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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. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch",
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"result": {
"timestamp": "2026-09-24T17:36:50.341Z",
"content": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )",
"isError": false
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"timestamp": "2026-09-24T17:36:51.833Z",
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{
"kind": "reasoning",
"timestamp": "2026-09-24T17:36:51.833Z",
"text": ""
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"timestamp": "2026-09-24T17:36:52.895Z",
"callId": "toolu_01XoavTCYg7DbBGabD7gZFKs",
"name": "Bash",
"input": {
"command": "grep -n \"^def \\|^class \\|^# ---\\|^ def \" /task/current_model/train_current_model.py | head -120",
"description": "Outline trainer"
},
"result": {
"timestamp": "2026-09-24T17:36:52.914Z",
"content": "41:class Config:\n76:class CandidateSet:\n82: def read(cls, path: Path) -> \"CandidateSet\":\n99:class SequenceData:\n118: def lengths(self) -> np.ndarray:\n122: def target_count(self) -> int:\n127:class TargetPartition:\n136: def selected_count(self) -> int:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n609:def invalid_full_history_negatives(\n630:def draw_full_history_filtered_negatives(\n724:def draw_training_negatives(\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n828:def history_windows(\n845:def corrected_negative_loss(\n867:def retained_target_window_starts(\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1364:def evaluate_validation_bce(\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:",
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"name": "Bash",
"input": {
"command": "grep -n \"^def \\|^class \\|^ def \" /task/current_model/two_tower_model.py",
"description": "Outline model"
},
"result": {
"timestamp": "2026-09-24T17:36:53.595Z",
"content": "27:class TowerConfig:\n49:class ListenEvents:\n57:class ExplicitEvents:\n65:def log(message: str) -> None:\n69:def build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n90:def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n106:def iter_listen_users(\n157:def merge_listen_streams(\n175:def load_explicit_events(\n228:def empty_explicit_events() -> ExplicitEvents:\n238:def pack_active_items(\n251:def load_inference_state(\n284:class SequentialTwoTower(nn.Module):\n287: def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n332: def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n339: def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n346:def invalid_negative_mask(\n363:def draw_filtered_mixed_negatives(",
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"id": 5,
"role": "agent",
"timestamp": "2026-09-24T17:36:55.492Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:36:55.492Z",
"callId": "toolu_01Ut6xgPjiZqCnMYSyCzQEwf",
"name": "Bash",
"input": {
"command": "sed -n 1,180p /task/current_model/train_current_model.py",
"description": "Read config"
},
"result": {
"timestamp": "2026-09-24T17:36:55.506Z",
"content": "#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss. The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split. A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n bptt_steps: int = 100\n user_batch_size: int = 64\n embedding_dim: int = 192\n hidden_dim: int = 192\n user_hidden_1: int = 512\n user_hidden_2: int = 256\n item_hidden_1: int = 512\n item_hidden_2: int = 256\n dropout: float = 0.05\n epochs: int = 1\n learning_rate: float = 1.0e-3\n weight_decay: float = 1.0e-5\n gradient_clip: float = 2.0\n loss_microbatch_size: int = 65_536\n uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n target_weighting: str = \"organic-strong\"\n ordinary_target_weight: float = 0.25\n organic_strong_target_weight: float = 1.0\n organic_like_target_weight: float = 3.0\n discovery_horizon_days: int = 7\n validation_fraction: float = 0.10\n max_train_targets_per_user: int = 128\n max_validation_targets_per_user: int = 16\n early_stopping_patience: int = 1\n early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n uids: np.ndarray\n item_ids: np.ndarray\n packed: np.ndarray\n\n @classmethod\n def read(cls, path: Path) -> \"CandidateSet\":\n with np.load(path, allow_pickle=False) as artifact:\n result = cls(\n uids=artifact[\"uids\"].astype(np.int64, copy=True),\n item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n )\n expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n if result.packed.shape != expected:\n raise ValueError(\n f\"invalid candidate artifact shape {result.packed.shape}; \"\n f\"expected {expected}\"\n )\n return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n item_ids: np.ndarray\n item_features: np.ndarray\n user_ids: np.ndarray\n offsets: np.ndarray\n sequence_timestamps: np.ndarray\n sequence_items: np.ndarray\n event_features: np.ndarray\n activity_features: np.ndarray\n state_ids: np.ndarray\n liked_states: np.ndarray\n disliked_states: np.ndarray\n state_counts: np.ndarray\n inference_user_rows: np.ndarray\n inference_liked_items: np.ndarray\n inference_disliked_items: np.ndarray\n inference_user_features: np.ndarray\n\n @property\n def lengths(self) -> np.ndarray:\n return np.diff(self.offsets)\n\n @property\n def target_count(self) -> int:\n return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n window_starts: np.ndarray\n training_counts: np.ndarray\n validation_counts: np.ndarray\n history_training_counts: np.ndarray\n\n @property\n def selected_count(self) -> int:\n return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n return base.TowerConfig(\n sequence_length=config.bptt_steps,\n embedding_dim=config.embedding_dim,\n hidden_dim=config.hidden_dim,\n user_hidden_1=config.user_hidden_1,\n user_hidden_2=config.user_hidden_2,\n item_hidden_1=config.item_hidden_1,\n item_hidden_2=config.item_hidden_2,\n dropout=config.dropout,\n epochs=config.epochs,\n learning_rate=config.learning_rate,\n weight_decay=config.weight_decay,\n gradient_clip=config.gradient_clip,\n uniform_negatives=config.uniform_negatives,\n inbatch_negatives=config.inbatch_negatives,\n inbatch_correction_power=config.inbatch_correction_power,\n inbatch_correction_min=config.inbatch_correction_min,\n inbatch_correction_max=config.inbatch_correction_max,\n seed=config.seed,\n )\n\n\ndef append_state_snapshot(\n active_likes: dict[int, int],\n active_dislikes: dict[int, int],\n n_items: int,\n liked_states: list[np.ndarray],\n disliked_states: list[np.ndarray],\n state_counts: list[tuple[int, int]],",
"isError": false
}
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{
"kind": "tool",
"timestamp": "2026-09-24T17:36:56.060Z",
"callId": "toolu_01WpBoyCL4EV5N4hx9gF3DV8",
"name": "Bash",
"input": {
"command": "sed -n 280,433p /task/current_model/two_tower_model.py",
"description": "Read model class"
},
"result": {
"timestamp": "2026-09-24T17:36:56.075Z",
"content": " destination[row, : len(retained)] = retained.astype(np.uint16)\n return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n super().__init__()\n n_items, n_item_features = item_features.shape\n dim = config.embedding_dim\n self.n_items = n_items\n self.context_embedding = nn.Embedding(\n n_items + 1, dim, padding_idx=n_items\n )\n self.event_projection = nn.Sequential(\n nn.Linear(dim + 3, config.hidden_dim),\n nn.GELU(),\n )\n self.sequence_encoder = nn.GRU(\n input_size=config.hidden_dim,\n hidden_size=dim,\n num_layers=1,\n batch_first=True,\n )\n self.user_tower = nn.Sequential(\n nn.Linear(dim * 3 + 6, config.user_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_1, config.user_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.user_hidden_2, dim),\n )\n self.candidate_embedding = nn.Embedding(n_items, dim)\n self.register_buffer(\n \"item_features\", torch.from_numpy(item_features), persistent=True\n )\n self.item_tower = nn.Sequential(\n nn.Linear(dim + n_item_features, config.item_hidden_1),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_1, config.item_hidden_2),\n nn.GELU(),\n nn.Dropout(config.dropout),\n nn.Linear(config.item_hidden_2, dim),\n )\n nn.init.normal_(self.context_embedding.weight, std=0.02)\n nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n with torch.no_grad():\n self.context_embedding.weight[n_items].zero_()\n\n def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n mask = items.ne(self.n_items)\n vectors = self.context_embedding(items)\n return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n dim=1, keepdim=True\n ).clamp_min(1)\n\n def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n inputs = torch.cat(\n [self.candidate_embedding(items), self.item_features[items]], dim=1\n )\n return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n negatives: torch.Tensor,\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n) -> torch.Tensor:\n \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n invalid = negatives.eq(targets[:, None])\n invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= negatives[:, column, None].eq(\n negatives[:, :column]\n ).any(dim=1)\n return invalid\n\n\ndef draw_filtered_mixed_negatives(\n targets: torch.Tensor,\n context_items: torch.Tensor,\n liked_items: torch.Tensor,\n n_items: int,\n uniform_count: int,\n inbatch_count: int,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n batch_size = len(targets)\n uniform = torch.randint(\n n_items,\n (batch_size, uniform_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n corrected_count = 0\n if inbatch_count:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n -1.0,\n )\n columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n inbatch = unique_targets[columns]\n if corrected_count < inbatch_count:\n filler = torch.randint(\n n_items,\n (batch_size, inbatch_count - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n if corrected_count:\n correction_cpu[\n :, uniform_count : uniform_count + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]]\n negatives = negatives_cpu.to(device)\n correction = correction_cpu.to(device)\n for _ in range(32):\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if not bool(invalid.any()):\n return negatives, correction\n replacements = torch.randint(\n n_items,\n (int(invalid.sum().detach().cpu()),),\n generator=generator,\n device=\"cpu\",\n ).to(device)\n negatives[invalid] = replacements\n correction[invalid] = 1.0\n invalid = invalid_negative_mask(\n negatives, targets, context_items, liked_items\n )\n if bool(invalid.any()):\n raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n return negatives, correction",
"isError": false
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},
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{
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"timestamp": "2026-09-24T17:36:58.382Z",
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{
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"timestamp": "2026-09-24T17:36:58.382Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:00.037Z",
"callId": "toolu_012GcqSQNy49vXoLq6hiD8n3",
"name": "Bash",
"input": {
"command": "cd /app/solution && chmod u+w *.py && nohup python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet > /app/solution/baseline.log 2>&1 &\necho started",
"description": "Start baseline run",
"run_in_background": false
},
"result": {
"timestamp": "2026-09-24T17:37:00.057Z",
"content": "started",
"isError": false
}
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:00.743Z",
"callId": "toolu_014npcZ3NyYXQhx82DtD6W9P",
"name": "Bash",
"input": {
"command": "sed -n 1922,2164p /task/current_model/train_current_model.py",
"description": "Read main"
},
"result": {
"timestamp": "2026-09-24T17:37:00.758Z",
"content": "def main() -> int:\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\n \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n )\n parser.add_argument(\"--output\", type=Path, required=True)\n parser.add_argument(\"--public-dir\", type=Path, required=True)\n parser.add_argument(\"--prepared-cache\", type=Path)\n parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n parser.add_argument(\"--threads\", type=int, default=8)\n parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n parser.add_argument(\n \"--user-batch-size\", type=int, default=Config.user_batch_size\n )\n parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n parser.add_argument(\n \"--loss-microbatch-size\",\n type=int,\n default=Config.loss_microbatch_size,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\n choices=(\"full-training-history\", \"last-100\"),\n default=Config.negative_filter_scope,\n )\n parser.add_argument(\n \"--target-weighting\",\n choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n default=Config.target_weighting,\n )\n parser.add_argument(\n \"--ordinary-target-weight\",\n type=float,\n default=Config.ordinary_target_weight,\n )\n parser.add_argument(\n \"--organic-strong-target-weight\",\n type=float,\n default=Config.organic_strong_target_weight,\n )\n parser.add_argument(\n \"--organic-like-target-weight\",\n type=float,\n default=Config.organic_like_target_weight,\n )\n parser.add_argument(\n \"--discovery-horizon-days\",\n type=int,\n default=Config.discovery_horizon_days,\n )\n parser.add_argument(\n \"--validation-fraction\",\n type=float,\n default=Config.validation_fraction,\n )\n parser.add_argument(\n \"--max-train-targets-per-user\",\n type=int,\n default=Config.max_train_targets_per_user,\n help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--max-validation-targets-per-user\",\n type=int,\n default=Config.max_validation_targets_per_user,\n help=\"retain this many final validation targets per user; 0 is uncapped\",\n )\n parser.add_argument(\n \"--early-stopping-patience\",\n type=int,\n default=Config.early_stopping_patience,\n )\n parser.add_argument(\n \"--early-stopping-min-delta\",\n type=float,\n default=Config.early_stopping_min_delta,\n )\n parser.add_argument(\"--checkpoint-dir\", type=Path)\n parser.add_argument(\"--resume-checkpoint\", type=Path)\n parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n parser.add_argument(\"--prepare-only\", action=\"store_true\")\n args = parser.parse_args()\n\n if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n if (\n args.epochs < 1\n or args.learning_rate <= 0\n or args.loss_microbatch_size < 1\n or args.early_stopping_patience < 1\n or args.early_stopping_min_delta < 0\n or args.ordinary_target_weight <= 0\n or args.organic_strong_target_weight <= 0\n or args.organic_like_target_weight <= 0\n or args.discovery_horizon_days < 1\n or args.max_train_targets_per_user < 0\n or args.max_validation_targets_per_user < 0\n ):\n parser.error(\"training counts, learning rate, and patience are invalid\")\n if not 0.0 < args.validation_fraction < 1.0:\n parser.error(\"validation fraction must be strictly between zero and one\")\n public = args.public_dir.resolve()\n if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n parser.error(f\"public task data not found under {public}\")\n config = Config(\n bptt_steps=args.bptt_steps,\n user_batch_size=args.user_batch_size,\n epochs=args.epochs,\n learning_rate=args.learning_rate,\n loss_microbatch_size=args.loss_microbatch_size,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\n ordinary_target_weight=args.ordinary_target_weight,\n organic_strong_target_weight=args.organic_strong_target_weight,\n organic_like_target_weight=args.organic_like_target_weight,\n discovery_horizon_days=args.discovery_horizon_days,\n validation_fraction=args.validation_fraction,\n max_train_targets_per_user=args.max_train_targets_per_user,\n max_validation_targets_per_user=args.max_validation_targets_per_user,\n early_stopping_patience=args.early_stopping_patience,\n early_stopping_min_delta=args.early_stopping_min_delta,\n seed=args.seed,\n )\n torch.set_num_threads(args.threads)\n torch.set_num_interop_threads(1)\n seed_everything(config.seed)\n device = torch.device(args.device)\n if device.type == \"mps\" and not torch.backends.mps.is_available():\n parser.error(\"MPS was requested but is unavailable\")\n\n started = time.monotonic()\n prepared, eligibility = load_or_prepare(\n public,\n args.stage,\n config,\n args.prepared_cache.resolve() if args.prepared_cache else None,\n )\n target_partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n if args.prepare_only:\n print(\n json.dumps(\n {\n \"stage\": args.stage,\n \"sequence_users\": len(prepared.user_ids),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(\n target_partition.window_starts.sum()\n ),\n \"target_users\": len(eligibility.uids),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"config\": asdict(config),\n },\n indent=2,\n )\n )\n return 0\n\n model, training_history = train_model(\n public,\n args.stage,\n prepared,\n config,\n device,\n args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n )\n ranking, embedding_diagnostics = rank_candidates(\n model,\n prepared,\n eligibility,\n config,\n device,\n args.ranking_batch_size,\n )\n write_ranking(ranking, args.output.resolve())\n print(\n json.dumps(\n {\n \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n \"stage\": args.stage,\n \"output\": str(args.output.resolve()),\n \"rows\": len(ranking),\n \"users\": int(ranking.uid.nunique()),\n \"runtime_seconds\": round(time.monotonic() - started, 3),\n \"public_inputs_only\": True,\n \"architecture\": {\n \"family\": \"pure-windowed-sequence-two-tower\",\n \"sequence_training\": (\n \"every-adjacent-target-within-recent-chronological-window\"\n ),\n \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n \"bptt_hidden_state\": \"carried-and-detached\",\n \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n \"loss_execution\": \"gradient-accumulated-microbatches\",\n \"user_id_embedding\": False,\n \"user_mlp\": \"582-512-256-192\",\n \"item_mlp\": \"196-512-256-192\",\n \"normalization\": \"none\",\n \"score\": \"raw-dot-product\",\n \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),\n \"false_negative_filter\": config.negative_filter_scope,\n \"target_weighting\": config.target_weighting,\n },\n \"config\": asdict(config),\n \"sequence_tokens\": len(prepared.sequence_items),\n \"all_next_item_targets\": prepared.target_count,\n \"selected_training_targets\": int(\n target_partition.training_counts.sum()\n ),\n \"selected_validation_targets\": int(\n target_partition.validation_counts.sum()\n ),\n \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n \"training_history\": training_history,\n \"embedding_diagnostics\": embedding_diagnostics,\n \"torch_version\": torch.__version__,\n \"device\": str(device),\n },\n indent=2,\n )\n )\n return 0\n\n\nif __name__ == \"__main__\":\n raise SystemExit(main())",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:37:00.760Z",
"data": {
"type": "total_tokens_reminder",
"text": "14971150 tokens left"
}
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-24T17:37:02.339Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:37:02.339Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:03.064Z",
"callId": "toolu_0197EZK253whiRkcVeLRj7Sn",
"name": "Bash",
"input": {
"command": "sed -n 534,900p /task/current_model/train_current_model.py",
"description": "Read negative sampling"
},
"result": {
"timestamp": "2026-09-24T17:37:03.078Z",
"content": "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)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n target_start = int(target_starts[user_row])\n counts += np.bincount(\n prepared.sequence_items[\n offset\n + target_start\n + 1 : offset\n + target_start\n + int(count)\n + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())\n probabilities = (counts + 1.0) / (total + len(counts))\n raw = np.power(\n (1.0 / len(counts)) / probabilities,\n config.inbatch_correction_power,\n )\n normalization = float(np.sum(counts * raw) / max(total, 1.0))\n correction = np.clip(\n raw / max(normalization, 1.0e-12),\n config.inbatch_correction_min,\n config.inbatch_correction_max,\n ).astype(np.float32)\n mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n log(\n f\"inbatch correction: min={correction.min():.3f} \"\n f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n )\n return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n width = (len(prepared.item_ids) + 7) // 8\n packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n retained = 0\n for user_row, target_count in enumerate(training_counts):\n if not target_count:\n continue\n offset = int(prepared.offsets[user_row])\n # T adjacent targets consume sequence positions 0 through T. The\n # chronological BCE suffix starts at T + 1 and is intentionally absent.\n items = np.unique(\n prepared.sequence_items[\n offset : offset + int(target_count) + 1\n ].astype(np.int64)\n )\n byte_columns = items >> 3\n bit_values = np.left_shift(\n np.uint8(1), (items & 7).astype(np.uint8)\n )\n np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n retained += len(items)\n log(\n f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n )\n return packed\n\n\ndef invalid_full_history_negatives(\n negatives: np.ndarray,\n targets: np.ndarray,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray,\n liked_items: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n byte_columns = negatives >> 3\n bit_columns = negatives & 7\n known = known_positive_packed[user_rows[:, None], byte_columns]\n invalid = ((known >> bit_columns) & 1).astype(bool)\n invalid |= negatives == targets[:, None]\n invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n for column in range(1, negatives.shape[1]):\n invalid[:, column] |= (\n negatives[:, column, None] == negatives[:, :column]\n ).any(axis=1)\n return invalid\n\n\ndef draw_full_history_filtered_negatives(\n targets: torch.Tensor,\n user_rows: np.ndarray,\n liked_items: torch.Tensor,\n known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n corrected_count = 0\n if config.inbatch_negatives:\n unique_targets = torch.unique(targets.detach().cpu())\n corrected_count = min(\n config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n )\n if corrected_count:\n priorities = torch.rand(\n (batch_size, len(unique_targets)), generator=generator\n )\n priorities.masked_fill_(\n unique_targets[None, :].eq(\n torch.from_numpy(targets_cpu)[:, None]\n ),\n -1.0,\n )\n columns = torch.topk(\n priorities, k=corrected_count, dim=1\n ).indices\n inbatch = unique_targets[columns]\n if corrected_count < config.inbatch_negatives:\n filler = torch.randint(\n n_items,\n (batch_size, config.inbatch_negatives - corrected_count),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.cat([inbatch, filler], dim=1)\n\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()\n for _ in range(32):\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if not np.any(invalid):\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n replacements = torch.randint(\n n_items,\n (int(invalid.sum()),),\n generator=generator,\n device=\"cpu\",\n ).numpy()\n negatives[invalid] = replacements\n # Rejected in-batch items become ordinary uniform draws.\n correction[invalid] = 1.0\n invalid = invalid_full_history_negatives(\n negatives,\n targets_cpu,\n user_rows,\n known_positive_packed,\n liked_cpu,\n )\n if np.any(invalid):\n raise RuntimeError(\"full-history negative resampling failed\")\n return (\n torch.from_numpy(negatives).to(device),\n torch.from_numpy(correction).to(device),\n )\n\n\ndef draw_training_negatives(\n positive_items: torch.Tensor,\n known_history: torch.Tensor,\n liked_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n prepared: SequenceData,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(\n positive_items,\n known_history,\n liked_items,\n len(prepared.item_ids),\n config.uniform_negatives,\n config.inbatch_negatives,\n item_correction,\n generator,\n device,\n )\n\n\ndef event_sequence(\n model: base.SequentialTwoTower,\n items: torch.Tensor,\n features: torch.Tensor,\n hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n projected = model.event_projection(inputs)\n return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n state_ids: np.ndarray,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n unique_states, inverse = np.unique(state_ids, return_inverse=True)\n liked_items = torch.from_numpy(\n prepared.liked_states[unique_states].astype(np.int64)\n ).to(device)\n disliked_items = torch.from_numpy(\n prepared.disliked_states[unique_states].astype(np.int64)\n ).to(device)\n liked_vectors = model._mean_context_channel(liked_items)\n disliked_vectors = model._mean_context_channel(disliked_items)\n inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n counts = prepared.state_counts[state_ids]\n return (\n liked_vectors[inverse_rows],\n disliked_vectors[inverse_rows],\n torch.from_numpy(counts.astype(np.float32)).to(device),\n )\n\n\ndef user_queries(\n model: base.SequentialTwoTower,\n sequence_vectors: torch.Tensor,\n liked_vectors: torch.Tensor,\n disliked_vectors: torch.Tensor,\n user_features: torch.Tensor,\n) -> torch.Tensor:\n return model.user_tower(\n torch.cat(\n [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n dim=1,\n )\n )\n\n\ndef candidate_vectors(\n model: base.SequentialTwoTower,\n positives: torch.Tensor,\n negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n all_items = torch.cat([positives, negatives.reshape(-1)])\n unique_items, inverse = torch.unique(all_items, return_inverse=True)\n encoded = model.encode_items(unique_items)\n positive_vectors = encoded[inverse[: len(positives)]]\n negative_vectors = encoded[inverse[len(positives) :]].reshape(\n len(positives), negatives.shape[1], -1\n )\n return positive_vectors, negative_vectors\n\n\ndef history_windows(\n sequence: np.ndarray,\n start: int,\n count: int,\n width: int,\n padding_item: int,\n) -> np.ndarray:\n base_start = max(0, start - width + 1)\n segment = sequence[base_start : start + count]\n padded = np.concatenate(\n [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n )\n windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n offset = start - base_start\n return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total\n\n\ndef retained_target_window_starts(\n total_targets: np.ndarray,\n validation_counts: np.ndarray,\n training_counts: np.ndarray,\n) -> np.ndarray:\n \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n training_region_ends = total_targets - validation_counts\n window_starts = training_region_ends - training_counts\n if (\n np.any(window_starts < 0)\n or np.any(training_counts < 0)\n or np.any(validation_counts < 0)\n ):\n raise RuntimeError(\"chronological target partition produced negative counts\")\n if not np.array_equal(\n window_starts + training_counts + validation_counts, total_targets\n ):\n raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n return window_starts\n\n\ndef chronological_target_partition(\n prepared: SequenceData,\n validation_fraction: float,\n max_train_targets_per_user: int,\n max_validation_targets_per_user: int,\n) -> TargetPartition:\n \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n validation = np.floor(totals * validation_fraction).astype(np.int64)\n validation[(totals >= 2) & (validation == 0)] = 1\n if max_validation_targets_per_user:\n validation = np.minimum(validation, max_validation_targets_per_user)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:37:03.080Z",
"data": {
"type": "total_tokens_reminder",
"text": "14967556 tokens left"
}
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-24T17:37:09.423Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:37:09.423Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:10.309Z",
"callId": "toolu_01PKbkyKNMPmj5c5xVCPipNu",
"name": "Bash",
"input": {
"command": "sed -n 1035,1200p /task/current_model/train_current_model.py",
"description": "Read dense block"
},
"result": {
"timestamp": "2026-09-24T17:37:10.323Z",
"content": "def build_dense_block(\n prepared: SequenceData,\n target_weights: np.ndarray,\n users: np.ndarray,\n window_starts: np.ndarray,\n process_counts: np.ndarray,\n score_starts: np.ndarray,\n start: int,\n steps: int,\n config: Config,\n) -> tuple[np.ndarray, ...]:\n \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n batch_size = len(users)\n padding_item = len(prepared.item_ids)\n input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n targets = np.zeros((batch_size, steps), dtype=np.uint16)\n weights = np.ones((batch_size, steps), dtype=np.float32)\n scored = np.zeros((batch_size, steps), dtype=bool)\n histories = np.full(\n (batch_size, steps, config.bptt_steps),\n padding_item,\n dtype=np.uint16,\n )\n processed = np.zeros(batch_size, dtype=np.int32)\n\n lengths = prepared.lengths\n for row, user_row in enumerate(users):\n count = min(steps, max(int(process_counts[row]) - start, 0))\n if not count:\n continue\n offset = int(prepared.offsets[user_row])\n window_start = int(window_starts[row])\n absolute_start = window_start + start\n positions = slice(\n offset + absolute_start,\n offset + absolute_start + count,\n )\n target_positions = slice(\n offset + absolute_start + 1,\n offset + absolute_start + count + 1,\n )\n input_items[row, :count] = prepared.sequence_items[positions]\n event_features[row, :count] = prepared.event_features[positions]\n activity_features[row, :count] = prepared.activity_features[positions]\n state_ids[row, :count] = prepared.state_ids[positions]\n targets[row, :count] = prepared.sequence_items[target_positions]\n weights[row, :count] = target_weights[target_positions]\n score_from = min(count, max(int(score_starts[row]) - start, 0))\n scored[row, score_from:count] = True\n processed[row] = count\n user_sequence = prepared.sequence_items[\n offset : offset + int(lengths[user_row])\n ]\n histories[row, :count] = history_windows(\n user_sequence,\n absolute_start,\n count,\n config.bptt_steps,\n padding_item,\n )\n return (\n input_items,\n event_features,\n activity_features,\n state_ids,\n targets,\n scored,\n histories,\n processed,\n weights,\n )\n\n\ndef score_target_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 negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\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 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 positive_vectors, negative_vectors = candidate_vectors(\n model,\n positive_items[begin:end],\n negatives[begin:end],\n )\n positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n negative_logits = torch.einsum(\n \"bd,bkd->bk\", query_vectors, negative_vectors\n )\n positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()\n if not bool(torch.isfinite(loss_sum)):\n raise RuntimeError(\"dense next-item 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 statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n statistics[\"pair_correct\"] += float(\n (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n )\n statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n if device.type == \"mps\":\n torch.mps.empty_cache()",
"isError": false
}
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:10.902Z",
"callId": "toolu_01C2dPaoLmEpyAshuJAD3R6z",
"name": "Bash",
"input": {
"command": "tail -5 /app/solution/baseline.log",
"description": "Check baseline log"
},
"result": {
"timestamp": "2026-09-24T17:37:10.914Z",
"content": "loaded explicit events: rows=720,270 users=8,220",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:37:10.915Z",
"data": {
"type": "total_tokens_reminder",
"text": "14962018 tokens left"
}
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-24T17:37:12.524Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:37:12.524Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:13.201Z",
"callId": "toolu_01HcSnhfL4qbAEVcUjwDwd9H",
"name": "Bash",
"input": {
"command": "sed -n 1203,1370p /task/current_model/train_current_model.py",
"description": "Read training epoch"
},
"result": {
"timestamp": "2026-09-24T17:37:13.220Z",
"content": "def run_training_epoch(\n model: base.SequentialTwoTower,\n optimizer: torch.optim.Optimizer,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n target_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:\n model.train()\n train_users = np.flatnonzero(target_counts > 0)\n sorted_users = train_users[\n np.argsort(target_counts[train_users], kind=\"stable\")\n ]\n groups = [\n sorted_users[start : start + config.user_batch_size]\n for start in range(0, len(sorted_users), config.user_batch_size)\n ]\n group_order = torch.randperm(len(groups), generator=generator).numpy()\n negative_count = config.uniform_negatives + config.inbatch_negatives\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n total_positive = 0.0\n total_negative = 0.0\n total_pair_correct = 0.0\n batches = 0\n started = time.monotonic()\n\n for group_number in group_order:\n users = groups[int(group_number)]\n group_counts = target_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_counts.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_counts,\n np.zeros(len(users), dtype=np.int64),\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0).detach()\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=True,\n )\n if sequence_gradient is None:\n raise RuntimeError(\"training block omitted its sequence gradient\")\n sequence_vectors.backward(sequence_gradient)\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]\n total_targets += count\n total_weight += statistics[\"weight_sum\"]\n total_positive += statistics[\"positive_sum\"]\n total_negative += statistics[\"negative_sum\"]\n total_pair_correct += statistics[\"pair_correct\"]\n batches += 1\n if batches % 25 == 0:\n release_accelerator_cache(device)\n if batches % 250 == 0:\n batch_bce = statistics[\"loss_sum\"] / max(\n statistics[\"weight_sum\"], 1.0e-12\n )\n running_bce = total_loss / max(total_weight, 1.0e-12)\n log(\n f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n f\"targets={total_targets:,} \"\n f\"batch_bce={batch_bce:.6f} \"\n f\"running_bce={running_bce:.6f} \"\n f\"elapsed={time.monotonic() - started:.1f}s\"\n )\n\n return {\n \"epoch\": float(epoch),\n \"loss\": total_loss / max(total_weight, 1.0e-12),\n \"positive_logit\": total_positive / max(total_targets, 1),\n \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),\n \"targets\": float(total_targets),\n \"target_weight_sum\": total_weight,\n \"batches\": float(batches),\n \"seconds\": time.monotonic() - started,\n }\n\n\n@torch.no_grad()\ndef evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:37:13.222Z",
"data": {
"type": "total_tokens_reminder",
"text": "14959332 tokens left"
}
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-24T17:37:15.223Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:15.223Z",
"callId": "toolu_012DG9MNZVb9GcJLrsjzZEEz",
"name": "Bash",
"input": {
"command": "sed -n 1521,1790p /task/current_model/train_current_model.py",
"description": "Read train_model"
},
"result": {
"timestamp": "2026-09-24T17:37:15.242Z",
"content": "def train_model(\n public: Path,\n stage: str,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n checkpoint_dir: Path | None,\n resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n partition = chronological_target_partition(\n prepared,\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n training_counts = partition.training_counts\n validation_counts = partition.validation_counts\n log(\n f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n f\"validation={int(validation_counts.sum()):,} \"\n f\"selected={partition.selected_count:,} \"\n f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n f\"all={prepared.target_count:,}\"\n )\n model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n device\n )\n optimizer = torch.optim.AdamW(\n model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n )\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\n target_weights = build_target_weights(\n public,\n stage,\n prepared,\n partition.history_training_counts,\n config,\n )\n known_positive_packed = (\n build_known_positive_filter(prepared, partition.history_training_counts)\n if config.negative_filter_scope == \"full-training-history\"\n else None\n )\n history: list[dict[str, float]] = []\n best_loss = float(\"inf\")\n best_epoch = 0\n best_state: dict[str, torch.Tensor] | None = None\n epochs_without_improvement = 0\n start_epoch = 0\n\n if resume_checkpoint is not None:\n payload = torch.load(\n resume_checkpoint, map_location=device, weights_only=False\n )\n stored_config = payload.get(\"config\", {})\n architecture_fields = (\n \"bptt_steps\",\n \"embedding_dim\",\n \"hidden_dim\",\n \"user_hidden_1\",\n \"user_hidden_2\",\n \"item_hidden_1\",\n \"item_hidden_2\",\n )\n mismatches = {\n name: (stored_config.get(name), getattr(config, name))\n for name in architecture_fields\n if stored_config.get(name) != getattr(config, name)\n }\n if mismatches:\n raise RuntimeError(\n f\"resume checkpoint architecture differs: {mismatches}\"\n )\n stored_filter_scope = stored_config.get(\n \"negative_filter_scope\", \"last-100\"\n )\n stored_target_weighting = stored_config.get(\n \"target_weighting\", \"uniform\"\n )\n stored_weight_values = (\n stored_config.get(\"ordinary_target_weight\", 1.0),\n stored_config.get(\"organic_strong_target_weight\", 1.0),\n stored_config.get(\"organic_like_target_weight\", 3.0),\n stored_config.get(\"discovery_horizon_days\", 7),\n )\n current_weight_values = (\n config.ordinary_target_weight,\n config.organic_strong_target_weight,\n config.organic_like_target_weight,\n config.discovery_horizon_days,\n )\n stored_window_values = (\n stored_config.get(\"validation_fraction\", 0.10),\n stored_config.get(\"max_train_targets_per_user\", 0),\n stored_config.get(\"max_validation_targets_per_user\", 0),\n )\n current_window_values = (\n config.validation_fraction,\n config.max_train_targets_per_user,\n config.max_validation_targets_per_user,\n )\n objective_changed = (\n stored_filter_scope != config.negative_filter_scope\n or stored_target_weighting != config.target_weighting\n or stored_weight_values != current_weight_values\n or stored_window_values != current_window_values\n )\n model.load_state_dict(payload[\"model_state\"])\n if \"optimizer_state\" in payload and not objective_changed:\n optimizer.load_state_dict(payload[\"optimizer_state\"])\n for group in optimizer.param_groups:\n group[\"lr\"] = config.learning_rate\n group[\"weight_decay\"] = config.weight_decay\n log(f\"restored optimizer state from {resume_checkpoint}\")\n else:\n log(\n f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n )\n if \"generator_state\" in payload and not objective_changed:\n generator.set_state(payload[\"generator_state\"])\n start_epoch = int(payload[\"epoch\"])\n if start_epoch >= config.epochs:\n raise RuntimeError(\n f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n )\n prior = {\n str(name): float(value)\n for name, value in payload.get(\"diagnostics\", {}).items()\n }\n if objective_changed:\n prior.pop(\"validation_loss\", None)\n log(\n \"BCE objective changed: filter \"\n f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n \"target weighting \"\n f\"{stored_target_weighting}->{config.target_weighting}, \"\n f\"target window {stored_window_values}->{current_window_values}; \"\n \"recomputing the resume checkpoint's validation BCE\"\n )\n if \"validation_loss\" not in prior:\n prior.update(\n evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n )\n history.append(prior)\n best_loss = float(prior[\"validation_loss\"])\n best_epoch = start_epoch\n best_state = snapshot_model(model)\n log(\n f\"resumed validation-BCE training: epoch={start_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n\n for epoch in range(start_epoch + 1, config.epochs + 1):\n diagnostics = run_training_epoch(\n model,\n optimizer,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\n validation = evaluate_validation_bce(\n model,\n prepared,\n config,\n target_weights,\n partition.window_starts,\n training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\n diagnostics.update(validation)\n history.append(diagnostics)\n current_state = snapshot_model(model)\n if checkpoint_dir is not None:\n save_epoch_checkpoint(\n checkpoint_dir,\n epoch,\n current_state,\n config,\n diagnostics,\n optimizer,\n generator,\n )\n improved = (\n diagnostics[\"validation_loss\"]\n < best_loss - config.early_stopping_min_delta\n )\n if improved:\n best_loss = diagnostics[\"validation_loss\"]\n best_epoch = epoch\n best_state = current_state\n epochs_without_improvement = 0\n if checkpoint_dir is not None:\n torch.save(\n {\n \"epoch\": epoch,\n \"config\": asdict(config),\n \"diagnostics\": diagnostics,\n \"model_state\": current_state,\n \"optimizer_state\": optimizer.state_dict(),\n \"generator_state\": generator.get_state(),\n },\n checkpoint_dir / \"best.pt\",\n )\n else:\n epochs_without_improvement += 1\n log(\n f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n f\"train_targets={int(diagnostics['targets']):,} \"\n f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n f\"train_seconds={diagnostics['seconds']:.1f} \"\n f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n )\n release_accelerator_cache(device)\n if epochs_without_improvement >= config.early_stopping_patience:\n log(\n f\"early stopping after epoch={epoch}; \"\n f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n )\n break\n\n if best_state is None:\n raise RuntimeError(\"training did not produce a valid checkpoint\")\n model.load_state_dict(best_state)\n model.to(device).eval()\n log(\n f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n f\"loss={best_loss:.6f}\"\n )\n return model, history\n\n\n@torch.inference_mode()\ndef inference_queries(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n device: torch.device,\n) -> torch.Tensor:\n sequence_rows = prepared.inference_user_rows\n lengths = np.where(\n sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:37:15.245Z",
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"text": "14957028 tokens left"
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{
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"timestamp": "2026-09-24T17:37:24.321Z",
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{
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"timestamp": "2026-09-24T17:37:24.321Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:37:25.100Z",
"callId": "toolu_01ByNFjgUKeWFkkcvevZNLWV",
"name": "Bash",
"input": {
"command": "sleep 60; tail -3 /app/solution/baseline.log; ls -la /app/solution/",
"description": "Check progress"
},
"result": {
"timestamp": "2026-09-24T17:38:25.124Z",
"content": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ntotal 112\ndrwxr-xr-x 1 agent agent 4096 Sep 24 17:37 .\ndrwxr-xr-x 1 agent agent 4096 Sep 24 17:32 ..\n-rw-r--r-- 1 agent agent 1159 Sep 24 17:37 baseline.log\n-rw-r--r-- 1 agent agent 80058 Sep 24 17:36 train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 24 17:36 two_tower_model.py",
"isError": false
}
},
{
"kind": "context",
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{
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"timestamp": "2026-09-24T17:38:28.009Z",
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{
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"timestamp": "2026-09-24T17:38:28.009Z",
"text": ""
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{
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"timestamp": "2026-09-24T17:38:29.017Z",
"callId": "toolu_01UAPz3MjbTN9RCAe8F2aM8B",
"name": "Bash",
"input": {
"command": "sed -n 471,534p /task/current_model/train_current_model.py; echo ---; sed -n 1861,1922p /task/current_model/train_current_model.py",
"description": "Read cache and ranking"
},
"result": {
"timestamp": "2026-09-24T17:38:29.077Z",
"content": "def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n return {\n \"cache_version\": CACHE_VERSION,\n \"stage\": stage,\n \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n }\n\n\ndef save_cache(\n path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n path.parent.mkdir(parents=True, exist_ok=True)\n np.savez(\n path,\n metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n **prepared.__dict__,\n )\n log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n with np.load(path, allow_pickle=False) as artifact:\n actual = json.loads(str(artifact[\"metadata\"].item()))\n expected = cache_metadata(stage, config)\n if actual != expected:\n raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n values = {\n field: artifact[field].copy()\n for field in SequenceData.__dataclass_fields__\n }\n prepared = SequenceData(**values)\n log(\n f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n f\"targets={prepared.target_count:,}\"\n )\n return prepared\n\n\ndef load_or_prepare(\n public: Path,\n stage: str,\n config: Config,\n cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n candidate_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"validation\"\n else \"test_eligible_candidates.npz\"\n )\n eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n if cache is not None and cache.is_file():\n prepared = load_cache(cache, stage, config)\n if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n raise RuntimeError(\"cached catalog differs from candidate artifact\")\n return prepared, eligibility\n prepared, built_eligibility = prepare_data(public, stage, config)\n if cache is not None:\n save_cache(cache, prepared, stage, config)\n return prepared, built_eligibility\n\n\ndef build_item_correction(\n---\ndef rank_candidates(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n eligibility: CandidateSet,\n config: Config,\n device: torch.device,\n ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n model.eval()\n queries = inference_queries(model, prepared, config, device)\n item_parts: list[torch.Tensor] = []\n for start in range(0, len(prepared.item_ids), 2_048):\n rows = torch.arange(\n start,\n min(start + 2_048, len(prepared.item_ids)),\n dtype=torch.long,\n device=device,\n )\n item_parts.append(model.encode_items(rows).cpu())\n items = torch.cat(item_parts)\n rows: list[tuple[int, int, int]] = []\n for start in range(0, len(eligibility.uids), ranking_batch_size):\n end = min(start + ranking_batch_size, len(eligibility.uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(\n eligibility.packed[start:end],\n axis=1,\n count=len(prepared.item_ids),\n bitorder=\"little\",\n ).astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n for local_row, columns in enumerate(selected):\n uid = int(eligibility.uids[start + local_row])\n rows.extend(\n (uid, int(prepared.item_ids[column]), rank)\n for rank, column in enumerate(columns, start=1)\n )\n if end == len(eligibility.uids) or end % 1_024 == 0:\n log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n diagnostics = {\n \"query_norm_mean\": float(query_norm.mean()),\n \"query_norm_std\": float(query_norm.std()),\n \"item_norm_mean\": float(item_norm.mean()),\n \"item_norm_std\": float(item_norm.std()),\n }\n return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n output.parent.mkdir(parents=True, exist_ok=True)\n if output.suffix == \".parquet\":\n frame.to_parquet(output, index=False, compression=\"zstd\")\n elif output.suffix == \".csv\":\n frame.to_csv(output, index=False)\n else:\n raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:38:29.079Z",
"data": {
"type": "total_tokens_reminder",
"text": "14952379 tokens left"
}
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-24T17:38:30.825Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:38:30.825Z",
"callId": "toolu_01EwyWDQJ5LHajbzHvN9TrEM",
"name": "Bash",
"input": {
"command": "tail -4 /app/solution/baseline.log",
"description": "Check progress"
},
"result": {
"timestamp": "2026-09-24T17:38:30.836Z",
"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\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": "2026-09-24T17:38:30.838Z",
"data": {
"type": "total_tokens_reminder",
"text": "14950357 tokens left"
}
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-24T17:39:22.259Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:39:22.259Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:39:23.023Z",
"callId": "toolu_01SsENp9MgdfVWrAtaNrRJCe",
"name": "Bash",
"input": {
"command": "tail -3 /app/solution/baseline.log; date",
"description": "Check progress"
},
"result": {
"timestamp": "2026-09-24T17:39:23.064Z",
"content": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nThu Sep 24 17:39:23 UTC 2026",
"isError": false
}
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:39:23.617Z",
"callId": "toolu_01QcZj47ZnX42jubUxMRMhmE",
"name": "Bash",
"input": {
"command": "sed -n 1364,1490p /task/current_model/train_current_model.py",
"description": "Read validation bce"
},
"result": {
"timestamp": "2026-09-24T17:39:23.633Z",
"content": "def evaluate_validation_bce(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n target_weights: np.ndarray,\n window_starts: np.ndarray,\n training_counts: np.ndarray,\n validation_counts: np.ndarray,\n item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\n \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n model.eval()\n totals = training_counts + validation_counts\n users_with_validation = np.flatnonzero(validation_counts > 0)\n ordered = users_with_validation[\n np.argsort(totals[users_with_validation], kind=\"stable\")\n ]\n groups = [\n ordered[start : start + config.user_batch_size]\n for start in range(0, len(ordered), config.user_batch_size)\n ]\n generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n total_loss = 0.0\n total_targets = 0\n total_weight = 0.0\n started = time.monotonic()\n\n for group_number, users in enumerate(groups, start=1):\n group_totals = totals[users]\n group_starts = training_counts[users]\n hidden = torch.zeros(\n 1, len(users), config.embedding_dim, device=device\n )\n max_targets = int(group_totals.max())\n for start in range(0, max_targets, config.bptt_steps):\n steps = min(config.bptt_steps, max_targets - start)\n block = build_dense_block(\n prepared,\n target_weights,\n users,\n window_starts[users],\n group_totals,\n group_starts,\n start,\n steps,\n config,\n )\n sequence_outputs, _ = event_sequence(\n model,\n torch.from_numpy(block[0].astype(np.int64)).to(device),\n torch.from_numpy(block[1].astype(np.float32)).to(device),\n hidden,\n )\n hidden = torch.stack(\n [\n sequence_outputs[row, count - 1]\n if count\n else hidden[0, row]\n for row, count in enumerate(block[7])\n ]\n ).unsqueeze(0)\n flat_scored = block[5].reshape(-1)\n if not np.any(flat_scored):\n continue\n mask = torch.from_numpy(flat_scored).to(device)\n sequence_vectors = sequence_outputs.reshape(\n -1, config.embedding_dim\n )[mask]\n flat_states = block[3].reshape(-1)[flat_scored]\n positive_items = torch.from_numpy(\n block[4].reshape(-1)[flat_scored].astype(np.int64)\n ).to(device)\n known_history = torch.from_numpy(\n block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n np.int64\n )\n ).to(device)\n liked_items = torch.from_numpy(\n prepared.liked_states[flat_states].astype(np.int64)\n ).to(device)\n flat_user_rows = np.broadcast_to(\n users[:, None], block[5].shape\n ).reshape(-1)[flat_scored]\n negatives, negative_weights = draw_training_negatives(\n positive_items,\n known_history,\n liked_items,\n flat_user_rows,\n known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\n statistics, _ = score_target_microbatches(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=False,\n )\n total_loss += statistics[\"loss_sum\"]\n total_targets += len(positive_items)\n total_weight += statistics[\"weight_sum\"]\n if group_number % 25 == 0:\n release_accelerator_cache(device)\n return {\n \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n \"validation_targets\": float(total_targets),\n \"validation_target_weight_sum\": total_weight,\n \"validation_seconds\": time.monotonic() - started,\n }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n return {\n name: value.detach().cpu().clone()",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:39:23.634Z",
"data": {
"type": "total_tokens_reminder",
"text": "14946275 tokens left"
}
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-24T17:40:18.535Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:40:18.535Z",
"text": ""
},
{
"kind": "message",
"timestamp": "2026-09-24T17:40:18.948Z",
"text": "Baseline is training. Let me now write the improved version while it runs."
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:19.482Z",
"callId": "toolu_01JdK2k283BW39RSd3z4YS9x",
"name": "Bash",
"input": {
"command": "tail -2 /app/solution/baseline.log",
"description": "Check progress"
},
"result": {
"timestamp": "2026-09-24T17:40:19.499Z",
"content": "target weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:19.501Z",
"data": {
"type": "total_tokens_reminder",
"text": "14940594 tokens left"
}
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-24T17:40:21.783Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:40:21.783Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:23.820Z",
"callId": "toolu_016ZSueECcAopDWhdG9UscSm",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5",
"new_string": " uniform_negatives: int = 8\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"sampled-softmax\"\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5"
},
"result": {
"timestamp": "2026-09-24T17:40:23.836Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:23.839Z",
"data": {
"type": "total_tokens_reminder",
"text": "14940150 tokens left"
}
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-24T17:40:26.087Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:40:26.087Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:28.687Z",
"callId": "toolu_01TuBhiTjRqmuTzgKfu1Q577",
"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 selected_target_item_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": "2026-09-24T17:40:28.708Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:28.716Z",
"data": {
"type": "total_tokens_reminder",
"text": "14939632 tokens left"
}
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-24T17:40:31.916Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:31.916Z",
"callId": "toolu_01Roi8o8WtvsaSruk5xMVA3H",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n total = float(counts.sum())",
"new_string": " + 1\n ].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\ndef build_item_correction(\n counts: np.ndarray,\n config: Config,\n) -> torch.Tensor:\n \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n total = float(counts.sum())"
},
"result": {
"timestamp": "2026-09-24T17:40:31.953Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:31.961Z",
"data": {
"type": "total_tokens_reminder",
"text": "14939246 tokens left"
}
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-24T17:40:33.769Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:40:33.769Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:43.987Z",
"callId": "toolu_01SjuxK6oDSnyDYiKkMLNseT",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(",
"new_string": " return torch.from_numpy(correction)\n\n\ndef build_negative_sampler(\n counts: np.ndarray,\n config: Config,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Build the popularity proposal and the mixture log-propensity table.\n\n The production sampler drew every non-in-batch negative uniformly from the\n 30,000-track catalog. Listening is heavily head-skewed, so a uniform draw\n almost never lands on a track that actually competes for a top-10 slot: the\n softmax partition function is dominated by head items the sampler rarely\n sees. This builds a word2vec-style ``count ** power`` proposal so that the\n dominant terms are sampled often, together with the exact mixture\n propensity ``q`` of the combined uniform + popularity + in-batch draw so the\n sampled-softmax estimator stays unbiased for the full-catalog softmax.\n\n Args:\n counts: Selected-target occurrence count per catalog item.\n config: Trainer configuration supplying the sampler mixture.\n\n Returns:\n ``(popularity_probs, log_q)``: the proposal used for popularity draws\n and ``log q(i)`` for the full mixture, both over the catalog.\n \"\"\"\n n_items = len(counts)\n total = float(counts.sum())\n empirical = (counts + 1.0) / (total + n_items)\n weights = np.power(counts + 1.0, config.popularity_sampling_power)\n popularity = weights / weights.sum()\n draws = (\n config.uniform_negatives\n + config.popularity_negatives\n + config.inbatch_negatives\n )\n mixture = (\n config.uniform_negatives * (1.0 / n_items)\n + config.popularity_negatives * popularity\n + config.inbatch_negatives * empirical\n ) / max(draws, 1)\n log_q = np.log(np.maximum(mixture, 1.0e-12)).astype(np.float32)\n head = int(np.argmax(counts))\n log(\n f\"negative sampler: uniform={config.uniform_negatives} \"\n f\"popularity={config.popularity_negatives} \"\n f\"inbatch={config.inbatch_negatives} \"\n f\"power={config.popularity_sampling_power} \"\n f\"q_head={mixture[head]:.3e} q_min={mixture.min():.3e} \"\n f\"logq_beta={config.logq_correction}\"\n )\n return (\n torch.from_numpy(popularity.astype(np.float64)),\n torch.from_numpy(log_q),\n )\n\n\ndef build_known_positive_filter("
},
"result": {
"timestamp": "2026-09-24T17:40:44.012Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:44.018Z",
"data": {
"type": "total_tokens_reminder",
"text": "14938095 tokens left"
}
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-24T17:40:45.282Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-24T17:40:45.282Z",
"text": "Now the negative draw function:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:51.760Z",
"callId": "toolu_01WRARoEHT1Mhvt7d4UZGNAi",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)",
"new_string": " known_positive_packed: np.ndarray,\n n_items: int,\n config: Config,\n item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n batch_size = len(targets_cpu)\n uniform = torch.randint(\n n_items,\n (batch_size, config.uniform_negatives),\n generator=generator,\n device=\"cpu\",\n )\n if config.popularity_negatives:\n popular = torch.multinomial(\n popularity_probs,\n batch_size * config.popularity_negatives,\n replacement=True,\n generator=generator,\n ).reshape(batch_size, config.popularity_negatives)\n uniform = torch.cat([uniform, popular], dim=1)\n inbatch = torch.empty((batch_size, 0), dtype=torch.long)"
},
"result": {
"timestamp": "2026-09-24T17:40:51.803Z",
"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": "2026-09-24T17:40:51.812Z",
"data": {
"type": "total_tokens_reminder",
"text": "14937173 tokens left"
}
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-24T17:40:54.564Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:40:54.564Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:40:57.442Z",
"callId": "toolu_011iiQP9fuZxhqxNJGnbQo12",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, config.uniform_negatives : config.uniform_negatives + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()",
"new_string": " sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:\n correction[\n :, sampled_columns : sampled_columns + corrected_count\n ] = item_correction[inbatch[:, :corrected_count]].numpy()"
},
"result": {
"timestamp": "2026-09-24T17:40:57.469Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:40:57.474Z",
"data": {
"type": "total_tokens_reminder",
"text": "14936574 tokens left"
}
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-24T17:40:59.063Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-24T17:40:59.063Z",
"text": "Now update `draw_training_negatives` and the BCE fallback guard:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:04.252Z",
"callId": "toolu_01QJkeLCuU7P1MKR5upAen6Q",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " config: Config,\n item_correction: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n generator,\n device,\n )\n return base.draw_filtered_mixed_negatives(",
"new_string": " config: Config,\n item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n if config.negative_filter_scope == \"full-training-history\":\n if known_positive_packed is None:\n raise RuntimeError(\"full-history negative filter was not built\")\n return draw_full_history_filtered_negatives(\n positive_items,\n user_rows,\n liked_items,\n known_positive_packed,\n len(prepared.item_ids),\n config,\n item_correction,\n popularity_probs,\n generator,\n device,\n )\n if config.popularity_negatives:\n raise RuntimeError(\n \"popularity negatives require the full-training-history filter\"\n )\n return base.draw_filtered_mixed_negatives("
},
"result": {
"timestamp": "2026-09-24T17:41:04.311Z",
"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": "2026-09-24T17:41:04.313Z",
"data": {
"type": "total_tokens_reminder",
"text": "14935823 tokens left"
}
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-24T17:41:05.544Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-24T17:41:05.544Z",
"text": "Now the loss itself:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:13.673Z",
"callId": "toolu_01Xz1kZ7BZkthvFVE6fBcsVk",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": "def corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives",
"new_string": "def sampled_softmax_loss(\n positive_logits: torch.Tensor,\n negative_logits: torch.Tensor,\n positive_items: torch.Tensor,\n negatives: torch.Tensor,\n log_q: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n \"\"\"Per-target sampled-softmax cross entropy against the full catalog.\n\n The importance-sampled partition function is\n ``exp(s_pos) + sum_k exp(s_k) / (M q_k)``, so each negative logit is shifted\n by ``-(log M + beta * log q_k)``. With ``beta = 1`` the estimator is\n unbiased for the full-catalog softmax while the popularity proposal\n concentrates draws on the terms that dominate that sum.\n\n Args:\n positive_logits: Dot-product score of each target's true item.\n negative_logits: Dot-product scores of the drawn negatives.\n positive_items: Catalog row of each target's true item.\n negatives: Catalog rows of the drawn negatives.\n log_q: Mixture log-propensity per catalog item.\n config: Trainer configuration supplying the correction strength.\n\n Returns:\n A per-target cross-entropy vector.\n \"\"\"\n count = negatives.shape[1]\n shifted = negative_logits - (\n config.logq_correction * log_q[negatives] + float(np.log(max(count, 1)))\n )\n logits = torch.cat([positive_logits[:, None], shifted], dim=1)\n return F.cross_entropy(\n logits,\n torch.zeros(len(logits), dtype=torch.long, device=logits.device),\n reduction=\"none\",\n )\n\n\ndef corrected_negative_loss(\n logits: torch.Tensor,\n correction: torch.Tensor,\n config: Config,\n) -> torch.Tensor:\n raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives"
},
"result": {
"timestamp": "2026-09-24T17:41:13.693Z",
"content": "The file /app/solution/train_current_model.py has been updated successfully. (file state is current in your context — no need to Read it back)",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:41:13.698Z",
"data": {
"type": "total_tokens_reminder",
"text": "14934855 tokens left"
}
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-24T17:41:18.772Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:18.772Z",
"callId": "toolu_019zY8WiEAYjsjsruoXySSHC",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()",
"new_string": " selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.loss_objective == \"sampled-softmax\":\n per_target = sampled_softmax_loss(\n positive_logits,\n negative_logits,\n positive_items[begin:end],\n negatives[begin:end],\n log_q,\n config,\n )\n else:\n per_target = (\n F.softplus(-positive_logits)\n + corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n ) / 2.0\n loss_sum = (selected_weights * per_target).sum()"
},
"result": {
"timestamp": "2026-09-24T17:41:18.804Z",
"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": "2026-09-24T17:41:18.812Z",
"data": {
"type": "total_tokens_reminder",
"text": "14934270 tokens left"
}
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-24T17:41:22.260Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:22.260Z",
"callId": "toolu_01BxrLM8Uynox618i7zfgQYi",
"name": "Edit",
"input": {
"replace_all": false,
"file_path": "/app/solution/train_current_model.py",
"old_string": " negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:",
"new_string": " negatives: torch.Tensor,\n negative_weights: torch.Tensor,\n sample_weights: np.ndarray,\n log_q: torch.Tensor,\n device: torch.device,\n backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:"
},
"result": {
"timestamp": "2026-09-24T17:41:22.273Z",
"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": "2026-09-24T17:41:22.275Z",
"data": {
"type": "total_tokens_reminder",
"text": "14933884 tokens left"
}
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-24T17:41:25.191Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:41:25.191Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:37.739Z",
"callId": "toolu_013g8eJTMYmMVksTU2iJXJCv",
"name": "Bash",
"input": {
"command": "cd /app/solution && python - <<'EOF'\nimport re\np='train_current_model.py'\ns=open(p).read()\n\n# call sites: draw_training_negatives\nold_draw = \"\"\" known_positive_packed,\n prepared,\n config,\n item_correction,\n generator,\n device,\n )\"\"\"\nnew_draw = \"\"\" known_positive_packed,\n prepared,\n config,\n item_correction,\n popularity_probs,\n generator,\n device,\n )\"\"\"\nassert s.count(old_draw)==2, s.count(old_draw)\ns=s.replace(old_draw,new_draw)\n\nold_score = \"\"\" negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n device,\n backward=\"\"\"\nnew_score = \"\"\" negatives,\n negative_weights,\n block[8].reshape(-1)[flat_scored],\n log_q,\n device,\n backward=\"\"\"\nassert s.count(old_score)==2\ns=s.replace(old_score,new_score)\n\n# signatures: run_training_epoch and evaluate_validation_bce\nold_sig = \"\"\" item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\"\"\"\nnew_sig = \"\"\" item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n log_q: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\"\"\"\nassert s.count(old_sig)==1\ns=s.replace(old_sig,new_sig)\n\nold_sig2 = \"\"\" item_correction: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\"\"\"\nnew_sig2 = \"\"\" item_correction: torch.Tensor,\n popularity_probs: torch.Tensor,\n log_q: torch.Tensor,\n known_positive_packed: np.ndarray | None,\n device: torch.device,\n) -> dict[str, float]:\"\"\"\nassert s.count(old_sig2)==1\ns=s.replace(old_sig2,new_sig2)\n\n# train_model call sites\nold_c1 = \"\"\" target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\"\"\"\nnew_c1 = \"\"\" target_weights,\n partition.window_starts,\n training_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n generator,\n device,\n epoch,\n )\"\"\"\nassert s.count(old_c1)==1\ns=s.replace(old_c1,new_c1)\n\nold_c2 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\"\"\"\nnew_c2 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n device,\n )\"\"\"\nassert s.count(old_c2)==1\ns=s.replace(old_c2,new_c2)\n\nold_c3 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n known_positive_packed,\n device,\n )\"\"\"\nnew_c3 = \"\"\" training_counts,\n validation_counts,\n item_correction,\n popularity_probs,\n log_q,\n known_positive_packed,\n device,\n )\"\"\"\nassert s.count(old_c3)==1\ns=s.replace(old_c3,new_c3)\n\n# build_item_correction call in train_model\nold_bc = \"\"\" item_correction = build_item_correction(\n prepared,\n config,\n partition.window_starts,\n training_counts,\n )\"\"\"\nnew_bc = \"\"\" target_item_counts = selected_target_item_counts(\n prepared, partition.window_starts, training_counts\n )\n item_correction = build_item_correction(target_item_counts, config)\n popularity_probs, log_q_cpu = build_negative_sampler(\n target_item_counts, config\n )\n log_q = log_q_cpu.to(device)\"\"\"\nassert s.count(old_bc)==1\ns=s.replace(old_bc,new_bc)\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF",
"description": "Wire up new sampler plumbing"
},
"result": {
"timestamp": "2026-09-24T17:41:37.860Z",
"content": "ok",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:41:37.972Z",
"data": {
"type": "total_tokens_reminder",
"text": "14932103 tokens left"
}
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-24T17:41:40.511Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:41:40.511Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:51.178Z",
"callId": "toolu_01LxgwsYnqYSh6MeZVN8H7rT",
"name": "Bash",
"input": {
"command": "python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\n\nold = \"\"\" parser.add_argument(\n \"--negative-filter-scope\",\"\"\"\nnew = \"\"\" parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--popularity-negatives\", type=int, default=Config.popularity_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n )\n parser.add_argument(\n \"--popularity-sampling-power\",\n type=float,\n default=Config.popularity_sampling_power,\n )\n parser.add_argument(\n \"--logq-correction\", type=float, default=Config.logq_correction\n )\n parser.add_argument(\n \"--loss-objective\",\n choices=(\"sampled-softmax\", \"bce\"),\n default=Config.loss_objective,\n )\n parser.add_argument(\n \"--negative-filter-scope\",\"\"\"\nassert s.count(old)==1\ns=s.replace(old,new,1)\n\nold2 = \"\"\" config = Config(\n bptt_steps=args.bptt_steps,\"\"\"\nnew2 = \"\"\" if (\n args.uniform_negatives < 0\n or args.popularity_negatives < 0\n or args.inbatch_negatives < 0\n or args.uniform_negatives\n + args.popularity_negatives\n + args.inbatch_negatives\n < 1\n or args.popularity_sampling_power < 0\n or args.logq_correction < 0\n ):\n parser.error(\"negative sampler counts and exponents are invalid\")\n config = Config(\n bptt_steps=args.bptt_steps,\"\"\"\nassert s.count(old2)==1\ns=s.replace(old2,new2,1)\n\nold3 = \"\"\" negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\"\"\"\nnew3 = \"\"\" uniform_negatives=args.uniform_negatives,\n popularity_negatives=args.popularity_negatives,\n inbatch_negatives=args.inbatch_negatives,\n popularity_sampling_power=args.popularity_sampling_power,\n logq_correction=args.logq_correction,\n loss_objective=args.loss_objective,\n negative_filter_scope=args.negative_filter_scope,\n target_weighting=args.target_weighting,\"\"\"\nassert s.count(old3)==1\ns=s.replace(old3,new3,1)\n\nold4 = ''' \"negative_sampling\": (\n \"24-uniform-plus-8-propensity-corrected-inbatch\"\n ),'''\nnew4 = ''' \"negative_sampling\": (\n f\"{config.uniform_negatives}-uniform-plus-\"\n f\"{config.popularity_negatives}-popularity-plus-\"\n f\"{config.inbatch_negatives}-inbatch\"\n ),\n \"loss_objective\": config.loss_objective,'''\nassert s.count(old4)==1\ns=s.replace(old4,new4,1)\n\n# negative_count diagnostic\nold5 = \" negative_count = config.uniform_negatives + config.inbatch_negatives\"\nnew5 = (\" negative_count = (\\n\"\n \" config.uniform_negatives\\n\"\n \" + config.popularity_negatives\\n\"\n \" + config.inbatch_negatives\\n\"\n \" )\")\nassert s.count(old5)==1\ns=s.replace(old5,new5,1)\nopen(p,'w').write(s)\nprint(\"ok\")\nEOF\npython -c \"import ast;ast.parse(open('/app/solution/train_current_model.py').read())\" && echo parsed",
"description": "Add CLI flags"
},
"result": {
"timestamp": "2026-09-24T17:41:51.435Z",
"content": "ok\nparsed",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:41:51.448Z",
"data": {
"type": "total_tokens_reminder",
"text": "14930633 tokens left"
}
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-24T17:41:53.730Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:53.730Z",
"callId": "toolu_01PKS3AgQS8k3zHjWzWxH6Dv",
"name": "Bash",
"input": {
"command": "grep -n \"objective\\|epochs = 1\\|loss_objective\" train_current_model.py | head -20; tail -3 baseline.log; date",
"description": "Check wiring and baseline"
},
"result": {
"timestamp": "2026-09-24T17:41:53.797Z",
"content": "60: loss_objective: str = \"sampled-softmax\"\n1295: if config.loss_objective == \"sampled-softmax\":\n1773: objective_changed = (\n1780: if \"optimizer_state\" in payload and not objective_changed:\n1790: if \"generator_state\" in payload and not objective_changed:\n1801: if objective_changed:\n1804: \"BCE objective changed: filter \"\n2112: \"--loss-objective\",\n2114: default=Config.loss_objective,\n2224: loss_objective=args.loss_objective,\n2326: \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n2332: \"loss_objective\": config.loss_objective,\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\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=213.3s\nThu Sep 24 17:41:53 UTC 2026",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:41:53.799Z",
"data": {
"type": "total_tokens_reminder",
"text": "14930476 tokens left"
}
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-24T17:41:58.354Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:41:58.354Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:41:58.913Z",
"callId": "toolu_01G9rrNThovW5ULEQMgxXXWg",
"name": "Bash",
"input": {
"command": "sleep 150; tail -4 /app/solution/baseline.log",
"description": "Wait for baseline"
},
"result": {
"timestamp": "2026-09-24T17:43:58.939Z",
"content": "Exit code 143\nCommand timed out after 2m 0s",
"isError": true
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:43:58.941Z",
"data": {
"type": "total_tokens_reminder",
"text": "14929728 tokens left"
}
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-24T17:44:00.842Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:00.842Z",
"callId": "toolu_013CjJZ7aGSaupHVsK7V1tje",
"name": "Bash",
"input": {
"command": "tail -4 /app/solution/baseline.log; ls -la /app/solution/*.parquet 2>/dev/null",
"description": "Check baseline"
},
"result": {
"timestamp": "2026-09-24T17:44:00.860Z",
"content": " },\n \"torch_version\": \"2.8.0+cpu\",\n \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 1244230 Sep 24 17:43 /app/solution/production_validation.parquet",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:44:00.862Z",
"data": {
"type": "total_tokens_reminder",
"text": "14929563 tokens left"
}
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-24T17:44:03.416Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:03.416Z",
"callId": "toolu_01Xe6LAKxYc5raPiR4scqWNh",
"name": "Bash",
"input": {
"command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --output /app/solution/improved_validation.parquet > /app/solution/improved.log 2>&1 &\nsleep 2; echo started",
"description": "Start improved run"
},
"result": {
"timestamp": "2026-09-24T17:44:05.434Z",
"content": "started",
"isError": false
}
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:04.187Z",
"callId": "toolu_01BHL3JvsSALEJmGZFGzHZs6",
"name": "Bash",
"input": {
"command": "cd /task && python evaluate_public.py /app/solution/production_validation.parquet",
"description": "Evaluate baseline"
},
"result": {
"timestamp": "2026-09-24T17:44:08.882Z",
"content": "{\n \"model\": \"/app/solution/production_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.02128,\n \"quality_users\": 4263\n }\n}\nShell cwd was reset to /app",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:44:08.884Z",
"data": {
"type": "total_tokens_reminder",
"text": "14929189 tokens left"
}
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-24T17:44:15.420Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:44:15.420Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:18.639Z",
"callId": "toolu_01JKu6EcXdimFLitrWvb3id3",
"name": "Bash",
"input": {
"command": "cd /app/solution && python - <<'EOF'\nimport numpy as np, pandas as pd\nd=np.load('/task/data/test_eligible_candidates.npz')\nuids=d['uids']; items=d['item_ids']; packed=d['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n sel=np.flatnonzero(m)[:100] # catalog is popularity-ordered\n assert len(sel)==100, (u,len(sel))\n for rank,c in enumerate(sel,1):\n rows.append((int(u),int(items[c]),rank))\nf=pd.DataFrame(rows,columns=['uid','item_id','rank'])\nf.to_parquet('/app/solution/submission.parquet',index=False)\nprint(len(f), f.uid.nunique())\nEOF",
"description": "Write popularity fallback submission"
},
"result": {
"timestamp": "2026-09-24T17:44:21.535Z",
"content": "845600 8456",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:44:21.537Z",
"data": {
"type": "total_tokens_reminder",
"text": "14928247 tokens left"
}
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-24T17:44:23.079Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:23.079Z",
"callId": "toolu_01YPrXp1rgLxd2Kayh2iKwfT",
"name": "Bash",
"input": {
"command": "tail -6 /app/solution/improved.log",
"description": "Check improved log"
},
"result": {
"timestamp": "2026-09-24T17:44:23.090Z",
"content": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s",
"isError": false
}
},
{
"kind": "context",
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"data": {
"type": "total_tokens_reminder",
"text": "14928127 tokens left"
}
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-24T17:44:24.625Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:44:24.625Z",
"callId": "toolu_0123mtLc8UtUZP2DtvMfoz1u",
"name": "Bash",
"input": {
"command": "sleep 110; tail -5 /app/solution/improved.log",
"description": "Monitor improved"
},
"result": {
"timestamp": "2026-09-24T17:46:14.649Z",
"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\nnegative sampler: uniform=8 popularity=16 inbatch=8 power=0.75 q_head=1.198e-03 q_min=9.847e-06 logq_beta=1.0\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": "2026-09-24T17:46:14.651Z",
"data": {
"type": "total_tokens_reminder",
"text": "14927915 tokens left"
}
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-24T17:46:17.225Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:46:17.225Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:46:17.774Z",
"callId": "toolu_01WhFyrkjcyLyZBan2CwQF8a",
"name": "Bash",
"input": {
"command": "sleep 240; tail -4 /app/solution/improved.log",
"description": "Monitor improved"
},
"result": {
"timestamp": "2026-09-24T17:48:17.792Z",
"content": "Exit code 143\nCommand timed out after 2m 0s",
"isError": true
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:48:17.798Z",
"data": {
"type": "total_tokens_reminder",
"text": "14927493 tokens left"
}
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-24T17:48:19.391Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:48:19.391Z",
"callId": "toolu_01V9uRvku5X15Kpyy4i3erqE",
"name": "Bash",
"input": {
"command": "sleep 115; tail -3 /app/solution/improved.log",
"description": "Monitor improved"
},
"result": {
"timestamp": "2026-09-24T17:50:14.432Z",
"content": "epoch=1/1 batches=250 targets=987,417 batch_bce=9.528758 running_bce=9.306299 elapsed=209.4s\nepoch=1/1 train_loss=9.29833 validation_loss=9.05439 pair_accuracy=0.672 train_targets=1,052,953 validation_targets=130,103 train_seconds=222.6 validation_seconds=16.6\nrestored best validation-BCE checkpoint: epoch=1 loss=9.054388",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:50:14.438Z",
"data": {
"type": "total_tokens_reminder",
"text": "14927346 tokens left"
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}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-24T17:50:16.720Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:50:16.720Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:50:17.591Z",
"callId": "toolu_01Gce3LMJVrXe1aGJzucdWE6",
"name": "Bash",
"input": {
"command": "sleep 45; tail -2 /app/solution/improved.log; ls -la /app/solution/improved_validation.parquet 2>/dev/null",
"description": "Wait for ranking"
},
"result": {
"timestamp": "2026-09-24T17:51:02.631Z",
"content": " \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 1185639 Sep 24 17:50 /app/solution/improved_validation.parquet",
"isError": false
}
},
{
"kind": "context",
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"data": {
"type": "total_tokens_reminder",
"text": "14926950 tokens left"
}
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-24T17:51:04.484Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:51:04.484Z",
"callId": "toolu_01JkjELbt3sNjU6VzB4S9r3n",
"name": "Bash",
"input": {
"command": "cd /task && python evaluate_public.py /app/solution/improved_validation.parquet",
"description": "Evaluate improved"
},
"result": {
"timestamp": "2026-09-24T17:51:07.876Z",
"content": "{\n \"model\": \"/app/solution/improved_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015572,\n \"quality_users\": 4263\n }\n}\nShell cwd was reset to /app",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:51:07.878Z",
"data": {
"type": "total_tokens_reminder",
"text": "14926761 tokens left"
}
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-24T17:51:24.048Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:51:24.048Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:51:25.461Z",
"callId": "toolu_01Ny5TFLAcAdGqnqEbgNyWYM",
"name": "Bash",
"input": {
"command": "grep -E \"pair_accuracy|epoch=1/1 batches\" /app/solution/baseline.log; grep -E \"query_norm|item_norm\" /app/solution/baseline.log /app/solution/improved.log; grep -A6 '\"embedding_diagnostics\"' /app/solution/baseline.log /app/solution/improved.log",
"description": "Compare diagnostics"
},
"result": {
"timestamp": "2026-09-24T17:51:25.487Z",
"content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=213.3s\nepoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=226.7 validation_seconds=19.3\n \"pair_accuracy\": 0.7547733612041563,\n/app/solution/improved.log: \"query_norm_mean\": 1.3343452215194702,\n/app/solution/improved.log: \"query_norm_std\": 0.7968101501464844,\n/app/solution/improved.log: \"item_norm_mean\": 2.111861228942871,\n/app/solution/improved.log: \"item_norm_std\": 1.071254849433899\n/app/solution/baseline.log: \"query_norm_mean\": 1.603016972541809,\n/app/solution/baseline.log: \"query_norm_std\": 0.7697497606277466,\n/app/solution/baseline.log: \"item_norm_mean\": 1.7570279836654663,\n/app/solution/baseline.log: \"item_norm_std\": 0.8279485702514648\n/app/solution/improved.log: \"embedding_diagnostics\": {\n/app/solution/improved.log- \"query_norm_mean\": 1.3343452215194702,\n/app/solution/improved.log- \"query_norm_std\": 0.7968101501464844,\n/app/solution/improved.log- \"item_norm_mean\": 2.111861228942871,\n/app/solution/improved.log- \"item_norm_std\": 1.071254849433899\n/app/solution/improved.log- },\n/app/solution/improved.log- \"torch_version\": \"2.8.0+cpu\",\n/app/solution/baseline.log: \"embedding_diagnostics\": {\n/app/solution/baseline.log- \"query_norm_mean\": 1.603016972541809,\n/app/solution/baseline.log- \"query_norm_std\": 0.7697497606277466,\n/app/solution/baseline.log- \"item_norm_mean\": 1.7570279836654663,\n/app/solution/baseline.log- \"item_norm_std\": 0.8279485702514648\n/app/solution/baseline.log- },\n/app/solution/baseline.log- \"torch_version\": \"2.8.0+cpu\",",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:51:25.489Z",
"data": {
"type": "total_tokens_reminder",
"text": "14925404 tokens left"
}
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-24T17:51:45.799Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:51:45.799Z",
"text": ""
},
{
"kind": "message",
"timestamp": "2026-09-24T17:51:46.738Z",
"text": "Softmax + full logQ correction underperformed (0.0156 vs 0.0213). Pivoting to a change that preserves the proven BCE calibration and only enriches the negative pool:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:51:56.455Z",
"callId": "toolu_01QLbBkQzSKgqX11bC65nZ8J",
"name": "Bash",
"input": {
"command": "cd /app/solution && python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\n\ns=s.replace(''' uniform_negatives: int = 8\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"sampled-softmax\"''',''' uniform_negatives: int = 24\n popularity_negatives: int = 16\n popularity_sampling_power: float = 0.75\n logq_correction: float = 1.0\n loss_objective: str = \"bce\"''',1)\n\n# popularity block correction inside the draw\nold=''' sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if corrected_count:'''\nnew=''' sampled_columns = uniform.shape[1]\n negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n correction = np.ones_like(negatives, dtype=np.float32)\n if config.popularity_negatives:\n popularity_slice = slice(\n config.uniform_negatives, sampled_columns\n )\n correction[:, popularity_slice] = item_correction[\n torch.from_numpy(negatives[:, popularity_slice])\n ].numpy()\n if corrected_count:'''\nassert s.count(old)==1\ns=s.replace(old,new,1)\n\nold2=''' raw = F.softplus(logits)\n total = config.uniform_negatives + config.inbatch_negatives\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n if config.inbatch_negatives:\n selected = slice(config.uniform_negatives, total)\n weights = correction[:, selected]\n result += config.inbatch_negatives * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n return result / total'''\nnew2=''' raw = F.softplus(logits)\n total = (\n config.uniform_negatives\n + config.popularity_negatives\n + config.inbatch_negatives\n )\n result = torch.zeros(len(logits), device=logits.device)\n if config.uniform_negatives:\n result += config.uniform_negatives * raw[\n :, : config.uniform_negatives\n ].mean(dim=1)\n cursor = config.uniform_negatives\n for count in (config.popularity_negatives, config.inbatch_negatives):\n if not count:\n continue\n selected = slice(cursor, cursor + count)\n weights = correction[:, selected]\n result += count * (\n (raw[:, selected] * weights).sum(dim=1)\n / weights.sum(dim=1).clamp_min(1.0e-6)\n )\n cursor += count\n return result / total'''\nassert s.count(old2)==1\ns=s.replace(old2,new2,1)\nopen(p,'w').write(s)\nprint('ok')\nEOF\npython -c \"import ast;ast.parse(open('/app/solution/train_current_model.py').read())\" && echo parsed",
"description": "Switch to BCE with corrected popularity negatives"
},
"result": {
"timestamp": "2026-09-24T17:51:56.588Z",
"content": "ok\nparsed",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:51:56.590Z",
"data": {
"type": "total_tokens_reminder",
"text": "14921879 tokens left"
}
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-24T17:51:59.741Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:51:59.741Z",
"callId": "toolu_0112KwDQeTHswkyHPMQEVFrd",
"name": "Bash",
"input": {
"command": "nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --output /app/solution/v2_validation.parquet > /app/solution/v2.log 2>&1 & sleep 30; tail -3 /app/solution/v2.log",
"description": "Run v2 validation"
},
"result": {
"timestamp": "2026-09-24T17:52:29.760Z",
"content": "negative sampler: uniform=24 popularity=16 inbatch=8 power=0.75 q_head=8.100e-04 q_min=1.768e-05 logq_beta=1.0\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": "2026-09-24T17:52:29.762Z",
"data": {
"type": "total_tokens_reminder",
"text": "14921664 tokens left"
}
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-24T17:52:33.793Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:52:33.793Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:52:35.318Z",
"callId": "toolu_01XjFDUd1fD8z5AtbGKYRXSf",
"name": "Bash",
"input": {
"command": "nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/test_cache.npz --output /tmp/unused.parquet --prepare-only --threads 2 > /app/solution/prep_test.log 2>&1 & sleep 5; echo ok",
"description": "Prebuild test-stage cache"
},
"result": {
"timestamp": "2026-09-24T17:52:40.345Z",
"content": "ok",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:52:40.350Z",
"data": {
"type": "total_tokens_reminder",
"text": "14921130 tokens left"
}
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-24T17:52:42.447Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:52:42.447Z",
"callId": "toolu_01X8GDEgtMuxsJLGV5dH3CHz",
"name": "Bash",
"input": {
"command": "sleep 115; tail -2 /app/solution/v2.log; tail -1 /app/solution/prep_test.log",
"description": "Monitor"
},
"result": {
"timestamp": "2026-09-24T17:54:37.486Z",
"content": "target weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n}",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:54:37.488Z",
"data": {
"type": "total_tokens_reminder",
"text": "14920998 tokens left"
}
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-24T17:54:39.521Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:54:39.521Z",
"callId": "toolu_017evs83RXk4y9ZCi3fDkcrt",
"name": "Bash",
"input": {
"command": "ls -la /app/solution/test_cache.npz; grep -c . /app/solution/prep_test.log",
"description": "Check test cache"
},
"result": {
"timestamp": "2026-09-24T17:54:39.541Z",
"content": "-rw-r--r-- 1 agent agent 552686232 Sep 24 17:53 /app/solution/test_cache.npz\n61",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:54:39.543Z",
"data": {
"type": "total_tokens_reminder",
"text": "14920794 tokens left"
}
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-24T17:54:41.256Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:54:41.256Z",
"callId": "toolu_016XMH4nz4KLktoUth7pgZEJ",
"name": "Bash",
"input": {
"command": "sleep 230; tail -3 /app/solution/v2.log",
"description": "Monitor v2"
},
"result": {
"timestamp": "2026-09-24T17:56:41.284Z",
"content": "Exit code 143\nCommand timed out after 2m 0s",
"isError": true
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:56:41.286Z",
"data": {
"type": "total_tokens_reminder",
"text": "14920626 tokens left"
}
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-24T17:56:43.034Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:56:43.034Z",
"callId": "toolu_01XaYLY3odcDfQViDjcyjkez",
"name": "Bash",
"input": {
"command": "sleep 115; tail -3 /app/solution/v2.log",
"description": "Monitor v2"
},
"result": {
"timestamp": "2026-09-24T17:58:38.083Z",
"content": "epoch=1/1 batches=250 targets=987,417 batch_bce=0.555003 running_bce=0.550155 elapsed=293.8s\nepoch=1/1 train_loss=0.54852 validation_loss=0.50628 pair_accuracy=0.723 train_targets=1,052,953 validation_targets=130,103 train_seconds=309.4 validation_seconds=20.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.506278",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:58:38.085Z",
"data": {
"type": "total_tokens_reminder",
"text": "14920479 tokens left"
}
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-24T17:58:40.203Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T17:58:40.203Z",
"callId": "toolu_01VBvrF9P2aJhEVi83Yum1B7",
"name": "Bash",
"input": {
"command": "sleep 50; ls -la /app/solution/v2_validation.parquet && cd /task && python evaluate_public.py /app/solution/v2_validation.parquet",
"description": "Evaluate v2"
},
"result": {
"timestamp": "2026-09-24T17:59:33.599Z",
"content": "-rw-r--r-- 1 agent agent 1251666 Sep 24 17:59 /app/solution/v2_validation.parquet\n{\n \"model\": \"/app/solution/v2_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020402,\n \"quality_users\": 4263\n }\n}\nShell cwd was reset to /app",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:59:33.600Z",
"data": {
"type": "total_tokens_reminder",
"text": "14920160 tokens left"
}
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-24T17:59:43.862Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T17:59:43.862Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T17:59:46.847Z",
"callId": "toolu_01NH6vZr6SrbtQu9R2bkMuxg",
"name": "Bash",
"input": {
"command": "cd /app/solution && python - <<'EOF'\nimport numpy as np, pandas as pd\nd=np.load('/task/data/validation_eligible_candidates.npz')\nuids=d['uids']; items=d['item_ids']; packed=d['packed_eligible']\nrows=[]\nfor r,u in enumerate(uids):\n m=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n sel=np.flatnonzero(m)[:100]\n for rank,c in enumerate(sel,1):\n rows.append((int(u),int(items[c]),rank))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet('/app/solution/pop_validation.parquet',index=False)\nEOF\ncd /task && python evaluate_public.py /app/solution/pop_validation.parquet",
"description": "Popularity-only diagnostic"
},
"result": {
"timestamp": "2026-09-24T17:59:52.980Z",
"content": "{\n \"model\": \"/app/solution/pop_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nShell cwd was reset to /app",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T17:59:52.981Z",
"data": {
"type": "total_tokens_reminder",
"text": "14919006 tokens left"
}
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-24T18:00:38.657Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T18:00:38.657Z",
"text": ""
},
{
"kind": "message",
"timestamp": "2026-09-24T18:00:39.244Z",
"text": "Both hard-negative variants regressed. The trend says popularity-skewed negatives hurt this objective. Testing the opposite direction:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T18:00:41.015Z",
"callId": "toolu_01XH2oJweBgkPW1n2ouXACCN",
"name": "Bash",
"input": {
"command": "cd /app/solution && nohup python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/val_cache.npz --uniform-negatives 40 --popularity-negatives 0 --inbatch-negatives 0 --output /app/solution/v3_validation.parquet > /app/solution/v3.log 2>&1 & sleep 20; tail -2 /app/solution/v3.log; date",
"description": "Run v3: pure uniform negatives"
},
"result": {
"timestamp": "2026-09-24T18:01:01.067Z",
"content": "target weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nThu Sep 24 18:01:01 UTC 2026",
"isError": false
}
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"text": ""
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"name": "Bash",
"input": {
"command": "sleep 115; tail -2 /app/solution/v3.log",
"description": "Monitor v3"
},
"result": {
"timestamp": "2026-09-24T18:02:59.801Z",
"content": "target weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250",
"isError": false
}
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"callId": "toolu_01XvLvtLsxrsgHkibRvtuMub",
"name": "Bash",
"input": {
"command": "sleep 118; tail -2 /app/solution/v3.log",
"description": "Monitor v3"
},
"result": {
"timestamp": "2026-09-24T18:04:59.593Z",
"content": "epoch=1/1 train_loss=0.52329 validation_loss=0.47733 pair_accuracy=0.793 train_targets=1,052,953 validation_targets=130,103 train_seconds=218.0 validation_seconds=19.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.477329",
"isError": false
}
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{
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"timestamp": "2026-09-24T18:05:01.365Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T18:05:01.365Z",
"callId": "toolu_01WYPmUcJf7vSTqsba1qLeUJ",
"name": "Bash",
"input": {
"command": "sleep 45; cd /task && python evaluate_public.py /app/solution/v3_validation.parquet",
"description": "Evaluate v3"
},
"result": {
"timestamp": "2026-09-24T18:05:49.145Z",
"content": "Exit code 1\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/v3_validation.parquet'",
"isError": true
}
},
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"callId": "toolu_01DNSxqZsZFMdSvbujbKWjzX",
"name": "Bash",
"input": {
"command": "sleep 40; ls /app/solution/v3_validation.parquet && cd /task && python evaluate_public.py /app/solution/v3_validation.parquet; date",
"description": "Evaluate v3"
},
"result": {
"timestamp": "2026-09-24T18:06:34.690Z",
"content": "/app/solution/v3_validation.parquet\n{\n \"model\": \"/app/solution/v3_validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020408,\n \"quality_users\": 4263\n }\n}\nThu Sep 24 18:06:34 UTC 2026\nShell cwd was reset to /app",
"isError": false
}
},
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{
"id": 54,
"role": "agent",
"timestamp": "2026-09-24T18:06:54.695Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T18:06:54.695Z",
"text": ""
},
{
"kind": "message",
"timestamp": "2026-09-24T18:06:55.523Z",
"text": "None of the variants beat the baseline. Shipping the production configuration for the final refit — starting it now so a real model-based submission is guaranteed:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T18:06:57.326Z",
"callId": "toolu_01V36BwASPcMZyn3XZ2deAgL",
"name": "Bash",
"input": {
"command": "cd /app/solution && nohup python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/test_cache.npz --uniform-negatives 24 --popularity-negatives 0 --inbatch-negatives 8 --loss-objective bce --output /app/solution/submission.parquet > /app/solution/submission.log 2>&1 & sleep 25; tail -3 /app/solution/submission.log; date",
"description": "Start final submission refit"
},
"result": {
"timestamp": "2026-09-24T18:07:22.347Z",
"content": "negative sampler: uniform=24 popularity=0 inbatch=8 power=0.75 q_head=6.940e-04 q_min=2.523e-05 logq_beta=1.0\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\nThu Sep 24 18:07:22 UTC 2026",
"isError": false
}
},
{
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{
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"blocks": [
{
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"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T18:07:27.974Z",
"callId": "toolu_015dZv33hziEVLELdr3Bu9e3",
"name": "Bash",
"input": {
"command": "cd /app/solution && python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\nold=''' uniform_negatives: int = 24\n popularity_negatives: int = 16'''\nnew=''' uniform_negatives: int = 24\n popularity_negatives: int = 0'''\nassert s.count(old)==1\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"uniform_negatives: int\\|popularity_negatives: int\\|inbatch_negatives: int\\|loss_objective: str\\|logq_correction: float\\|popularity_sampling_power\" /app/solution/train_current_model.py | head",
"description": "Set shipped defaults"
},
"result": {
"timestamp": "2026-09-24T18:07:28.038Z",
"content": "56: uniform_negatives: int = 24\n57: popularity_negatives: int = 0\n58: popularity_sampling_power: float = 0.75\n59: logq_correction: float = 1.0\n60: loss_objective: str = \"bce\"\n61: inbatch_negatives: int = 8\n615: weights = np.power(counts + 1.0, config.popularity_sampling_power)\n633: f\"power={config.popularity_sampling_power} \"\n2121: default=Config.popularity_sampling_power,\n2224: or args.popularity_sampling_power < 0",
"isError": false
}
},
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},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-24T18:07:53.049Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T18:07:53.049Z",
"callId": "toolu_01WBfKsiF5o95WpKh3U8upWY",
"name": "Write",
"input": {
"file_path": "/app/solution/README.md",
"content": "# Made-for-You two-tower: negative-sampling investigation\n\n## What was measured\n\nAll numbers are public-backtest graded NDCG@10 (`/task/evaluate_public.py`,\n4,263 scored users), trained on `train_*` only and scored on `val_*`.\n\n| Run | Negative sampling | Loss | NDCG@10 |\n| --- | --- | --- | --- |\n| Popularity-order control (no model) | – | – | 0.01071 |\n| **Production baseline (shipped)** | 24 uniform + 8 propensity-corrected in-batch | sample-weighted BCE | **0.02128** |\n| v1 | 8 uniform + 16 popularity^0.75 + 8 in-batch | sampled softmax, full logQ correction | 0.01557 |\n| v2 | 24 uniform + 16 popularity^0.75 (propensity-corrected) + 8 in-batch | sample-weighted BCE | 0.02040 |\n| v3 | 40 uniform only | sample-weighted BCE | 0.02041 |\n\n## Diagnosis\n\nThe production sampler draws 24 of its 32 negatives uniformly from the 30,000\ntrack catalog. Listening is strongly head-skewed, so a uniform draw almost never\nlands on a track that actually competes for a top-10 slot: the softmax partition\nfunction (and, under BCE, the set of negatives with non-negligible\n`sigmoid(s)` gradient) is dominated by head items the sampler rarely sees. That\nis the textbook motivation for popularity-proportional (\"hard\") negatives, so\nthe hypothesis under test was that enriching the proposal toward the head would\nraise retrieval quality.\n\n**The hypothesis did not hold on this objective, and the measurements say why.**\nBoth interventions that shifted negative mass toward the head regressed, and\nthey regressed monotonically in how strongly they do so:\n\n- v1 (softmax with an unbiased logQ correction) was the most aggressive and lost\n 27%. With a `count^0.75` proposal and `beta = 1`, the correction shifts tail\n negatives by roughly `+8` logits versus `+3.2` for head negatives inside the\n log-sum-exp. The corrected tail therefore *becomes* the effective hard\n negative set, and the model spends its capacity suppressing tail items — the\n opposite of what the popularity proposal was meant to buy.\n- v2 (the same proposal, propensity-corrected, under the unchanged BCE) lost\n 3.5%.\n- v3, which removed the head-skewed component entirely (40 uniform, no in-batch),\n also landed at 0.0204 — i.e. the two BCE variants are indistinguishable from\n each other and both sit just below the baseline.\n\nThe discovery label is itself head-skewed: grade comes from organic likes and\n≥80% organic listens, and a pure popularity ordering already reaches half the\nproduction model's NDCG (0.0107 vs 0.0213). Anything that pushes head items down\n— whether directly (harder head negatives under BCE) or indirectly (a logQ\ncorrection that up-weights the tail) — trades away score. The production mix of\nmostly-uniform negatives plus a *small, partially* debiased in-batch component\n(power 0.5, clipped to [0.25, 4]) is already close to the local optimum of this\nknob: 24/8 beats both 40/0 and 24/16/8.\n\n## Model change shipped\n\n**None to the objective.** No variant beat the production baseline on the public\nbacktest, so the shipped artifact is the production configuration. Shipping a\nmeasured regression to claim a change would be worse than shipping the baseline.\n\n`train_current_model.py` is nonetheless a superset of the production trainer:\nthe negative sampler is now configurable (`--uniform-negatives`,\n`--popularity-negatives`, `--popularity-sampling-power`, `--inbatch-negatives`,\n`--logq-correction`, `--loss-objective`) so the experiments above are\nreproducible and the knob can be re-swept. **The defaults reproduce the\nproduction model exactly** (24 uniform + 8 in-batch, BCE); with\n`--popularity-negatives 0` the negative-draw and loss code paths are\nbit-identical to the original. The data protocol, the two-tower serving\ncontract (precomputed item vectors, one user vector, dot-product scoring, no\nuser-ID table), and the evaluation boundary are untouched.\n\n## Submission\n\n```sh\npython /app/solution/train_current_model.py \\\n --stage submission --public-dir /task \\\n --output /app/solution/submission.parquet\n```\n\nRefits on `train_* + val_*` and ranks against `test_eligible_candidates.npz`.\n\n## Where I would look next\n\nThe negative-sampling knob looks exhausted at this budget; the remaining\nheadroom is more likely in training volume than in the proposal distribution.\nThe trainer currently sees one epoch over the most recent 128 targets per user —\n1.05M of 20.0M available next-item targets (95% of targets are discarded). A\nsecond epoch, or a larger `--max-train-targets-per-user`, is the untested lever\nwith the largest surface. I did not run it inside the time budget because a\nsingle epoch already costs ~4 minutes of the 60-minute allowance and I chose to\nspend the remaining runs on the negative-sampling question that was posed.\n"
},
"result": {
"timestamp": "2026-09-24T18:07:53.074Z",
"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",
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{
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"callId": "toolu_01WcUFUbyQGmenj9x8ySd27Q",
"name": "Bash",
"input": {
"command": "sleep 115; tail -2 /app/solution/submission.log; date",
"description": "Monitor submission run"
},
"result": {
"timestamp": "2026-09-24T18:09:50.118Z",
"content": "target 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\nThu Sep 24 18:09:50 UTC 2026",
"isError": false
}
},
{
"kind": "context",
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"data": {
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{
"id": 58,
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"timestamp": "2026-09-24T18:09:52.152Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T18:09:52.152Z",
"callId": "toolu_0141FEsCyCW8QLhaUdvR1cNs",
"name": "Bash",
"input": {
"command": "sleep 118; tail -1 /app/solution/submission.log",
"description": "Monitor"
},
"result": {
"timestamp": "2026-09-24T18:11:50.216Z",
"content": "restored best validation-BCE checkpoint: epoch=1 loss=0.497949",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T18:11:50.222Z",
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"type": "total_tokens_reminder",
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{
"id": 59,
"role": "agent",
"timestamp": "2026-09-24T18:11:52.129Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-24T18:11:52.129Z",
"callId": "toolu_01HmYYd4LNtDCMJNFCKr1Xc8",
"name": "Bash",
"input": {
"command": "sleep 75; tail -2 /app/solution/submission.log; ls -la /app/solution/submission.parquet",
"description": "Wait for ranking"
},
"result": {
"timestamp": "2026-09-24T18:13:07.162Z",
"content": " \"device\": \"cpu\"\n}\n-rw-r--r-- 1 agent agent 1212474 Sep 24 18:12 /app/solution/submission.parquet",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T18:13:07.164Z",
"data": {
"type": "total_tokens_reminder",
"text": "14908799 tokens left"
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},
{
"id": 60,
"role": "agent",
"timestamp": "2026-09-24T18:13:08.740Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-24T18:13:08.740Z",
"text": "Final submission written. Validating it against the deliverable rules:"
},
{
"kind": "tool",
"timestamp": "2026-09-24T18:13:14.132Z",
"callId": "toolu_019gyWfATh9vXZCf1Xzh1kir",
"name": "Bash",
"input": {
"command": "python - <<'EOF'\nimport numpy as np, pandas as pd, os\nf=pd.read_parquet('submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nprint(\"cols\",list(f.columns),\"rows\",len(f))\nprint(\"islink\",os.path.islink('submission.parquet'),\"csv_exists\",os.path.exists('submission.csv'))\nfor c in ['uid','item_id','rank']:\n assert np.isfinite(f[c]).all() and (f[c]==f[c].astype(np.int64)).all(), c\nprint(\"users match:\", set(f.uid.astype(int))==t, len(t))\ng=f.groupby('uid')\nassert (g.size()==100).all()\nassert (g['item_id'].nunique()==100).all()\nassert g['rank'].apply(lambda r: sorted(r)==list(range(1,101))).all()\nd=np.load('/task/data/test_eligible_candidates.npz')\nuids,items,packed=d['uids'],d['item_ids'],d['packed_eligible']\npos={int(x):i for i,x in enumerate(items)}\nrow={int(u):r for r,u in enumerate(uids)}\nbad=0\nfor u,grp in g:\n m=np.unpackbits(packed[row[int(u)]],count=len(items),bitorder='little').astype(bool)\n bad+=int((~m[[pos[int(i)] for i in grp.item_id]]).sum())\nprint(\"ineligible items:\",bad)\nprint(\"OK\")\nEOF",
"description": "Validate submission"
},
"result": {
"timestamp": "2026-09-24T18:13:16.821Z",
"content": "cols ['uid', 'item_id', 'rank'] rows 845600\nislink False csv_exists False\nusers match: True 8456\nineligible items: 0\nOK",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T18:13:16.822Z",
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{
"id": 61,
"role": "agent",
"timestamp": "2026-09-24T18:13:29.322Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T18:13:29.322Z",
"text": ""
},
{
"kind": "tool",
"timestamp": "2026-09-24T18:13:30.629Z",
"callId": "toolu_01472XAq2UTgv5c7k55booZZ",
"name": "Bash",
"input": {
"command": "rm -f val_cache.npz test_cache.npz pop_validation.parquet && ls -la && python -c \"\nimport ast,sys; ast.parse(open('train_current_model.py').read()); print('script parses')\"",
"description": "Clean up caches"
},
"result": {
"timestamp": "2026-09-24T18:13:30.801Z",
"content": "total 6124\ndrwxr-xr-x 1 agent agent 4096 Sep 24 18:13 .\ndrwxr-xr-x 1 agent agent 4096 Sep 24 17:32 ..\n-rw-r--r-- 1 agent agent 4765 Sep 24 18:07 README.md\n-rw-r--r-- 1 agent agent 4694 Sep 24 17:43 baseline.log\n-rw-r--r-- 1 agent agent 5045 Sep 24 17:50 improved.log\n-rw-r--r-- 1 agent agent 1185639 Sep 24 17:50 improved_validation.parquet\n-rw-r--r-- 1 agent agent 2291 Sep 24 17:53 prep_test.log\n-rw-r--r-- 1 agent agent 1244230 Sep 24 17:43 production_validation.parquet\n-rw-r--r-- 1 agent agent 4195 Sep 24 18:12 submission.log\n-rw-r--r-- 1 agent agent 1212474 Sep 24 18:12 submission.parquet\n-rw-r--r-- 1 agent agent 87721 Sep 24 18:07 train_current_model.py\n-rw-r--r-- 1 agent agent 15985 Sep 24 17:36 two_tower_model.py\n-rw-r--r-- 1 agent agent 4203 Sep 24 17:59 v2.log\n-rw-r--r-- 1 agent agent 1251666 Sep 24 17:59 v2_validation.parquet\n-rw-r--r-- 1 agent agent 4198 Sep 24 18:06 v3.log\n-rw-r--r-- 1 agent agent 1195740 Sep 24 18:06 v3_validation.parquet\nscript parses",
"isError": false
}
},
{
"kind": "context",
"timestamp": "2026-09-24T18:13:30.802Z",
"data": {
"type": "total_tokens_reminder",
"text": "14907060 tokens left"
}
}
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},
{
"id": 62,
"role": "agent",
"timestamp": "2026-09-24T18:13:33.613Z",
"blocks": [
{
"kind": "reasoning",
"timestamp": "2026-09-24T18:13:33.613Z",
"text": ""
},
{
"kind": "message",
"timestamp": "2026-09-24T18:13:48.159Z",
"text": "## Outcome\n\n**I did not beat the production baseline.** The submission ships the production configuration, refit on `train_* + val_*`.\n\n### Measured (public backtest, graded NDCG@10, 4,263 users)\n\n| Run | Negatives | Loss | NDCG@10 |\n|---|---|---|---|\n| Popularity-order control (no model) | – | – | 0.01071 |\n| **Production baseline — shipped** | 24 uniform + 8 corrected in-batch | weighted BCE | **0.02128** |\n| v1 | 8 uniform + 16 pop^0.75 + 8 in-batch | sampled softmax, full logQ | 0.01557 |\n| v2 | 24 uniform + 16 pop^0.75 (corrected) + 8 in-batch | weighted BCE | 0.02040 |\n| v3 | 40 uniform only | weighted BCE | 0.02041 |\n\n### What the experiments say\n\nThe stated hypothesis — uniform draws over a 30k head-skewed catalog almost never sample a real top-10 competitor, so the head needs more negative mass — is the textbook diagnosis, and it is wrong for *this* objective. Both head-enriching interventions regressed, monotonically in aggressiveness. v1's full logQ correction shifts tail negatives ~`+8` logits vs ~`+3.2` for head ones inside the log-sum-exp, so the *corrected tail* becomes the effective hard-negative set — the inverse of the intent. v2 and v3 land at the same 0.0204 from opposite directions (more head skew, and none at all), which reads as this knob being flat-to-slightly-negative around the production 24/8 mix.\n\nThe reason is in the label: grade comes from organic likes and ≥80% organic listens, and a pure popularity ordering already reaches half the model's score (0.0107 vs 0.0213). Suppressing head items costs NDCG regardless of how it's done. The production mix — mostly uniform, with a small *partially* debiased in-batch component (power 0.5, clipped) — is near the local optimum.\n\nI did not ship a measured regression to claim a change.\n\n### Delivered\n\n- `/app/solution/submission.parquet` — 845,600 rows, 8,456 users, verified: exactly 100 distinct items and ranks 1–100 per user, all integer/finite, users match `target_users.parquet` exactly, 0 ineligible items, regular file, no competing `.csv`.\n- `/app/solution/train_current_model.py` + `two_tower_model.py` — a strict superset of the production trainer. The sampler is now configurable (`--uniform-negatives`, `--popularity-negatives`, `--popularity-sampling-power`, `--inbatch-negatives`, `--logq-correction`, `--loss-objective`) so the sweep is reproducible. **Defaults reproduce production exactly**; with `--popularity-negatives 0` the draw and loss paths are identical to the original. Data protocol, two-tower serving contract, and evaluation boundary untouched.\n- `/app/solution/README.md` — diagnosis, table, and reasoning; per-run logs and validation rankings retained.\n\n### Untested lever I'd go to next\n\nThe trainer uses one epoch over the most recent 128 targets per user: **1.05M of 20.0M available next-item targets, so 95% are discarded.** A second epoch or a larger `--max-train-targets-per-user` is the largest untouched surface. I spent the run budget on the negative-sampling question as posed — an epoch costs ~4 of the 60 minutes, and adding one would have pushed the final refit past the deadline with the gain unvalidated."
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