\"):\n                pool_items, pool_log_inclusion = draw_shared_pool(\"\"\")\nrep(\"\"\"            if config.negative_objective == \"sampled-softmax\":\n                pool_items, pool_log_inclusion = draw_shared_pool(\"\"\",\n\"\"\"            if config.negative_objective in (\"sampled-softmax\", \"pool-bce\"):\n                pool_items, pool_log_inclusion = draw_shared_pool(\"\"\")\nrep(\"\"\"    negative_count = (\n        1\n        if config.negative_objective == \"sampled-softmax\\\"\"\"\",\n\"\"\"    negative_count = (\n        1\n        if config.negative_objective in (\"sampled-softmax\", \"pool-bce\")\"\"\")\nopen(p,'w').write(s)\nprint('ok')\nEOF\npython -c \"import ast;ast.parse(open('train_current_model.py').read())\" && grep -n 'pool-bce' train_current_model.py | head",
            "description": "Add pool-BCE objective variant"
          },
          "result": {
            "timestamp": "2026-09-24T18:02:24.326Z",
            "content": "ok\n758:        if config.negative_objective == \"pool-bce\":\n1454:        if config.negative_objective in (\"sampled-softmax\", \"pool-bce\")\n1523:            if config.negative_objective in (\"sampled-softmax\", \"pool-bce\"):\n1701:            if config.negative_objective in (\"sampled-softmax\", \"pool-bce\"):\n2228:        choices=(\"sampled-softmax\", \"pool-bce\", \"bce-mixed-negatives\"),",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:02:24.327Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14878773 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 53,
      "role": "agent",
      "timestamp": "2026-09-24T18:02:30.007Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:02:30.007Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:02:31.651Z",
          "callId": "toolu_018GC6MLnrw4ufvV1DYY48Ha",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective pool-bce --pool-size 2048 --output /app/solution/val_poolbce.parquet > val_poolbce.log 2>&1 < /dev/null &\nsleep 4; echo launched1",
            "description": "Launch pool-BCE validation run"
          },
          "result": {
            "timestamp": "2026-09-24T18:02:35.667Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective pool-bce --pool-size 2048 --output /app/solution/val_poolbce.parquet > val_poolbce.log 2>&1 < /dev/null\nlaunched1",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:02:35.669Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14878100 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 54,
      "role": "agent",
      "timestamp": "2026-09-24T18:02:39.725Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:02:39.725Z",
          "callId": "toolu_01JhVQL5GGAQUfdGYzyLSKKv",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective sampled-softmax --gradient-clip 20 --learning-rate 2e-3 --output /app/solution/val_softmax_clip20.parquet > val_softmax_clip20.log 2>&1 < /dev/null &\nsleep 30; tail -n1 val_poolbce.log|cut -c1-90; tail -n1 val_softmax_clip20.log|cut -c1-90; date",
            "description": "Launch softmax+clip20 validation run"
          },
          "result": {
            "timestamp": "2026-09-24T18:03:09.769Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective sampled-softmax --gradient-clip 20 --learning-rate 2e-3 --output /app/solution/val_softmax_clip20.parquet > val_softmax_clip20.log 2>&1 < /dev/null\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,2\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,2\nThu Sep 24 18:03:09 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:03:09.772Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14877677 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 55,
      "role": "agent",
      "timestamp": "2026-09-24T18:03:18.365Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:03:18.365Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:03:19.234Z",
          "callId": "toolu_01QYymq3sUk7ygNfaaoeUut5",
          "name": "Bash",
          "input": {
            "command": "sleep 100; tail -n1 val_poolbce.log|cut -c1-120; tail -n1 val_softmax_clip20.log|cut -c1-120",
            "description": "Monitor both variants"
          },
          "result": {
            "timestamp": "2026-09-24T18:04:59.298Z",
            "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:04:59.301Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14876791 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 56,
      "role": "agent",
      "timestamp": "2026-09-24T18:05:03.193Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:05:03.193Z",
          "callId": "toolu_01EAJhjrG7LxToH2cs4rRDGu",
          "name": "Bash",
          "input": {
            "command": "sleep 110; grep -E \"batches=250|train_loss\" val_poolbce.log|tail -n1|cut -c1-140; grep -E \"batches=250|train_loss\" val_softmax_clip20.log|tail -n1|cut -c1-140; date",
            "description": "Monitor training progress"
          },
          "result": {
            "timestamp": "2026-09-24T18:06:53.250Z",
            "content": "epoch=1/1 train_loss=0.58235 validation_loss=0.52434 pair_accuracy=0.734 train_targets=1,052,953 validation_targets=130,103 train_seconds=16\nepoch=1/1 train_loss=9.33874 validation_loss=9.09906 pair_accuracy=0.719 train_targets=1,052,953 validation_targets=130,103 train_seconds=16\nThu Sep 24 18:06:53 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:06:53.252Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14876526 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 57,
      "role": "agent",
      "timestamp": "2026-09-24T18:07:17.532Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:07:17.532Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:07:18.762Z",
          "callId": "toolu_01Uv5G5YvHteVSf6wuftHJTc",
          "name": "Bash",
          "input": {
            "command": "sleep 75; for f in val_poolbce val_softmax_clip20; do ls $f.parquet >/dev/null 2>&1 && python /task/evaluate_public.py /app/solution/$f.parquet | grep ndcg; done; date",
            "description": "Score both variants"
          },
          "result": {
            "timestamp": "2026-09-24T18:08:40.662Z",
            "content": "    \"quality_ndcg@10\": 0.01017,\n    \"quality_ndcg@10\": 0.015668,\nThu Sep 24 18:08:40 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:08:40.663Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14874620 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 58,
      "role": "agent",
      "timestamp": "2026-09-24T18:08:58.642Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:08:58.642Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:08:59.461Z",
          "text": "Both variants confirm the signal: popularity-weighted negatives hurt this metric. Launching the corrected candidate plus a production-parity fallback refit in parallel:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:09:01.450Z",
          "callId": "toolu_01SbuLCeYSmEEjBXh1sd3MNY",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective pool-bce --pool-size 2048 --pool-uniform-fraction 1.0 --output /app/solution/val_poolbce_uniform.parquet > val_poolbce_uniform.log 2>&1 < /dev/null &\nsleep 3; echo launchedA",
            "description": "Launch uniform pool-BCE validation"
          },
          "result": {
            "timestamp": "2026-09-24T18:09:04.466Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective pool-bce --pool-size 2048 --pool-uniform-fraction 1.0 --output /app/solution/val_poolbce_uniform.parquet > val_poolbce_uniform.log 2>&1 < /dev/null\nlaunchedA",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:09:04.468Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14873166 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 59,
      "role": "agent",
      "timestamp": "2026-09-24T18:09:08.230Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:09:08.230Z",
          "callId": "toolu_0146Bhf3axRjLq7C9FjfXPR5",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --negative-objective bce-mixed-negatives --output /app/solution/submission_parity.parquet > sub_parity.log 2>&1 < /dev/null &\nsleep 3; ps -eo pid,etime,args|grep train_curr|grep -v grep|cut -c1-70; date",
            "description": "Launch production-parity submission refit"
          },
          "result": {
            "timestamp": "2026-09-24T18:09:11.266Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --negative-objective bce-mixed-negatives --output /app/solution/submission_parity.parquet > sub_parity.log 2>&1 < /dev/null\n   8808       00:09 python train_current_model.py --stage validation -\n   8824       00:03 python train_current_model.py --stage submission -\nThu Sep 24 18:09:11 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:09:11.268Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14872761 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 60,
      "role": "agent",
      "timestamp": "2026-09-24T18:09:18.526Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:09:18.526Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:09:19.139Z",
          "callId": "toolu_01TZM4VzSj9QX5Yw3MSuCz27",
          "name": "Bash",
          "input": {
            "command": "sleep 115; tail -n1 val_poolbce_uniform.log|cut -c1-100; tail -n1 sub_parity.log|cut -c1-100",
            "description": "Monitor runs"
          },
          "result": {
            "timestamp": "2026-09-24T18:11:14.205Z",
            "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:11:14.209Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14872180 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 61,
      "role": "agent",
      "timestamp": "2026-09-24T18:11:17.538Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:11:17.538Z",
          "callId": "toolu_0139XPdi5b3U2fCXaXfZsnGE",
          "name": "Bash",
          "input": {
            "command": "sleep 115; grep -E \"train_loss\" val_poolbce_uniform.log|tail -n1|cut -c1-120; grep -E \"train_loss\" sub_parity.log|tail -n1|cut -c1-120; date",
            "description": "Monitor training completion"
          },
          "result": {
            "timestamp": "2026-09-24T18:13:12.618Z",
            "content": "epoch=1/1 train_loss=0.52515 validation_loss=0.47925 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,\nThu Sep 24 18:13:12 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:13:12.621Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14871925 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 62,
      "role": "agent",
      "timestamp": "2026-09-24T18:13:17.180Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:13:17.180Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:13:18.325Z",
          "callId": "toolu_01LKiBtWDnYn5icLyD6opmyw",
          "name": "Bash",
          "input": {
            "command": "sleep 105; ls val_poolbce_uniform.parquet >/dev/null 2>&1 && python /task/evaluate_public.py /app/solution/val_poolbce_uniform.parquet|grep ndcg; tail -n1 sub_parity.log|cut -c1-80; date",
            "description": "Score uniform pool-BCE variant"
          },
          "result": {
            "timestamp": "2026-09-24T18:15:07.790Z",
            "content": "    \"quality_ndcg@10\": 0.01847,\nrestored best validation-BCE checkpoint: epoch=1 loss=0.497949\nThu Sep 24 18:15:07 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:15:07.792Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14871480 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 63,
      "role": "agent",
      "timestamp": "2026-09-24T18:15:43.337Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:15:43.337Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:15:44.385Z",
          "text": "Uniform pool (0.01847) still trails production (0.02128) — every hard-negative variant loses, and the internal loss is anti-correlated with discovery NDCG. Final experiment: keep production negatives, align target weights with the discovery grades:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:15:45.952Z",
          "callId": "toolu_01UvUFYfFDYc1U3iFn4d5frS",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/val_graded.parquet > val_graded.log 2>&1 < /dev/null &\nsleep 3; echo launchedC; date",
            "description": "Launch discovery-graded validation run"
          },
          "result": {
            "timestamp": "2026-09-24T18:15:48.975Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --threads 4 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/val_graded.parquet > val_graded.log 2>&1 < /dev/null\nlaunchedC\nThu Sep 24 18:15:48 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:15:48.978Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14868869 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 64,
      "role": "agent",
      "timestamp": "2026-09-24T18:15:52.790Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:15:52.790Z",
          "callId": "toolu_01GUYmpMsicvZBvcy8hnDz3g",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/submission_graded.parquet > sub_graded.log 2>&1 < /dev/null &\nsleep 20; ls -l submission_parity.parquet 2>/dev/null; tail -n1 sub_parity.log|cut -c1-60",
            "description": "Launch discovery-graded submission refit"
          },
          "result": {
            "timestamp": "2026-09-24T18:16:12.831Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/submission_graded.parquet > sub_graded.log 2>&1 < /dev/null\nrestored best validation-BCE checkpoint: epoch=1 loss=0.4979",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:16:12.836Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14868441 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 65,
      "role": "agent",
      "timestamp": "2026-09-24T18:16:18.326Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:16:18.326Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:16:47.633Z",
          "callId": "toolu_019Nrve2GXRPLjQUFHfMUuri",
          "name": "Write",
          "input": {
            "file_path": "/app/solution/README.md",
            "content": "# Made-for-You two-tower: negative-sampling investigation and objective fix\n\nEverything below was measured with the public backtest (train on `train_*` only,\nrank the validation cutoff, score with `/task/evaluate_public.py`). The data\nprotocol, two-tower serving contract (precomputed item vectors, one user vector\nfrom observed history, raw dot product, masked top-100 over the platform\ncandidate set), and the evaluation boundary are unchanged. No cross-encoder, no\nper-candidate sequence model, no user-ID embedding.\n\n## Diagnosis\n\nThe production objective scores each target against 32 negatives: 24 uniform over\nthe 30,000-track catalog plus 8 unique in-batch targets re-weighted by\n`((1/N)/p_i)^0.5` clipped to `[0.25, 4]`, under a sample-weighted BCE. The obvious\nreading — \"uniform negatives over a popularity-ordered catalog are too easy and\ntoo few, so the retriever never learns to order the head\" — is **wrong for this\nobjective**, and the backtest says so decisively.\n\nI implemented the standard retrieval upgrade (shared negative pool, sampled\nsoftmax with a Horvitz-Thompson inclusion correction, popularity-tempered\nsampling) and every variant *lost* score, monotonically in how much the negatives\nsuppress popular items:\n\n| objective / negatives | internal chrono. loss | pair acc. | public NDCG@10 |\n| --- | --- | --- | --- |\n| production: 24 uniform + 8 corrected in-batch BCE | 0.4966 (BCE) | 0.755 | **0.02128** |\n| shared pool 2048 BCE, **uniform** pool, no correction | 0.4793 (BCE) | 0.791 | 0.01847 |\n| sampled softmax, pool 2048, 50 % uniform / 50 % `pop^0.75`, HT correction | 9.127 (CE) | 0.714 | 0.01613 |\n| same, harder pool (20 % uniform / `pop^1.0`) | 9.114 (CE) | 0.631 | 0.01615 |\n| same, softer clipping (`--gradient-clip 20`, lr 2e-3) | 9.099 (CE) | 0.719 | 0.01567 |\n| shared pool 2048 BCE, 50 % uniform / 50 % `pop^0.75`, no correction | 0.5243 (BCE) | 0.734 | 0.01017 |\n\nTwo things fall out of that table, and they are the actual diagnosis:\n\n1. **The internal chronological next-item metric is anti-correlated with the\n   discovery objective.** The uniform 2048-negative pool is a strictly\n   lower-variance estimator of the very objective the production trainer\n   optimizes: it improves the internal validation BCE (0.4793 vs 0.4966) and\n   pairwise accuracy (0.791 vs 0.755) — and it *loses* 13 % of NDCG@10. Any\n   negative-sampling change selected on the trainer's own chronological loss will\n   therefore be selected in the wrong direction. This is the limitation that\n   matters: the production negatives are not mis-specified for the shelf, the\n   *model-selection signal* is.\n2. **Harder (popularity-weighted) negatives actively destroy discovery quality.**\n   Ranking only ever happens over candidates the user has *not* encountered, and\n   grades come from organic engagement in the following week, where the item prior\n   is a large part of the signal. Popularity-weighted negatives and the softmax's\n   hardest-negative focus both normalize that prior away: `pop^0.75` negatives cost\n   half the score. Conversely the production in-batch group is not a liability —\n   its `p^-0.5` re-weighting *preserves* the prior by discounting popular\n   negatives, and every variant that dropped that group lost score.\n\nSo the production negative sampler is already close to the right operating point\nfor a next-item-trained retriever serving a discovery shelf, and the exploitable\nmismatch is elsewhere in the objective: the target distribution is dominated by\nrepeat plays, which are ineligible at ranking time by construction.\n\n## Change shipped\n\n`--target-weighting discovery-graded` with the production negative sampler\nunchanged. It reweights the per-timestep BCE so the training targets mirror the\ngraded relevance definition instead of raw next-item likelihood:\n\n- a target that is a **repeat** of an item already in the user's sequence keeps\n  only the ordinary weight 0.25 — it can never be an eligible discovery outcome;\n- a **first encounter** the user organically likes within the 7-day discovery\n  horizon (evaluated causally, capped at the chronological split so no future\n  information crosses the boundary) gets weight 3.0, matching `2^3 - 1` grade-3\n  dominance in the metric;\n- other organic first encounters completed to at least 80 % keep weight 1.0.\n\nDiagnostic runs and the shipped artifact are all one epoch, one seed, in the\nsupplied CPU environment; the shipped configuration trains and ranks in about\n5 minutes on 4 threads.\n\n## Public backtest\n\n| model | public NDCG@10 |\n| --- | --- |\n| production baseline (`organic-strong`, 24 uniform + 8 in-batch) | 0.02128 |\n| shipped (`discovery-graded`, production negatives) | GRADED_SCORE |\n\n## Submission\n\n`/app/solution/submission.parquet` is a refit on `train_* + val_*` at the\nhidden-test cutoff, validated with `check_submission.py` (8,456 users x 100 rows,\nranks 1..100 per user, all items inside `test_eligible_candidates.npz`).\n\n```sh\n# public backtest\npython /app/solution/train_current_model.py --stage validation \\\n  --public-dir /task --prepared-cache /app/solution/cache_val.npz \\\n  --output /app/solution/val_graded.parquet\npython /task/evaluate_public.py /app/solution/val_graded.parquet\n\n# hidden-test artifact\npython /app/solution/train_current_model.py --stage submission \\\n  --public-dir /task --prepared-cache /app/solution/cache_sub.npz \\\n  --output /app/solution/submission.parquet\n\npython /app/solution/check_submission.py /app/solution/submission.parquet\n```\n\nThe negative-sampling experiments above remain reproducible from this file:\n`--negative-objective sampled-softmax|pool-bce|bce-mixed-negatives` with\n`--pool-size`, `--pool-uniform-fraction`, `--pool-popularity-power`,\n`--uniform-negatives`, `--inbatch-negatives`, and `--gradient-clip`.\n\n## If more time were available\n\nThe measured anti-correlation between the trainer's chronological BCE and the\ndiscovery metric is the highest-value follow-up: replace the internal\nearly-stopping signal with a held-out *discovery-window NDCG* proxy (novel\norganic-engagement targets only, ranked against a sampled candidate set), then\nre-run the negative-sampling sweep above against that signal. Under the current\nselection signal, better next-item models are shipped as worse shelves.\n"
          },
          "result": {
            "timestamp": "2026-09-24T18:16:47.897Z",
            "content": "The file /app/solution/README.md has been updated successfully. (file state is current in your context — no need to Read it back)",
            "isError": false
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          "callId": "toolu_01BLemjhkB4GXM5qQEQonFKp",
          "name": "Bash",
          "input": {
            "command": "ls -l submission_parity.parquet 2>/dev/null; tail -n1 val_graded.log|cut -c1-80; tail -n1 sub_graded.log|cut -c1-80; date",
            "description": "Check run states"
          },
          "result": {
            "timestamp": "2026-09-24T18:16:52.019Z",
            "content": "full-history false-negative filter: users=9,135 unique_user_items=5,524,275 byte\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 byte\nThu Sep 24 18:16:52 UTC 2026",
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          "callId": "toolu_013ahkxkFQHEq8Ahep4LaisC",
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          "input": {
            "command": "sleep 80; ls -l submission_parity.parquet 2>/dev/null && cp submission_parity.parquet submission.parquet && python check_submission.py submission.parquet; date",
            "description": "Install parity submission as safe artifact"
          },
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            "timestamp": "2026-09-24T18:18:17.718Z",
            "content": "Thu Sep 24 18:18:17 UTC 2026",
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          "callId": "toolu_01RzGrm2vXAfobkvhP1B5B5M",
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          "input": {
            "command": "sleep 70; ls -l submission_parity.parquet 2>/dev/null && cp submission_parity.parquet submission.parquet && python check_submission.py submission.parquet; grep -c \"ranked users\" sub_parity.log; date",
            "description": "Install parity submission when ready"
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            "command": "sleep 85; for f in submission_parity val_graded; do ls -l $f.parquet 2>/dev/null; done; date",
            "description": "Poll for artifacts"
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            "timestamp": "2026-09-24T18:21:04.813Z",
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          "callId": "toolu_01JG6e653oqt98fuE58Ra9CA",
          "name": "Bash",
          "input": {
            "command": "sleep 80; ls -l *.parquet | tail -5; grep -c \"ranked\" sub_parity.log val_graded.log sub_graded.log; date",
            "description": "Poll for artifacts"
          },
          "result": {
            "timestamp": "2026-09-24T18:22:29.052Z",
            "content": "-rw-r--r-- 1 agent agent 1175318 Sep 24 17:56 submission.parquet\n-rw-r--r-- 1 agent agent 1249067 Sep 24 18:22 val_graded.parquet\n-rw-r--r-- 1 agent agent 1282634 Sep 24 18:07 val_poolbce.parquet\n-rw-r--r-- 1 agent agent 1218319 Sep 24 18:13 val_poolbce_uniform.parquet\n-rw-r--r-- 1 agent agent 1240261 Sep 24 18:08 val_softmax_clip20.parquet\nval_graded.log:9\nsub_parity.log:0\nsub_graded.log:0\nThu Sep 24 18:22:29 UTC 2026",
            "isError": false
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          "callId": "toolu_016UmkiCB9rswdzUsYjpBPx7",
          "name": "Bash",
          "input": {
            "command": "python /task/evaluate_public.py /app/solution/val_graded.parquet | grep ndcg",
            "description": "Score discovery-graded validation ranking"
          },
          "result": {
            "timestamp": "2026-09-24T18:22:36.791Z",
            "content": "    \"quality_ndcg@10\": 0.023522,",
            "isError": false
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          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:22:45.397Z",
          "text": "Confirmed improvement: **0.02352 vs 0.02128 baseline**. Updating defaults and README while the refit finishes ranking:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:22:48.241Z",
          "callId": "toolu_01CAqoZ1fjCSaMRbkS5qi1Cn",
          "name": "Bash",
          "input": {
            "command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ndef rep(a,b):\n    global s\n    assert s.count(a)>0, a[:40]\n    s=s.replace(a,b,1)\nrep('    negative_objective: str = \"sampled-softmax\"',\n    '    negative_objective: str = \"bce-mixed-negatives\"')\nrep('    target_weighting: str = \"organic-strong\"',\n    '    target_weighting: str = \"discovery-graded\"')\nopen(p,'w').write(s)\nEOF\nsed -i 's/GRADED_SCORE/**0.02352** (+10.5 % relative)/' README.md && grep -n \"0.02352\\|negative_objective: str\\|target_weighting: str\" train_current_model.py README.md | head",
            "description": "Set shipped defaults and README score"
          },
          "result": {
            "timestamp": "2026-09-24T18:22:48.301Z",
            "content": "README.md:81:| shipped (`discovery-graded`, production negatives) | **0.02352** (+10.5 % relative) |\ntrain_current_model.py:61:    negative_objective: str = \"bce-mixed-negatives\"\ntrain_current_model.py:66:    target_weighting: str = \"discovery-graded\"",
            "isError": false
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          "timestamp": "2026-09-24T18:22:48.314Z",
          "data": {
            "type": "edited_text_file",
            "filename": "/app/solution/README.md",
            "snippet": "1\t# Made-for-You two-tower: negative-sampling investigation and objective fix\n2\t\n3\tEverything below was measured with the public backtest (train on `train_*` only,\n4\trank the validation cutoff, score with `/task/evaluate_public.py`). The data\n5\tprotocol, two-tower serving contract (precomputed item vectors, one user vector\n6\tfrom observed history, raw dot product, masked top-100 over the platform\n7\tcandidate set), and the evaluation boundary are unchanged. No cross-encoder, no\n8\tper-candidate sequence model, no user-ID embedding.\n9\t\n10\t## Diagnosis\n11\t\n12\tThe production objective scores each target against 32 negatives: 24 uniform over\n13\tthe 30,000-track catalog plus 8 unique in-batch targets re-weighted by\n14\t`((1/N)/p_i)^0.5` clipped to `[0.25, 4]`, under a sample-weighted BCE. The obvious\n15\treading — \"uniform negatives over a popularity-ordered catalog are too easy and\n16\ttoo few, so the retriever never learns to order the head\" — is **wrong for this\n17\tobjective**, and the backtest says so decisively.\n18\t\n19\tI implemented the standard retrieval upgrade (shared negative pool, sampled\n20\tsoftmax with a Horvitz-Thompson inclusion correction, popularity-tempered\n21\tsampling) and every variant *lost* score, monotonically in how much the negatives\n22\tsuppress popular items:\n23\t\n24\t| objective / negatives | internal chrono. loss | pair acc. | public NDCG@10 |\n25\t| --- | --- | --- | --- |\n26\t| production: 24 uniform + 8 corrected in-batch BCE | 0.4966 (BCE) | 0.755 | **0.02128** |\n27\t| shared pool 2048 BCE, **uniform** pool, no correction | 0.4793 (BCE) | 0.791 | 0.01847 |\n28\t| sampled softmax, pool 2048, 50 % uniform / 50 % `pop^0.75`, HT correction | 9.127 (CE) | 0.714 | 0.01613 |\n29\t| same, harder pool (20 % uniform / `pop^1.0`) | 9.114 (CE) | 0.631 | 0.01615 |\n30\t| same, softer clipping (`--gradient-clip 20`, lr 2e-3) | 9.099 (CE) | 0.719 | 0.01567 |\n31\t| shared pool 2048 BCE, 50 % uniform / 50 % `pop^0.75`, no correction | 0.5243 (BCE) | 0.734 | 0.01017 |\n32\t\n33\tTwo things fall out of that table, and they are the actual diagnosis:\n34\t\n35\t1. **The internal chronological next-item metric is anti-correlated with the\n36\t   discovery objective.** The uniform 2048-negative pool is a strictly\n37\t   lower-variance estimator of the very objective the production trainer\n38\t   optimizes: it improves the internal validation BCE (0.4793 vs 0.4966) and\n39\t   pairwise accuracy (0.791 vs 0.755) — and it *loses* 13 % of NDCG@10. Any\n40\t   negative-sampling change selected on the trainer's own chronological loss will\n41\t   therefore be selected in the wrong direction. This is the limitation that\n42\t   matters: the production negatives are not mis-specified for the shelf, the\n43\t   *model-selection signal* is.\n44\t2. **Harder (popularity-weighted) negatives actively destroy discovery quality.**\n45\t   Ranking only ever happens over candidates the user has *not* encountered, and\n46\t   grades come from organic engagement in the following week, where the item prior\n47\t   is a large part of the signal. Popularity-weighted negatives and the softmax's\n48\t   hardest-negative focus both normalize that prior away: `pop^0.75` negatives cost\n49\t   half the score. Conversely the production in-batch group is not a liability —\n50\t   its `p^-0.5` re-weighting *preserves* the prior by discounting popular\n51\t   negatives, and every variant that dropped that group lost score.\n52\t\n53\tSo the production negative sampler is already close to the right operating point\n54\tfor a next-item-trained retriever serving a discovery shelf, and the exploitable\n55\tmismatch is elsewhere in the objective: the target distribution is dominated by\n56\trepeat plays, which are ineligible at ranking time by construction.\n57\t\n58\t## Change shipped\n59\t\n60\t`--target-weighting discovery-graded` with the production negative sampler\n61\tunchanged. It reweights the per-timestep BCE so the training targets mirror the\n62\tgraded relevance definition instead of raw next-item likelihood:\n63\t\n64\t- a target that is a **repeat** of an item already in the user's sequence keeps\n65\t  only the ordinary weight 0.25 — it can never be an eligible discovery outcome;\n66\t- a **first encounter** the user organically likes within the 7-day discovery\n67\t  horizon (evaluated causally, capped at the chronological split so no future\n68\t  information crosses the boundary) gets weight 3.0, matching `2^3 - 1` grade-3\n69\t  dominance in the metric;\n70\t- other organic first encounters completed to at least 80 % keep weight 1.0.\n71\t\n72\tDiagnostic runs and the shipped artifact are all one epoch, one seed, in the\n73\tsupplied CPU environment; the shipped configuration trains and ranks in about\n74\t5 minutes on 4 threads.\n75\t\n76\t## Public backtest\n77\t\n78\t| model | public NDCG@10 |\n79\t| --- | --- |\n80\t| production baseline (`organic-strong`, 24 uniform + 8 in-batch) | 0.02128 |\n81\t| shipped (`discovery-graded`, production negatives) | **0.02352** (+10.5 % relative) |\n82\t\n83\t## Submission\n84\t\n85\t`/app/solution/submission.parquet` is a refit on `train_* + val_*` at the\n86\thidden-test cutoff, validated with `check_submission.py` (8,456 users x 100 rows,\n87\tranks 1..100 per user, all items inside `test_eligible_candidates.npz`).\n88\t\n89\t```sh\n90\t# public backtest\n91\tpython /app/solution/train_current_model.py --stage validation \\\n92\t  --public-dir /task --prepared-cache /app/solution/cache_val.npz \\\n93\t  --output /app/solution/val_graded.parquet\n94\tpython /task/evaluate_public.py /app/solution/val_graded.parquet\n95\t\n96\t# hidden-test artifact\n97\tpython /app/solution/train_current_model.py --stage submission \\\n98\t  --public-dir /task --prepared-cache /app/solution/cache_sub.npz \\\n99\t  --output /app/solution/submission.parquet\n100\t\n101\tpython /app/solution/check_submission.py /app/solution/submission.parquet\n102\t```\n103\t\n104\tThe negative-sampling experiments above remain reproducible from this file:\n105\t`--negative-objective sampled-softmax|pool-bce|bce-mixed-negatives` with\n106\t`--pool-size`, `--pool-uniform-fraction`, `--pool-popularity-power`,\n107\t`--uniform-negatives`, `--inbatch-negatives`, and `--gradient-clip`.\n108\t\n109\t## If more time were available\n110\t\n111\tThe measured anti-correlation between the trainer's chronological BCE and the\n112\tdiscovery metric is the highest-value follow-up: replace the internal\n113\tearly-stopping signal with a held-out *discovery-window NDCG* proxy (novel\n114\torganic-engagement targets only, ranked against a sampled candidate set), then\n115\tre-run the negative-sampling sweep above against that signal. Under the current\n116\tselection signal, better next-item models are shipped as worse shelves."
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          "input": {
            "command": "sleep 60; ls -l submission_graded.parquet submission_parity.parquet 2>/dev/null; grep -c ranked sub_graded.log sub_parity.log; date",
            "description": "Poll for submission artifacts"
          },
          "result": {
            "timestamp": "2026-09-24T18:23:58.091Z",
            "content": "sub_parity.log:0\nsub_graded.log:0\nThu Sep 24 18:23:58 UTC 2026",
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          "input": {
            "command": "pkill -f \"submission_parity.parquet\"; sleep 2; ps -eo pid,etime,args|grep train_curr|grep -v grep|cut -c1-60; echo killed_parity",
            "description": "Free CPU for the winning refit"
          },
          "result": {
            "timestamp": "2026-09-24T18:24:06.317Z",
            "content": "Exit code 144",
            "isError": true
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:24:06.319Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14859328 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 75,
      "role": "agent",
      "timestamp": "2026-09-24T18:24:09.504Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:24:09.504Z",
          "callId": "toolu_0158rRh59EwwhLZ4Ni4WWEHi",
          "name": "Bash",
          "input": {
            "command": "ps -eo pid,etime,pcpu,args|grep train_curr|grep -v grep|cut -c1-60; date",
            "description": "Check surviving process"
          },
          "result": {
            "timestamp": "2026-09-24T18:24:09.531Z",
            "content": "Thu Sep 24 18:24:09 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:24:09.533Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14859174 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 76,
      "role": "agent",
      "timestamp": "2026-09-24T18:24:18.134Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:24:18.134Z",
          "text": ""
        },
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:24:18.723Z",
          "text": "Both refits were killed by a process-group cleanup. Relaunching the winning config immediately at full thread count:"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:24:20.480Z",
          "callId": "toolu_01PpN6SACNtKdLWvNKQASYjT",
          "name": "Bash",
          "input": {
            "command": "setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 8 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/submission_graded.parquet > sub_graded.log 2>&1 < /dev/null &\nsleep 3; ps -eo pid,args|grep -c \"stage submission\"; date",
            "description": "Relaunch winning submission refit at 8 threads"
          },
          "result": {
            "timestamp": "2026-09-24T18:24:23.503Z",
            "content": "[1]+  Done                    setsid nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 8 --negative-objective bce-mixed-negatives --target-weighting discovery-graded --output /app/solution/submission_graded.parquet > sub_graded.log 2>&1 < /dev/null\n4\nThu Sep 24 18:24:23 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:24:23.505Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14858352 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 77,
      "role": "agent",
      "timestamp": "2026-09-24T18:24:27.289Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:24:27.289Z",
          "callId": "toolu_016DhMqWTSoKnkam6uc3hJRq",
          "name": "Bash",
          "input": {
            "command": "sleep 95; tail -n1 sub_graded.log|cut -c1-90; date",
            "description": "Monitor refit"
          },
          "result": {
            "timestamp": "2026-09-24T18:26:02.309Z",
            "content": "full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,7\nThu Sep 24 18:26:02 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:26:02.315Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14858071 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 78,
      "role": "agent",
      "timestamp": "2026-09-24T18:26:05.425Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:26:05.425Z",
          "callId": "toolu_013SVnpGu8Cv4xpMoWJkjZoF",
          "name": "Bash",
          "input": {
            "command": "sleep 100; grep -E \"train_loss|restored\" sub_graded.log|tail -n1|cut -c1-90; date",
            "description": "Monitor refit training"
          },
          "result": {
            "timestamp": "2026-09-24T18:27:45.462Z",
            "content": "Thu Sep 24 18:27:45 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:27:45.464Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14857867 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 79,
      "role": "agent",
      "timestamp": "2026-09-24T18:27:51.988Z",
      "blocks": [
        {
          "kind": "reasoning",
          "timestamp": "2026-09-24T18:27:51.988Z",
          "text": ""
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:27:52.473Z",
          "callId": "toolu_014S815aYJnTbLAUTaLvobtP",
          "name": "Bash",
          "input": {
            "command": "sleep 95; (grep -E \"train_loss\" sub_graded.log|tail -n1|cut -c1-80) ; date; true",
            "description": "Monitor refit training"
          },
          "result": {
            "timestamp": "2026-09-24T18:29:27.541Z",
            "content": "epoch=1/1 train_loss=0.54085 validation_loss=0.51262 pair_accuracy=0.762 train_t\nThu Sep 24 18:29:27 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:29:27.546Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14857469 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 80,
      "role": "agent",
      "timestamp": "2026-09-24T18:29:31.023Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:29:31.023Z",
          "callId": "toolu_017rUkqrRuJYGaHu6tewmcmv",
          "name": "Bash",
          "input": {
            "command": "sleep 85; ls -l submission_graded.parquet 2>/dev/null; (grep -c ranked sub_graded.log); date; true",
            "description": "Poll for refit artifact"
          },
          "result": {
            "timestamp": "2026-09-24T18:30:56.055Z",
            "content": "-rw-r--r-- 1 agent agent 1200187 Sep 24 18:30 submission_graded.parquet\n9\nThu Sep 24 18:30:56 UTC 2026",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:30:56.056Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14857252 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 81,
      "role": "agent",
      "timestamp": "2026-09-24T18:30:59.977Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:30:59.977Z",
          "callId": "toolu_0188Q9mc5fV8LbzEhAdNS2HF",
          "name": "Bash",
          "input": {
            "command": "cp submission_graded.parquet submission.parquet && python check_submission.py submission.parquet; ls -l submission.parquet; true",
            "description": "Install and validate final submission"
          },
          "result": {
            "timestamp": "2026-09-24T18:31:02.809Z",
            "content": "rows=845,600 users=8,456 status=OK\n-rw-r--r-- 1 agent agent 1200187 Sep 24 18:30 submission.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:31:02.811Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14857041 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 82,
      "role": "agent",
      "timestamp": "2026-09-24T18:31:06.040Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-24T18:31:06.040Z",
          "callId": "toolu_01CvXu23wudSA3Hz5Qe6piMx",
          "name": "Bash",
          "input": {
            "command": "rm -f x.parquet y.parquet smoke.parquet smoke.log; ls -l /app/solution | grep -v cache_; true",
            "description": "Clean scratch files and list deliverables"
          },
          "result": {
            "timestamp": "2026-09-24T18:31:06.076Z",
            "content": "total 1073480\n-rw-r--r-- 1 agent agent      6297 Sep 24 18:22 README.md\n-rw-r--r-- 1 agent agent      3945 Sep 24 18:00 base_val.log\n-rw-r--r-- 1 agent agent      2735 Sep 24 17:56 check_submission.py\n-rw-r--r-- 1 agent agent      4239 Sep 24 17:56 improved_sub.log\n-rw-r--r-- 1 agent agent      4250 Sep 24 17:50 improved_val.log\n-rw-r--r-- 1 agent agent      4247 Sep 24 18:01 improved_val_hard.log\n-rw-r--r-- 1 agent agent   1217436 Sep 24 17:50 improved_validation.parquet\n-rw-r--r-- 1 agent agent   1237468 Sep 24 18:01 improved_validation_hard.parquet\n-rw-r--r-- 1 agent agent      2162 Sep 24 17:39 prep_sub.log\n-rw-r--r-- 1 agent agent      2162 Sep 24 17:39 prep_val.log\n-rw-r--r-- 1 agent agent   1244230 Sep 24 18:00 production_validation.parquet\n-rw-r--r-- 1 agent agent      4354 Sep 24 18:30 sub_graded.log\n-rw-r--r-- 1 agent agent       843 Sep 24 18:14 sub_parity.log\n-rw-r--r-- 1 agent agent   1200187 Sep 24 18:30 submission.parquet\n-rw-r--r-- 1 agent agent   1200187 Sep 24 18:30 submission_graded.parquet\n-rw-r--r-- 1 agent agent     93806 Sep 24 18:22 train_current_model.py\n-rw-r--r-- 1 agent agent     15985 Sep 24 17:37 two_tower_model.py\n-rw-r--r-- 1 agent agent      4337 Sep 24 18:22 val_graded.log\n-rw-r--r-- 1 agent agent   1249067 Sep 24 18:22 val_graded.parquet\n-rw-r--r-- 1 agent agent      4207 Sep 24 18:07 val_poolbce.log\n-rw-r--r-- 1 agent agent   1282634 Sep 24 18:07 val_poolbce.parquet\n-rw-r--r-- 1 agent agent      4216 Sep 24 18:13 val_poolbce_uniform.log\n-rw-r--r-- 1 agent agent   1218319 Sep 24 18:13 val_poolbce_uniform.parquet\n-rw-r--r-- 1 agent agent      4245 Sep 24 18:08 val_softmax_clip20.log\n-rw-r--r-- 1 agent agent   1240261 Sep 24 18:08 val_softmax_clip20.parquet",
            "isError": false
          }
        },
        {
          "kind": "context",
          "timestamp": "2026-09-24T18:31:06.077Z",
          "data": {
            "type": "total_tokens_reminder",
            "text": "<total_tokens>14856832 tokens left</total_tokens>"
          }
        }
      ]
    },
    {
      "id": 83,
      "role": "agent",
      "timestamp": "2026-09-24T18:31:22.173Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-24T18:31:22.173Z",
          "text": "Done. Final state in `/app/solution/`:\n\n**Submission**: `submission.parquet` — regular file, 845,600 rows, 8,456 users × 100 ranks, all items eligible at the test cutoff (verified with `check_submission.py`). Refit on `train_* + val_*` at the hidden-test cutoff.\n\n**What I found** (all public-backtest NDCG@10, baseline reproduced at **0.02128**):\n\n| config | NDCG@10 |\n|---|---|\n| production (24 uniform + 8 corrected in-batch BCE) | 0.02128 |\n| shared pool 2048, uniform, BCE | 0.01847 |\n| sampled softmax, pool 2048, 50 % `pop^0.75`, HT logQ correction | 0.01613 |\n| same, harder pool (`pop^1.0`) / softer clipping | 0.01615 / 0.01567 |\n| shared pool 2048, `pop^0.75`, no correction | 0.01017 |\n| **shipped: production negatives + `discovery-graded` weighting** | **0.02352** |\n\nThe expected fix — replacing 32 easy negatives with a large shared pool and a sampled softmax with inclusion (logQ) correction — is implemented and it *loses*, monotonically in how much the negatives suppress popular items. The diagnostic value is in the second row: a uniform 2048-negative pool is a lower-variance estimator of exactly the production objective, and it improves the trainer's own chronological validation BCE (0.4793 vs 0.4966) and pairwise accuracy (0.791 vs 0.755) while dropping NDCG 13 %. The trainer's internal selection signal is anti-correlated with the discovery metric, so negative-sampling changes tuned on it move the wrong way; and because ranking only ever covers *unencountered* candidates graded by next-week organic engagement, hard/popularity-weighted negatives normalize away an item prior the metric rewards.\n\nWhat shipped is the objective change the evidence supports: `--target-weighting discovery-graded` (production negatives untouched) — repeat targets drop to weight 0.25 since they can never be eligible discovery outcomes, causally-verified organic likes within the 7-day horizon get 3.0, mirroring the grade-3 gain. +10.5 % relative on the public backtest.\n\nTwo caveats worth flagging: each number is one epoch / one seed (no variance estimate), and the shipped win comes from target weighting rather than the negative sampler I was pointed at — the negative-sampling work is a documented negative result, reproducible from the new `--negative-objective`, `--pool-*`, `--uniform-negatives`, `--inbatch-negatives`, and `--gradient-clip` flags. `README.md` records the diagnosis, the table, the shipped change, and the commands; the highest-value follow-up (replacing the internal early-stopping signal with a discovery-window NDCG proxy, then re-running the negative sweep) is noted there too."
        }
      ]
    }
  ]
}