{
  "schemaVersion": 1,
  "taskId": "yambda-discovery-ranking-v01",
  "attemptId": "gemini-3-8-flash-high-basic-fixed-r02-d237f45e",
  "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
  "agentVersion": "0.61.0",
  "model": "gemini-3.8-flash",
  "sessionId": "22eb3288-1efd-4162-96ce-2835ddd16d72",
  "steps": [
    {
      "id": 1,
      "role": "user",
      "timestamp": "2026-09-29T04:19:32.587Z",
      "blocks": [
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          "kind": "message",
          "timestamp": "2026-09-29T04:19:32.587Z",
          "text": "<session_context>\nThis is the Gemini CLI. We are setting up the context for our chat.\nToday's date is Tuesday, September 29, 2026 (formatted according to the user's locale).\nMy operating system is: linux\nThe project's temporary directory is: /home/agent/.gemini/tmp/app\n- **Workspace Directories:**\n  - /app\n  - /task\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\n└───solution/\nShowing up to 200 items (files + folders).\n\n/task/\n├───evaluate_public.py\n├───ranking_evaluation.py\n├───requirements.txt\n└───data/\n    ├───candidate_catalog.parquet\n    ├───target_users.parquet\n    ├───test_eligible_candidates.npz\n    ├───test_preference_state.parquet\n    ├───train_dislikes.parquet\n    ├───train_likes.parquet\n    ├───train_listens.parquet\n    ├───train_undislikes.parquet\n    ├───train_unlikes.parquet\n    ├───val_dislikes.parquet\n    ├───val_likes.parquet\n    ├───val_listens.parquet\n    ├───val_undislikes.parquet\n    ├───val_unlikes.parquet\n    ├───validation_eligible_candidates.npz\n    └───validation_preference_state.parquet\n\n\n</session_context>"
        }
      ]
    },
    {
      "id": 2,
      "role": "user",
      "timestamp": "2026-09-29T04:19:32.632Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-29T04:19:32.632Z",
          "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\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\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, 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\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe 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 objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\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- You have up to 30 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n"
        }
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    },
    {
      "id": 3,
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      "timestamp": "2026-09-29T04:19:39.681Z",
      "blocks": [
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          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:19:39.681Z",
          "text": "Reviewing Task Parameters\n\nI'm currently examining the core requirements and constraints for this music discovery ranker project. My focus is on understanding the operational guidelines and the tight 30-minute time limit, ensuring a clear path forward."
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            "summary": "Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.",
            "title": "Initial Exploration and Environment Analysis"
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              "summary": "Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.",
              "title": "Initial Exploration and Environment Analysis"
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                    "output": "Current topic: \"Initial Exploration and Environment Analysis\"\nTopic summary: Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.\n\nStrategic Intent: Explore task files, understand data schema and sizes, and inspect public evaluation logic."
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            "resultDisplay": "## 📂 Topic: **Initial Exploration and Environment Analysis**\n\n**Summary:**\nStarting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.\n\n> [!STRATEGY]\n> **Intent:** Explore task files, understand data schema and sizes, and inspect public evaluation logic.",
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            "displayName": "Update Topic Context",
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            "timestamp": "2026-09-29T04:19:39.699Z",
            "content": "Current topic: \"Initial Exploration and Environment Analysis\"\nTopic summary: Starting initial exploration of the task environment. We will inspect evaluate_public.py, ranking_evaluation.py, requirements.txt, and explore the dataset dimensions and schemas in /task/data.\n\nStrategic Intent: Explore task files, understand data schema and sizes, and inspect public evaluation logic.",
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          "callId": "read_file__call_188919",
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                    "output": "#!/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 <validation-ranking.parquet|csv>\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        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
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            "timestamp": "2026-09-29T04:19:39.699Z",
            "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 <validation-ranking.parquet|csv>\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        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
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          "callId": "read_file__call_188922",
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                    "output": "\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:19:39.697Z",
            "resultDisplay": "",
            "description": "../task/ranking_evaluation.py",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T04:19:39.699Z",
            "content": "\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
            "isError": false
          },
          "responseMessageId": "3c7a97f4-dc4f-4d0e-af4d-74369fba752c"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:19:39.681Z",
          "callId": "read_file__call_188925",
          "name": "read_file",
          "input": {
            "file_path": "/task/requirements.txt"
          },
          "recorded": {
            "id": "read_file__call_188925",
            "name": "read_file",
            "args": {
              "file_path": "/task/requirements.txt"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_188925",
                  "name": "read_file",
                  "response": {
                    "output": "# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:19:39.697Z",
            "resultDisplay": "",
            "description": "../task/requirements.txt",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T04:19:39.699Z",
            "content": "# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
            "isError": false
          },
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        }
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    },
    {
      "id": 4,
      "role": "agent",
      "timestamp": "2026-09-29T04:19:42.723Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:19:42.723Z",
          "text": "Analyzing Installed Packages\n\nI'm reviewing the installed packages to identify any discrepancies with the requirements. Notably, LightGBM, XGBoost, CatBoost, and PyTorch are absent, which seems significant given the presence of scikit-learn and scipy."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:19:42.723Z",
          "callId": "run_shell_command__call_343759",
          "name": "run_shell_command",
          "input": {
            "command": "lscpu | head -n 20; free -h; ls -la /task/data",
            "description": "Check system CPU, RAM, and list files in /task/data with sizes."
          },
          "recorded": {
            "id": "run_shell_command__call_343759",
            "name": "run_shell_command",
            "args": {
              "command": "lscpu | head -n 20; free -h; ls -la /task/data",
              "description": "Check system CPU, RAM, and list files in /task/data with sizes."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_343759",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Architecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5299.99\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\n/usr/bin/bash: line 4: free: command not found\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 29 04:14 .\ndr-xr-xr-x 1 root root      4096 Sep 29 04:17 ..\n-r--r--r-- 1 root root    349352 Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1618\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:19:42.758Z",
            "resultDisplay": "Architecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5299.99\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\n/usr/bin/bash: line 4: free: command not found\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 29 04:14 .\ndr-xr-xr-x 1 root root      4096 Sep 29 04:17 ..\n-r--r--r-- 1 root root    349352 Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 29 04:14 validation_preference_state.parquet",
            "description": "lscpu | head -n 20; free -h; ls -la /task/data",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:19:42.758Z",
            "content": "<untrusted_context>\nOutput: Architecture:                            x86_64\nCPU op-mode(s):                          32-bit, 64-bit\nAddress sizes:                           48 bits physical, 48 bits virtual\nByte Order:                              Little Endian\nCPU(s):                                  8\nOn-line CPU(s) list:                     0-7\nVendor ID:                               AuthenticAMD\nModel name:                              AMD EPYC 7R13 Processor\nCPU family:                              25\nModel:                                   1\nThread(s) per core:                      2\nCore(s) per socket:                      4\nSocket(s):                               1\nStepping:                                1\nBogoMIPS:                                5299.99\nFlags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor:                       KVM\nVirtualization type:                     full\nL1d cache:                               128 KiB (4 instances)\nL1i cache:                               128 KiB (4 instances)\n/usr/bin/bash: line 4: free: command not found\ntotal 347392\ndr-xr-xr-x 1 root root      4096 Sep 29 04:14 .\ndr-xr-xr-x 1 root root      4096 Sep 29 04:17 ..\n-r--r--r-- 1 root root    349352 Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root     84127 Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root   7736341 Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root   1092329 Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root    857213 Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root   5374652 Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root    155988 Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root   1855885 Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root     36571 Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root    298484 Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root   9947609 Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root      7845 Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root     72536 Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root   7573821 Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root   1061761 Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1618\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "c91c0b49-e44d-49d2-8f34-94a56a1fdc62"
        }
      ]
    },
    {
      "id": 5,
      "role": "agent",
      "timestamp": "2026-09-29T04:19:52.722Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:19:52.722Z",
          "callId": "run_shell_command__call_201481",
          "name": "run_shell_command",
          "input": {
            "description": "Check total and available memory.",
            "command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\""
          },
          "recorded": {
            "id": "run_shell_command__call_201481",
            "name": "run_shell_command",
            "args": {
              "description": "Check total and available memory.",
              "command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_201481",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: MemTotal:       15988964 kB\nMemAvailable:   14738800 kB\nProcess Group PGID: 1624\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:19:52.737Z",
            "resultDisplay": "MemTotal:       15988964 kB\nMemAvailable:   14738800 kB",
            "description": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\"",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:19:52.738Z",
            "content": "<untrusted_context>\nOutput: MemTotal:       15988964 kB\nMemAvailable:   14738800 kB\nProcess Group PGID: 1624\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "fa009f83-3eed-45a7-a5fd-08e6faed0b31"
        }
      ]
    },
    {
      "id": 6,
      "role": "agent",
      "timestamp": "2026-09-29T04:19:55.593Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:19:55.593Z",
          "text": "Checking Available Resources\n\nI'm currently assessing our computational environment, specifically looking into the ~16 GB RAM and 8 vCPUs. My immediate goal is to identify available Python libraries like SciPy and scikit-learn, and explore any implicit or other relevant packages that might enhance our processing capabilities."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:19:55.593Z",
          "callId": "run_shell_command__call_417318",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nfor mod in ['implicit', 'torch', 'scipy', 'sklearn', 'lightgbm', 'xgboost', 'catboost']:\n    try:\n        __import__(mod)\n        print(mod, 'is available')\n    except ImportError:\n        print(mod, 'NOT available')\n\"",
            "description": "Check available ML packages in Python environment."
          },
          "recorded": {
            "id": "run_shell_command__call_417318",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nfor mod in ['implicit', 'torch', 'scipy', 'sklearn', 'lightgbm', 'xgboost', 'catboost']:\n    try:\n        __import__(mod)\n        print(mod, 'is available')\n    except ImportError:\n        print(mod, 'NOT available')\n\"",
              "description": "Check available ML packages in Python environment."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_417318",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: implicit NOT available\ntorch NOT available\nscipy is available\nsklearn is available\nlightgbm NOT available\nxgboost NOT available\ncatboost NOT available\nProcess Group PGID: 1628\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:19:56.978Z",
            "resultDisplay": "implicit NOT available\ntorch NOT available\nscipy is available\nsklearn is available\nlightgbm NOT available\nxgboost NOT available\ncatboost NOT available",
            "description": "Check available ML packages in Python environment.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:19:56.979Z",
            "content": "<untrusted_context>\nOutput: implicit NOT available\ntorch NOT available\nscipy is available\nsklearn is available\nlightgbm NOT available\nxgboost NOT available\ncatboost NOT available\nProcess Group PGID: 1628\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "e85ed3c8-c694-4215-84fb-1a8c6e767fa4"
        }
      ]
    },
    {
      "id": 7,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:06.491Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:06.491Z",
          "callId": "run_shell_command__call_149202",
          "name": "run_shell_command",
          "input": {
            "description": "Check existing contents of /app and /app/solution",
            "command": "ls -la /app /app/solution"
          },
          "recorded": {
            "id": "run_shell_command__call_149202",
            "name": "run_shell_command",
            "args": {
              "description": "Check existing contents of /app and /app/solution",
              "command": "ls -la /app /app/solution"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_149202",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: /app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 1 root  root  4096 Sep 29 04:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 solution\n\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 ..\nProcess Group PGID: 1646\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:06.503Z",
            "resultDisplay": "/app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 1 root  root  4096 Sep 29 04:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 solution\n\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 ..",
            "description": "ls -la /app /app/solution",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:06.504Z",
            "content": "<untrusted_context>\nOutput: /app:\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 1 root  root  4096 Sep 29 04:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 solution\n\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 04:17 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 04:17 ..\nProcess Group PGID: 1646\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "464d9ed0-ef68-46a8-8e9e-a28055033592"
        }
      ]
    },
    {
      "id": 8,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:14.185Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:20:14.185Z",
          "text": "Exploring Data Sources\n\nI'm beginning by thoroughly analyzing the provided problem and data. My initial step involves writing a Python script to inspect the `candidate_catalog.parquet` file, focusing on its shapes, column types, and distributions."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:14.185Z",
          "callId": "run_shell_command__call_361776",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nprint('--- TARGET USERS ---')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets shape:', targets.shape)\nprint(targets.head(3))\n\nprint('--- CATALOG ---')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog shape:', cat.shape)\nprint(cat.head(3))\n\nprint('--- PREFERENCE STATE ---')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val pref shape:', vpref.shape)\nprint(vpref.head(3))\ntpref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test pref shape:', tpref.shape)\nprint(tpref.head(3))\n\nprint('--- ELIGIBLE CANDIDATES ---')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n    uids = a['uids']\n    items = a['item_ids']\n    packed = a['packed_eligible']\n    print('val npz uids:', len(uids), 'items:', len(items), 'packed shape:', packed.shape)\n    # unpack a few rows to see count of eligible items\n    counts = []\n    for r in range(min(100, len(uids))):\n        mask = np.unpackbits(packed[r], count=len(items), bitorder='little')\n        counts.append(mask.sum())\n    print('eligible count per user sample (min/mean/max):', min(counts), np.mean(counts), max(counts))\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n    tuids = a['uids']\n    titems = a['item_ids']\n    tpacked = a['packed_eligible']\n    print('test npz uids:', len(tuids), 'items:', len(titems), 'packed shape:', tpacked.shape)\n\"",
            "description": "Inspect target users, catalog, preference state, and eligibility archives."
          },
          "recorded": {
            "id": "run_shell_command__call_361776",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nprint('--- TARGET USERS ---')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets shape:', targets.shape)\nprint(targets.head(3))\n\nprint('--- CATALOG ---')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog shape:', cat.shape)\nprint(cat.head(3))\n\nprint('--- PREFERENCE STATE ---')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val pref shape:', vpref.shape)\nprint(vpref.head(3))\ntpref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test pref shape:', tpref.shape)\nprint(tpref.head(3))\n\nprint('--- ELIGIBLE CANDIDATES ---')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n    uids = a['uids']\n    items = a['item_ids']\n    packed = a['packed_eligible']\n    print('val npz uids:', len(uids), 'items:', len(items), 'packed shape:', packed.shape)\n    # unpack a few rows to see count of eligible items\n    counts = []\n    for r in range(min(100, len(uids))):\n        mask = np.unpackbits(packed[r], count=len(items), bitorder='little')\n        counts.append(mask.sum())\n    print('eligible count per user sample (min/mean/max):', min(counts), np.mean(counts), max(counts))\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n    tuids = a['uids']\n    titems = a['item_ids']\n    tpacked = a['packed_eligible']\n    print('test npz uids:', len(tuids), 'items:', len(titems), 'packed shape:', tpacked.shape)\n\"",
              "description": "Inspect target users, catalog, preference state, and eligibility archives."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_361776",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: --- TARGET USERS ---\ntargets shape: (8456, 3)\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n--- CATALOG ---\ncatalog shape: (30000, 4)\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n--- PREFERENCE STATE ---\nval pref shape: (469124, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\ntest pref shape: (484776, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n--- ELIGIBLE CANDIDATES ---\nval npz uids: 8456 items: 30000 packed shape: (8456, 3750)\neligible count per user sample (min/mean/max): 25547 29098.94 29971\ntest npz uids: 8456 items: 30000 packed shape: (8456, 3750)\nProcess Group PGID: 1649\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:15.154Z",
            "resultDisplay": "--- TARGET USERS ---\ntargets shape: (8456, 3)\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n--- CATALOG ---\ncatalog shape: (30000, 4)\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n--- PREFERENCE STATE ---\nval pref shape: (469124, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\ntest pref shape: (484776, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n--- ELIGIBLE CANDIDATES ---\nval npz uids: 8456 items: 30000 packed shape: (8456, 3750)\neligible count per user sample (min/mean/max): 25547 29098.94 29971\ntest npz uids: 8456 items: 30000 packed shape: (8456, 3750)",
            "description": "Inspect target users, catalog, preference state, and eligibility archives.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:15.155Z",
            "content": "<untrusted_context>\nOutput: --- TARGET USERS ---\ntargets shape: (8456, 3)\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n--- CATALOG ---\ncatalog shape: (30000, 4)\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n--- PREFERENCE STATE ---\nval pref shape: (469124, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\ntest pref shape: (484776, 3)\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n--- ELIGIBLE CANDIDATES ---\nval npz uids: 8456 items: 30000 packed shape: (8456, 3750)\neligible count per user sample (min/mean/max): 25547 29098.94 29971\ntest npz uids: 8456 items: 30000 packed shape: (8456, 3750)\nProcess Group PGID: 1649\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "1d1245ba-fd54-46a7-a3c2-0fa140d3a7a3"
        }
      ]
    },
    {
      "id": 9,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:20.890Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:20:20.890Z",
          "text": "Analyzing User Data\n\nMy analysis reveals a substantial user base within `targets`, numbering 8,456. Concurrently, `candidate_catalog` boasts an impressive 30,000 items. A significant finding is that each user is associated with approximately 29,000 eligible items, a ratio that warrants further investigation into the data structure and its implications. I am now proceeding to cross-reference `validation_eligible_candidates.npz` with `test_eligible_candidates.npz` for deeper insights."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:20.890Z",
          "callId": "run_shell_command__call_344449",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n    val_items = a['item_ids']\n    val_uids = a['uids']\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n    test_items = a['item_ids']\n    test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('val_items equal cat item_id:', np.array_equal(val_items, cat['item_id'].to_numpy()))\nprint('test_items equal cat item_id:', np.array_equal(test_items, cat['item_id'].to_numpy()))\nprint('val_uids equal targets uid:', np.array_equal(val_uids, targets['uid'].to_numpy()))\nprint('test_uids equal targets uid:', np.array_equal(test_uids, targets['uid'].to_numpy()))\nprint('val_uids equal test_uids:', np.array_equal(val_uids, test_uids))\n\"",
            "description": "Check alignment of item_ids and uids across candidate catalog, targets, and npz files."
          },
          "recorded": {
            "id": "run_shell_command__call_344449",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n    val_items = a['item_ids']\n    val_uids = a['uids']\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n    test_items = a['item_ids']\n    test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('val_items equal cat item_id:', np.array_equal(val_items, cat['item_id'].to_numpy()))\nprint('test_items equal cat item_id:', np.array_equal(test_items, cat['item_id'].to_numpy()))\nprint('val_uids equal targets uid:', np.array_equal(val_uids, targets['uid'].to_numpy()))\nprint('test_uids equal targets uid:', np.array_equal(test_uids, targets['uid'].to_numpy()))\nprint('val_uids equal test_uids:', np.array_equal(val_uids, test_uids))\n\"",
              "description": "Check alignment of item_ids and uids across candidate catalog, targets, and npz files."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_344449",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: val_items equal cat item_id: True\ntest_items equal cat item_id: True\nval_uids equal targets uid: True\ntest_uids equal targets uid: True\nval_uids equal test_uids: True\nProcess Group PGID: 1667\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:21.609Z",
            "resultDisplay": "val_items equal cat item_id: True\ntest_items equal cat item_id: True\nval_uids equal targets uid: True\ntest_uids equal targets uid: True\nval_uids equal test_uids: True",
            "description": "Check alignment of item_ids and uids across candidate catalog, targets, and npz files.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:21.610Z",
            "content": "<untrusted_context>\nOutput: val_items equal cat item_id: True\ntest_items equal cat item_id: True\nval_uids equal targets uid: True\ntest_uids equal targets uid: True\nval_uids equal test_uids: True\nProcess Group PGID: 1667\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "0fb37018-09b4-4f2b-9cb7-6b076f16c16d"
        }
      ]
    },
    {
      "id": 10,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:33.604Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:20:33.604Z",
          "text": "Analyzing Interaction Data\n\nMy focus has shifted to examining the training and validation interaction logs. I've confirmed that `val_items`, `test_items`, and `cat['item_id']` are consistent. Similarly, `val_uids`, `test_uids`, and `targets['uid']` show identical content. The dataset comprises 8,456 target users and 30,000 candidate items."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:33.604Z",
          "callId": "run_shell_command__call_189987",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\n\nfor prefix in ['train', 'val']:\n    for name in ['likes', 'dislikes', 'unlikes', 'undislikes', 'listens']:\n        fname = f'/task/data/{prefix}_{name}.parquet'\n        df = pd.read_parquet(fname)\n        print(f'{prefix}_{name}: {len(df):,} rows, cols={list(df.columns)}, t_min={df.timestamp.min()}, t_max={df.timestamp.max()}')\n\"",
            "description": "Check summary statistics of interaction parquet files."
          },
          "recorded": {
            "id": "run_shell_command__call_189987",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\n\nfor prefix in ['train', 'val']:\n    for name in ['likes', 'dislikes', 'unlikes', 'undislikes', 'listens']:\n        fname = f'/task/data/{prefix}_{name}.parquet'\n        df = pd.read_parquet(fname)\n        print(f'{prefix}_{name}: {len(df):,} rows, cols={list(df.columns)}, t_min={df.timestamp.min()}, t_max={df.timestamp.max()}')\n\"",
              "description": "Check summary statistics of interaction parquet files."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_189987",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: train_likes: 814,831 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=80, t_max=24786790\ntrain_dislikes: 101,075 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=75, t_max=24786580\ntrain_unlikes: 292,665 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=250, t_max=24786570\ntrain_undislikes: 19,960 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=15950, t_max=24785395\ntrain_listens: 43,484,159 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=0, t_max=24786795\nval_likes: 37,795 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24788815, t_max=25393390\nval_dislikes: 3,563 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789920, t_max=25393280\nval_unlikes: 9,346 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789005, t_max=25393385\nval_undislikes: 606 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24810715, t_max=25393035\nval_listens: 1,501,697 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=24788600, t_max=25393395\nProcess Group PGID: 1684\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:35.167Z",
            "resultDisplay": "train_likes: 814,831 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=80, t_max=24786790\ntrain_dislikes: 101,075 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=75, t_max=24786580\ntrain_unlikes: 292,665 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=250, t_max=24786570\ntrain_undislikes: 19,960 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=15950, t_max=24785395\ntrain_listens: 43,484,159 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=0, t_max=24786795\nval_likes: 37,795 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24788815, t_max=25393390\nval_dislikes: 3,563 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789920, t_max=25393280\nval_unlikes: 9,346 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789005, t_max=25393385\nval_undislikes: 606 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24810715, t_max=25393035\nval_listens: 1,501,697 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=24788600, t_max=25393395",
            "description": "Check summary statistics of interaction parquet files.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:35.168Z",
            "content": "<untrusted_context>\nOutput: train_likes: 814,831 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=80, t_max=24786790\ntrain_dislikes: 101,075 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=75, t_max=24786580\ntrain_unlikes: 292,665 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=250, t_max=24786570\ntrain_undislikes: 19,960 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=15950, t_max=24785395\ntrain_listens: 43,484,159 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=0, t_max=24786795\nval_likes: 37,795 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24788815, t_max=25393390\nval_dislikes: 3,563 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789920, t_max=25393280\nval_unlikes: 9,346 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24789005, t_max=25393385\nval_undislikes: 606 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic'], t_min=24810715, t_max=25393035\nval_listens: 1,501,697 rows, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], t_min=24788600, t_max=25393395\nProcess Group PGID: 1684\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "678cc7fd-d106-4d17-8bd4-0dab6361289b"
        }
      ]
    },
    {
      "id": 11,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:39.689Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:20:39.689Z",
          "text": "Analyzing Data Sizes\n\nMy current focus is on understanding the scale of the datasets. The `train_listens` and `val_listens` tables are particularly large, with `train_listens` exceeding 43 million rows. I'm also noting the sizes of the `train_likes` and `val_likes` datasets."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:39.689Z",
          "callId": "run_shell_command__call_260112",
          "name": "run_shell_command",
          "input": {
            "description": "Check user overlaps in train and val datasets.",
            "command": "python3 -c \"\nimport pandas as pd\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nval_listens = pd.read_parquet('/task/data/val_listens.parquet', columns=['uid'])\nval_uids = set(val_listens['uid'])\nprint(f'Target users: {len(targets)}')\nprint(f'Val listens distinct users: {len(val_uids)}')\nprint(f'Val users in targets: {len(val_uids & targets)}')\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet', columns=['uid'])\ntrain_likes_uids = set(train_likes['uid'])\nprint(f'Train likes distinct users: {len(train_likes_uids)}, in targets: {len(train_likes_uids & targets)}')\n\n# Check a sample of train_listens\ntl_sample = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid'])\ntl_uids = set(tl_sample['uid'])\nprint(f'Train listens distinct users: {len(tl_uids)}, in targets: {len(tl_uids & targets)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_260112",
            "name": "run_shell_command",
            "args": {
              "description": "Check user overlaps in train and val datasets.",
              "command": "python3 -c \"\nimport pandas as pd\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nval_listens = pd.read_parquet('/task/data/val_listens.parquet', columns=['uid'])\nval_uids = set(val_listens['uid'])\nprint(f'Target users: {len(targets)}')\nprint(f'Val listens distinct users: {len(val_uids)}')\nprint(f'Val users in targets: {len(val_uids & targets)}')\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet', columns=['uid'])\ntrain_likes_uids = set(train_likes['uid'])\nprint(f'Train likes distinct users: {len(train_likes_uids)}, in targets: {len(train_likes_uids & targets)}')\n\n# Check a sample of train_listens\ntl_sample = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid'])\ntl_uids = set(tl_sample['uid'])\nprint(f'Train listens distinct users: {len(tl_uids)}, in targets: {len(tl_uids & targets)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_260112",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Target users: 8456\nVal listens distinct users: 8196\nVal users in targets: 7729\nTrain likes distinct users: 8144, in targets: 7071\nTrain listens distinct users: 9238, in targets: 8456\nProcess Group PGID: 1704\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:44.491Z",
            "resultDisplay": "Target users: 8456\nVal listens distinct users: 8196\nVal users in targets: 7729\nTrain likes distinct users: 8144, in targets: 7071\nTrain listens distinct users: 9238, in targets: 8456",
            "description": "Check user overlaps in train and val datasets.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:44.492Z",
            "content": "<untrusted_context>\nOutput: Target users: 8456\nVal listens distinct users: 8196\nVal users in targets: 7729\nTrain likes distinct users: 8144, in targets: 7071\nTrain listens distinct users: 9238, in targets: 8456\nProcess Group PGID: 1704\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "986850f3-ce9c-4bee-8713-47273dabf167"
        }
      ]
    },
    {
      "id": 12,
      "role": "agent",
      "timestamp": "2026-09-29T04:20:48.443Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:20:48.443Z",
          "text": "Confirming Training Data\n\nI've confirmed that all 8,456 target users are now present within the `train_listens` dataset. This constitutes a significant portion of the total 9,238 users. The data is substantial, with an average of approximately 4,700 listens per user across 43.48 million rows, focusing on key columns like `uid`, `item_id`, and `timestamp`."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:20:48.443Z",
          "callId": "run_shell_command__call_147503",
          "name": "run_shell_command",
          "input": {
            "description": "Measure time and memory to load train_listens columns.",
            "command": "python3 -c \"\nimport time\nimport pandas as pd\nt0 = time.time()\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read in {t1 - t0:.2f}s, shape: {df.shape}, mem: {df.memory_usage().sum() / 1e6:.1f} MB')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_147503",
            "name": "run_shell_command",
            "args": {
              "description": "Measure time and memory to load train_listens columns.",
              "command": "python3 -c \"\nimport time\nimport pandas as pd\nt0 = time.time()\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read in {t1 - t0:.2f}s, shape: {df.shape}, mem: {df.memory_usage().sum() / 1e6:.1f} MB')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_147503",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Read in 0.53s, shape: (43484159, 5), mem: 652.3 MB\nProcess Group PGID: 1723\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:20:49.767Z",
            "resultDisplay": "Read in 0.53s, shape: (43484159, 5), mem: 652.3 MB",
            "description": "Measure time and memory to load train_listens columns.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:20:49.768Z",
            "content": "<untrusted_context>\nOutput: Read in 0.53s, shape: (43484159, 5), mem: 652.3 MB\nProcess Group PGID: 1723\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "67b3ccdb-2813-4aa2-bc15-117471ed3a67"
        }
      ]
    },
    {
      "id": 13,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:02.452Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:02.452Z",
          "text": "Analyzing Data Speed\n\nMy thoughts are currently focused on the incredible speed and efficiency I've observed. Processing 43 million rows in just over half a second and a mere 652 MB is remarkable. Next, I'm investigating the `train_listens` and `val_listens` to confirm if all entries fall within the specified 30,000 catalog range or if there are outliers."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:02.452Z",
          "callId": "run_shell_command__call_150087",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\nprint(f'Catalog items: {len(cat_items)}')\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['item_id'])\ntl_items = set(tl['item_id'])\nprint(f'Train listens distinct items: {len(tl_items)}, in catalog: {len(tl_items & cat_items)}')\n\nvl = pd.read_parquet('/task/data/val_listens.parquet', columns=['item_id'])\nvl_items = set(vl['item_id'])\nprint(f'Val listens distinct items: {len(vl_items)}, in catalog: {len(vl_items & cat_items)}')\n\"",
            "description": "Check item sets and overlaps with candidate catalog."
          },
          "recorded": {
            "id": "run_shell_command__call_150087",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\nprint(f'Catalog items: {len(cat_items)}')\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['item_id'])\ntl_items = set(tl['item_id'])\nprint(f'Train listens distinct items: {len(tl_items)}, in catalog: {len(tl_items & cat_items)}')\n\nvl = pd.read_parquet('/task/data/val_listens.parquet', columns=['item_id'])\nvl_items = set(vl['item_id'])\nprint(f'Val listens distinct items: {len(vl_items)}, in catalog: {len(vl_items & cat_items)}')\n\"",
              "description": "Check item sets and overlaps with candidate catalog."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_150087",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Catalog items: 30000\nTrain listens distinct items: 845395, in catalog: 30000\nVal listens distinct items: 191747, in catalog: 29390\nProcess Group PGID: 1744\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:08.563Z",
            "resultDisplay": "Catalog items: 30000\nTrain listens distinct items: 845395, in catalog: 30000\nVal listens distinct items: 191747, in catalog: 29390",
            "description": "Check item sets and overlaps with candidate catalog.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:08.564Z",
            "content": "<untrusted_context>\nOutput: Catalog items: 30000\nTrain listens distinct items: 845395, in catalog: 30000\nVal listens distinct items: 191747, in catalog: 29390\nProcess Group PGID: 1744\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "896979e3-2c67-449c-978c-2085cb03603e"
        }
      ]
    },
    {
      "id": 14,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:13.707Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:13.707Z",
          "text": "Analyzing Candidate Presence\n\nIt's fascinating that every single one of the 30,000 `candidate_catalog` items is found within `train_listens`. My immediate next step is to deeply probe the problem's core requirements and the target objective."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:13.707Z",
          "callId": "run_shell_command__call_231490",
          "name": "run_shell_command",
          "input": {
            "description": "Inspect validation ground truth statistics.",
            "command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n    user_num_items.append(len(items))\n    for item, g in items.items():\n        grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_231490",
            "name": "run_shell_command",
            "args": {
              "description": "Inspect validation ground truth statistics.",
              "command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n    user_num_items.append(len(items))\n    for item, g in items.items():\n        grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_231490",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 3, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1765\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:14.425Z",
            "resultDisplay": "Traceback (most recent call last):\n  File \"<string>\", line 3, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'",
            "description": "Inspect validation ground truth statistics.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:14.425Z",
            "content": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 3, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1765\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "b8e413d4-d4f2-437f-99a8-c556cd5953f2"
        }
      ]
    },
    {
      "id": 15,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:19.911Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:19.911Z",
          "callId": "run_shell_command__call_261257",
          "name": "run_shell_command",
          "input": {
            "description": "Inspect validation ground truth statistics with PYTHONPATH set.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n    user_num_items.append(len(items))\n    for item, g in items.items():\n        grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_261257",
            "name": "run_shell_command",
            "args": {
              "description": "Inspect validation ground truth statistics with PYTHONPATH set.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Total target users: {len(targets)}')\nprint(f'Target users with at least one relevant item: {len(graded)}')\n\ngrade_counts = {}\nuser_num_items = []\nfor uid, items in graded.items():\n    user_num_items.append(len(items))\n    for item, g in items.items():\n        grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Grade counts across all users: {grade_counts}')\nprint(f'Items per user with labels: min={min(user_num_items)}, median={pd.Series(user_num_items).median()}, mean={pd.Series(user_num_items).mean():.2f}, max={max(user_num_items)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_261257",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Total target users: 8456\nTarget users with at least one relevant item: 4263\nGrade counts across all users: {1: 33352, 3: 4417}\nItems per user with labels: min=1, median=4.0, mean=8.86, max=442\nProcess Group PGID: 1776\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:21.186Z",
            "resultDisplay": "Total target users: 8456\nTarget users with at least one relevant item: 4263\nGrade counts across all users: {1: 33352, 3: 4417}\nItems per user with labels: min=1, median=4.0, mean=8.86, max=442",
            "description": "Inspect validation ground truth statistics with PYTHONPATH set.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:21.187Z",
            "content": "<untrusted_context>\nOutput: Total target users: 8456\nTarget users with at least one relevant item: 4263\nGrade counts across all users: {1: 33352, 3: 4417}\nItems per user with labels: min=1, median=4.0, mean=8.86, max=442\nProcess Group PGID: 1776\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "dc9eedb3-3206-40ce-ac07-173d08687100"
        }
      ]
    },
    {
      "id": 16,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:28.142Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:28.142Z",
          "text": "Analyzing User Data\n\nMy focus has shifted to dissecting user engagement metrics. I'm examining the distribution of positive interactions, particularly distinguishing between high-quality organic engagement (Grade 1) and more general positive signals (Grade 3). This granular breakdown is crucial for understanding user behavior and refining our targeting strategies."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:28.142Z",
          "callId": "run_shell_command__call_189109",
          "name": "run_shell_command",
          "input": {
            "description": "Investigate reasons for ineligibility.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('vpref states:', vpref['state'].value_counts())\n\n# Check user 100\nuid = 100\nrow = eligibility.uid_row[uid]\nmask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\nineligible_items = set(eligibility.item_ids[mask == 0])\nprint(f'User {uid}: ineligible items count = {len(ineligible_items)}')\n\n# Check overlap with user 100 preferences\nu100_pref = set(vpref[vpref.uid == uid]['item_id'])\nprint(f'User {uid}: preferences count = {len(u100_pref)}, in ineligible: {len(u100_pref & ineligible_items)}')\n\n# Check overlap with user 100 train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nu100_listens = set(tl[tl.uid == uid]['item_id'])\nprint(f'User {uid}: train listens items count = {len(u100_listens)}, in ineligible: {len(u100_listens & ineligible_items)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_189109",
            "name": "run_shell_command",
            "args": {
              "description": "Investigate reasons for ineligibility.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('vpref states:', vpref['state'].value_counts())\n\n# Check user 100\nuid = 100\nrow = eligibility.uid_row[uid]\nmask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\nineligible_items = set(eligibility.item_ids[mask == 0])\nprint(f'User {uid}: ineligible items count = {len(ineligible_items)}')\n\n# Check overlap with user 100 preferences\nu100_pref = set(vpref[vpref.uid == uid]['item_id'])\nprint(f'User {uid}: preferences count = {len(u100_pref)}, in ineligible: {len(u100_pref & ineligible_items)}')\n\n# Check overlap with user 100 train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nu100_listens = set(tl[tl.uid == uid]['item_id'])\nprint(f'User {uid}: train listens items count = {len(u100_listens)}, in ineligible: {len(u100_listens & ineligible_items)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_189109",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: vpref states: state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100: ineligible items count = 682\nUser 100: preferences count = 15, in ineligible: 15\nUser 100: train listens items count = 830, in ineligible: 682\nProcess Group PGID: 1796\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:29.378Z",
            "resultDisplay": "vpref states: state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100: ineligible items count = 682\nUser 100: preferences count = 15, in ineligible: 15\nUser 100: train listens items count = 830, in ineligible: 682",
            "description": "Investigate reasons for ineligibility.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:29.379Z",
            "content": "<untrusted_context>\nOutput: vpref states: state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100: ineligible items count = 682\nUser 100: preferences count = 15, in ineligible: 15\nUser 100: train listens items count = 830, in ineligible: 682\nProcess Group PGID: 1796\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "2be35952-9776-4eea-ba33-ed960e6a3f92"
        }
      ]
    },
    {
      "id": 17,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:36.639Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:36.639Z",
          "text": "Refining Discovery Criteria\n\nI'm currently focusing on refining the criteria for \"ineligible items.\" My current thinking is that these are tracks the user has already interacted with, as the purpose of \"Made for You\" is strictly about discovering *new* music."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:36.639Z",
          "callId": "run_shell_command__call_363555",
          "name": "run_shell_command",
          "input": {
            "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# Sort by organic_positive_listens or popularity_rank\nsorted_items = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\n# For each target user, pick top 100 eligible items\nval_ranks = {}\nfor uid in targets:\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # eligible item ids:\n    # fast lookup:\n    eligible_items = eligibility.item_ids[mask == 1]\n    # take first 100 in sorted_items that are eligible\n    # Or filter sorted_items:\n    # Since sorted_items is ordered, let's see how fast we can get top 100\n    top100 = []\n    # Using boolean indexing over sorted items if sorted_items == eligibility.item_ids\n    # Note: eligibility.item_ids is equal to cat['item_id'], which is already sorted by popularity_rank!\n    # Let's verify:\n    top100 = eligibility.item_ids[mask == 1][:100]\n    val_ranks[uid] = top100.tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint('Global catalog order NDCG@10:', scores)\n\n# What if sorted by organic_positive_listens?\ncat_sorted = cat.sort_values('organic_positive_listens', ascending=False)\norganic_order_items = cat_sorted['item_id'].to_numpy()\n# Map to index in eligibility.item_ids\ncol_indices = np.array([eligibility.item_column[item] for item in organic_order_items])\n\nval_ranks_organic = {}\nfor uid in targets:\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # Filter col_indices where mask is True\n    eligible_col_indices = col_indices[mask[col_indices] == 1]\n    val_ranks_organic[uid] = eligibility.item_ids[eligible_col_indices[:100]].tolist()\n\nscores_organic = score_rankings(val_ranks_organic, graded)\nprint('Organic positive listens order NDCG@10:', scores_organic)\n\"",
            "description": "Evaluate baseline popularity ranking on validation set."
          },
          "recorded": {
            "id": "run_shell_command__call_363555",
            "name": "run_shell_command",
            "args": {
              "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# Sort by organic_positive_listens or popularity_rank\nsorted_items = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\n# For each target user, pick top 100 eligible items\nval_ranks = {}\nfor uid in targets:\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # eligible item ids:\n    # fast lookup:\n    eligible_items = eligibility.item_ids[mask == 1]\n    # take first 100 in sorted_items that are eligible\n    # Or filter sorted_items:\n    # Since sorted_items is ordered, let's see how fast we can get top 100\n    top100 = []\n    # Using boolean indexing over sorted items if sorted_items == eligibility.item_ids\n    # Note: eligibility.item_ids is equal to cat['item_id'], which is already sorted by popularity_rank!\n    # Let's verify:\n    top100 = eligibility.item_ids[mask == 1][:100]\n    val_ranks[uid] = top100.tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint('Global catalog order NDCG@10:', scores)\n\n# What if sorted by organic_positive_listens?\ncat_sorted = cat.sort_values('organic_positive_listens', ascending=False)\norganic_order_items = cat_sorted['item_id'].to_numpy()\n# Map to index in eligibility.item_ids\ncol_indices = np.array([eligibility.item_column[item] for item in organic_order_items])\n\nval_ranks_organic = {}\nfor uid in targets:\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # Filter col_indices where mask is True\n    eligible_col_indices = col_indices[mask[col_indices] == 1]\n    val_ranks_organic[uid] = eligibility.item_ids[eligible_col_indices[:100]].tolist()\n\nscores_organic = score_rankings(val_ranks_organic, graded)\nprint('Organic positive listens order NDCG@10:', scores_organic)\n\"",
              "description": "Evaluate baseline popularity ranking on validation set."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_363555",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Global catalog order NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nOrganic positive listens order NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1819\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:39.380Z",
            "resultDisplay": "Global catalog order NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nOrganic positive listens order NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}",
            "description": "Evaluate baseline popularity ranking on validation set.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:39.380Z",
            "content": "<untrusted_context>\nOutput: Global catalog order NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nOrganic positive listens order NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1819\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "57d363cd-d5ec-4403-ae88-71183d4c17f8"
        }
      ]
    },
    {
      "id": 18,
      "role": "agent",
      "timestamp": "2026-09-29T04:21:55.236Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:55.236Z",
          "text": "Analyzing Baseline Performance\n\nMy initial analysis confirms the global popularity baseline achieves an NDCG@10 of 0.01071. I am now delving into the specifics of our available data and exploring models that have shown promise for this particular recommendation task."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:55.236Z",
          "text": "Exploring Candidate Boundaries\n\nMy current focus is on understanding the constraints of per-user candidate eligibility, recognizing that all modeling occurs within this platform-defined boundary. I'm also internalizing the distinct roles of the two public windows for validation and final refitting."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:55.236Z",
          "text": "Investigating Algorithm Options\n\nI'm now exploring collaborative filtering and discovery recommendation methods, considering matrix factorization, item-item, and user-user approaches, as well as hybrids. My immediate goal is to identify and potentially implement efficient algorithms using available libraries like scipy and sklearn, noting the absence of implicit and torch."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:21:55.236Z",
          "text": "Refining Candidate Set\n\nI'm now confirming that all evaluated candidates originate from the specified 30,000-item catalog, even when user interaction history includes items beyond this set. This clarifies the scope for subsequent model development."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:21:55.236Z",
          "callId": "run_shell_command__call_237178",
          "name": "run_shell_command",
          "input": {
            "description": "Check breakdown of train listens and likes inside vs outside catalog.",
            "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'])\nin_cat = tl['item_id'].isin(cat_items)\nprint(f'Train listens: total={len(tl):,}, in_cat={in_cat.sum():,} ({in_cat.mean():.1%})')\n\n# Positive listens: played_ratio_pct >= 80 or >= 50\npos50 = tl['played_ratio_pct'] >= 50\npos80 = tl['played_ratio_pct'] >= 80\nprint(f'Listens >= 50%: {pos50.sum():,} ({pos50.mean():.1%}), in cat: {(pos50 & in_cat).sum():,}')\nprint(f'Listens >= 80%: {pos80.sum():,} ({pos80.mean():.1%}), in cat: {(pos80 & in_cat).sum():,}')\nprint(f'Organic Listens >= 80%: {(pos80 & (tl.is_organic == 1)).sum():,}, in cat: {(pos80 & (tl.is_organic == 1) & in_cat).sum():,}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nin_cat_likes = likes['item_id'].isin(cat_items)\nprint(f'Train likes: total={len(likes):,}, in_cat={in_cat_likes.sum():,} ({in_cat_likes.mean():.1%})')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_237178",
            "name": "run_shell_command",
            "args": {
              "description": "Check breakdown of train listens and likes inside vs outside catalog.",
              "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'])\nin_cat = tl['item_id'].isin(cat_items)\nprint(f'Train listens: total={len(tl):,}, in_cat={in_cat.sum():,} ({in_cat.mean():.1%})')\n\n# Positive listens: played_ratio_pct >= 80 or >= 50\npos50 = tl['played_ratio_pct'] >= 50\npos80 = tl['played_ratio_pct'] >= 80\nprint(f'Listens >= 50%: {pos50.sum():,} ({pos50.mean():.1%}), in cat: {(pos50 & in_cat).sum():,}')\nprint(f'Listens >= 80%: {pos80.sum():,} ({pos80.mean():.1%}), in cat: {(pos80 & in_cat).sum():,}')\nprint(f'Organic Listens >= 80%: {(pos80 & (tl.is_organic == 1)).sum():,}, in cat: {(pos80 & (tl.is_organic == 1) & in_cat).sum():,}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nin_cat_likes = likes['item_id'].isin(cat_items)\nprint(f'Train likes: total={len(likes):,}, in_cat={in_cat_likes.sum():,} ({in_cat_likes.mean():.1%})')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_237178",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Train listens: total=43,484,159, in_cat=31,249,613 (71.9%)\nListens >= 50%: 27,500,755 (63.2%), in cat: 20,056,329\nListens >= 80%: 25,755,197 (59.2%), in cat: 18,801,138\nOrganic Listens >= 80%: 12,177,583, in cat: 8,699,160\nTrain likes: total=814,831, in_cat=554,824 (68.1%)\nProcess Group PGID: 1845\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:21:59.823Z",
            "resultDisplay": "Train listens: total=43,484,159, in_cat=31,249,613 (71.9%)\nListens >= 50%: 27,500,755 (63.2%), in cat: 20,056,329\nListens >= 80%: 25,755,197 (59.2%), in cat: 18,801,138\nOrganic Listens >= 80%: 12,177,583, in cat: 8,699,160\nTrain likes: total=814,831, in_cat=554,824 (68.1%)",
            "description": "Check breakdown of train listens and likes inside vs outside catalog.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:21:59.823Z",
            "content": "<untrusted_context>\nOutput: Train listens: total=43,484,159, in_cat=31,249,613 (71.9%)\nListens >= 50%: 27,500,755 (63.2%), in cat: 20,056,329\nListens >= 80%: 25,755,197 (59.2%), in cat: 18,801,138\nOrganic Listens >= 80%: 12,177,583, in cat: 8,699,160\nTrain likes: total=814,831, in_cat=554,824 (68.1%)\nProcess Group PGID: 1845\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "d997177e-5055-4d9f-a02b-b28ceaeb9a9a"
        }
      ]
    },
    {
      "id": 19,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:07.159Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:07.159Z",
          "text": "Analyzing Catalog Item Performance\n\nI'm focusing on the impressive engagement with our core catalog, noting over 70% of interactions occur within these 30,000 items. Approximately 20 million positive listens have been recorded. My next step is to determine the relevant time frame for `train_listens`."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:07.159Z",
          "text": "Clarifying Timestamp Units\n\nI'm now examining the timestamp units, suspecting they are in seconds given the range and the context of the training cutoff. This will allow me to accurately define timeframes for user behavior, as recent listening patterns are likely more predictive than older ones."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:07.159Z",
          "callId": "run_shell_command__call_353171",
          "name": "run_shell_command",
          "input": {
            "description": "Analyze validation preference state data.",
            "command": "python3 -c \"\nimport pandas as pd\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vpref.head(5))\nprint(vpref['state'].value_counts())\nprint(f'Distinct users in vpref: {vpref.uid.nunique()}')\nprint(f'Distinct items in vpref: {vpref.item_id.nunique()}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_353171",
            "name": "run_shell_command",
            "args": {
              "description": "Analyze validation preference state data.",
              "command": "python3 -c \"\nimport pandas as pd\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vpref.head(5))\nprint(vpref['state'].value_counts())\nprint(f'Distinct users in vpref: {vpref.uid.nunique()}')\nprint(f'Distinct items in vpref: {vpref.item_id.nunique()}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_353171",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Validation preference state:\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nDistinct users in vpref: 7038\nDistinct items in vpref: 28814\nProcess Group PGID: 1868\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:07.936Z",
            "resultDisplay": "Validation preference state:\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nDistinct users in vpref: 7038\nDistinct items in vpref: 28814",
            "description": "Analyze validation preference state data.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:22:07.937Z",
            "content": "<untrusted_context>\nOutput: Validation preference state:\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nDistinct users in vpref: 7038\nDistinct items in vpref: 28814\nProcess Group PGID: 1868\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "5aa56065-08cf-4b09-b3ff-8fe11c40a5f9"
        }
      ]
    },
    {
      "id": 20,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:11.523Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:11.523Z",
          "text": "Checking Preference Eligibility\n\nI'm currently investigating the eligibility of preference items across all users, specifically cross-referencing `validation_preference_state` against `validation_eligible_candidates.npz`. My initial tests, showing User 100 having all 15 preferences marked as ineligible, suggest a broader pattern to explore."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:11.523Z",
          "callId": "run_shell_command__call_148480",
          "name": "run_shell_command",
          "input": {
            "description": "Check if any items in preference state are eligible.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\neligible_pref = 0\nfor uid, item in vpref[['uid', 'item_id']].itertuples(index=False, name=None):\n    if eligibility.is_eligible(uid, item):\n        eligible_pref += 1\nprint(f'Eligible items that are in vpref: {eligible_pref} out of {len(vpref)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_148480",
            "name": "run_shell_command",
            "args": {
              "description": "Check if any items in preference state are eligible.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\neligible_pref = 0\nfor uid, item in vpref[['uid', 'item_id']].itertuples(index=False, name=None):\n    if eligibility.is_eligible(uid, item):\n        eligible_pref += 1\nprint(f'Eligible items that are in vpref: {eligible_pref} out of {len(vpref)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_148480",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Eligible items that are in vpref: 0 out of 469124\nProcess Group PGID: 1886\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:12.825Z",
            "resultDisplay": "Eligible items that are in vpref: 0 out of 469124",
            "description": "Check if any items in preference state are eligible.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:22:12.825Z",
            "content": "<untrusted_context>\nOutput: Eligible items that are in vpref: 0 out of 469124\nProcess Group PGID: 1886\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "9052c63c-d61c-424e-bdde-cf5f98320054"
        }
      ]
    },
    {
      "id": 21,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:24.299Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:24.299Z",
          "text": "Refining Candidate Eligibility\n\nI'm analyzing how to best leverage user preferences, even for items initially deemed ineligible. My current focus is on integrating \"like\" and \"dislike\" signals from `preference_state` to inform recommendations, ensuring excluded items still contribute to understanding user taste without direct recommendation."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:24.299Z",
          "text": "Exploring Track Similarities\n\nI'm now investigating track metadata. Specifically, I'm checking if features like artist, genre, or audio characteristics are available. So far, the `candidate_catalog.parquet` and `train_listens.parquet` files reveal columns such as `item_id`, `positive_listens`, and `track_length_seconds`, but direct genre or artist information is absent."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:24.299Z",
          "text": "Focusing on Interaction Data\n\nI'm realizing this is primarily a collaborative filtering problem, focusing on interaction graphs and behavioral ranking. My current analysis centers on the structure of user-item interactions: the number of users and items, playback details including completion ratio and recency, and explicit feedback like likes and dislikes."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:24.299Z",
          "callId": "run_shell_command__call_261549",
          "name": "run_shell_command",
          "input": {
            "description": "Create and validate initial baseline submission for test set.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_sorted = cat.sort_values('popularity_rank')\n# item_ids in order\nitems_in_order = cat_sorted['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # eligible item ids:\n    eligible_items = eligibility.item_ids[mask == 1]\n    top100 = eligible_items[:100]\n    for rank, item_id in enumerate(top100, 1):\n        records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\nprint('Generated rows:', len(df))\nvalidated = validate_ranking(df, targets)\nprint('Validation passed successfully!')\n\n# Check eligibility for all rows\nall_eligible = all(eligibility.is_eligible(row.uid, row.item_id) for row in validated.itertuples())\nprint('All eligible:', all_eligible)\n\nvalidated.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Saved baseline submission to /app/solution/submission.parquet')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_261549",
            "name": "run_shell_command",
            "args": {
              "description": "Create and validate initial baseline submission for test set.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\n\n# Popularity rank from candidate_catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_sorted = cat.sort_values('popularity_rank')\n# item_ids in order\nitems_in_order = cat_sorted['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # eligible item ids:\n    eligible_items = eligibility.item_ids[mask == 1]\n    top100 = eligible_items[:100]\n    for rank, item_id in enumerate(top100, 1):\n        records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\nprint('Generated rows:', len(df))\nvalidated = validate_ranking(df, targets)\nprint('Validation passed successfully!')\n\n# Check eligibility for all rows\nall_eligible = all(eligibility.is_eligible(row.uid, row.item_id) for row in validated.itertuples())\nprint('All eligible:', all_eligible)\n\nvalidated.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Saved baseline submission to /app/solution/submission.parquet')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_261549",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Generated rows: 845600\nValidation passed successfully!\nAll eligible: True\nSaved baseline submission to /app/solution/submission.parquet\nProcess Group PGID: 1910\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:27.886Z",
            "resultDisplay": "Generated rows: 845600\nValidation passed successfully!\nAll eligible: True\nSaved baseline submission to /app/solution/submission.parquet",
            "description": "Create and validate initial baseline submission for test set.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:22:27.886Z",
            "content": "<untrusted_context>\nOutput: Generated rows: 845600\nValidation passed successfully!\nAll eligible: True\nSaved baseline submission to /app/solution/submission.parquet\nProcess Group PGID: 1910\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "10488f74-e7f6-46c4-ab2d-3ff8608a7b8b"
        }
      ]
    },
    {
      "id": 22,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:34.315Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:34.315Z",
          "callId": "run_shell_command__call_143852",
          "name": "run_shell_command",
          "input": {
            "command": "ls -la /app/solution/submission.parquet",
            "description": "Verify baseline submission file exists."
          },
          "recorded": {
            "id": "run_shell_command__call_143852",
            "name": "run_shell_command",
            "args": {
              "command": "ls -la /app/solution/submission.parquet",
              "description": "Verify baseline submission file exists."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_143852",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: -rw-r--r-- 1 agent agent 735840 Sep 29 04:22 /app/solution/submission.parquet\nProcess Group PGID: 1927\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:34.329Z",
            "resultDisplay": "-rw-r--r-- 1 agent agent 735840 Sep 29 04:22 /app/solution/submission.parquet",
            "description": "ls -la /app/solution/submission.parquet",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:22:34.330Z",
            "content": "<untrusted_context>\nOutput: -rw-r--r-- 1 agent agent 735840 Sep 29 04:22 /app/solution/submission.parquet\nProcess Group PGID: 1927\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "bd8aa477-cc01-47c7-b1a6-2952c975f743"
        }
      ]
    },
    {
      "id": 23,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:37.943Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:37.943Z",
          "text": "Testing Evaluation Script\n\nI'm now checking how `evaluate_public.py` functions and its speed for development evaluations. It requires a validation ranking path like `python evaluate_public.py <validation-ranking.parquet|csv>`."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:37.943Z",
          "callId": "run_shell_command__call_191873",
          "name": "run_shell_command",
          "input": {
            "description": "Generate and evaluate validation baseline ranking.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems_in_order = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    eligible_items = eligibility.item_ids[mask == 1]\n    top100 = eligible_items[:100]\n    for rank, item_id in enumerate(top100, 1):\n        records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
          },
          "recorded": {
            "id": "run_shell_command__call_191873",
            "name": "run_shell_command",
            "args": {
              "description": "Generate and evaluate validation baseline ranking.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems_in_order = cat.sort_values('popularity_rank')['item_id'].to_numpy()\n\nrecords = []\nfor uid in sorted(targets):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    eligible_items = eligibility.item_ids[mask == 1]\n    top100 = eligible_items[:100]\n    for rank, item_id in enumerate(top100, 1):\n        records.append({'uid': uid, 'item_id': item_id, 'rank': rank})\n\ndf = pd.DataFrame(records)\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_191873",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: {\n  \"model\": \"/app/solution/val_baseline.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nProcess Group PGID: 1930\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:42.753Z",
            "resultDisplay": "{\n  \"model\": \"/app/solution/val_baseline.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}",
            "description": "Generate and evaluate validation baseline ranking.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:22:42.754Z",
            "content": "<untrusted_context>\nOutput: {\n  \"model\": \"/app/solution/val_baseline.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nProcess Group PGID: 1930\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "9a4cd777-ddf9-4a28-8315-1d7e8d0deca1"
        }
      ]
    },
    {
      "id": 24,
      "role": "agent",
      "timestamp": "2026-09-29T04:22:46.365Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:22:46.365Z",
          "text": "Exploring Model Design\n\nMy evaluation script is now producing solid results, specifically `0.01071`. I'm leaning towards clearing out temporary files like `/app/solution/val_baseline.parquet` to maintain a cleaner workspace. The next exciting phase involves a deep dive into the model's architecture and rigorous testing protocols."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:22:46.365Z",
          "callId": "update_topic__call_353549",
          "name": "update_topic",
          "input": {
            "summary": "Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.",
            "title": "Modeling and Collaborative Filtering Exploration",
            "strategic_intent": "Design and benchmark candidate collaborative filtering and ranking models on the validation window."
          },
          "recorded": {
            "id": "update_topic__call_353549",
            "name": "update_topic",
            "args": {
              "summary": "Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.",
              "title": "Modeling and Collaborative Filtering Exploration",
              "strategic_intent": "Design and benchmark candidate collaborative filtering and ranking models on the validation window."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "update_topic__call_353549",
                  "name": "update_topic",
                  "response": {
                    "output": "Current topic: \"Modeling and Collaborative Filtering Exploration\"\nTopic summary: Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.\n\nStrategic Intent: Design and benchmark candidate collaborative filtering and ranking models on the validation window."
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:22:46.368Z",
            "resultDisplay": "## 📂 Topic: **Modeling and Collaborative Filtering Exploration**\n\n**Summary:**\nCompleted initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.\n\n> [!STRATEGY]\n> **Intent:** Design and benchmark candidate collaborative filtering and ranking models on the validation window.",
            "description": "Update topic to: \"Modeling and Collaborative Filtering Exploration\"",
            "displayName": "Update Topic Context",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T04:22:46.368Z",
            "content": "Current topic: \"Modeling and Collaborative Filtering Exploration\"\nTopic summary: Completed initial exploration, data schema analysis, and verified the evaluation pipeline with a valid baseline submission (quality_ndcg@10 = 0.01071). We are now transitioning to designing and testing collaborative filtering algorithms (Item-Item CF, BM25 / cosine similarity, SVD, EASE, hybrid weighting with recency and explicit preferences) to maximize discovery NDCG@10.\n\nStrategic Intent: Design and benchmark candidate collaborative filtering and ranking models on the validation window.",
            "isError": false
          },
          "responseMessageId": "ea22611e-fd9d-4648-b7d4-ccc513e58fe1"
        }
      ]
    },
    {
      "id": 25,
      "role": "agent",
      "timestamp": "2026-09-29T04:23:01.193Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:23:01.193Z",
          "text": "Exploring Recommendation Nuances\n\nI'm currently dissecting the core elements of this recommendation challenge. My focus is on a graded NDCG@10 target within a 7-day timeframe, specifically for organic discovery events. This includes defining grade 3 as organic likes, grade 1 as organic listens exceeding 80% of track duration, and ensuring user relevance."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:23:01.193Z",
          "text": "Defining Eligibility Criteria\n\nMy current focus is on refining the eligibility rules for candidate recommendations. It's clear that only truly new tracks, those the user hasn't interacted with via listens, likes, or dislikes, should be considered. This ensures a genuine discovery experience rather than reinforcing past behavior."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:23:01.193Z",
          "text": "Testing Recommendation Interactions\n\nI'm now focused on building a Python experimentation harness. I'm exploring various user interaction signals for $R$, including different completion ratio thresholds, the impact of organic listens, explicit likes, and preference states. I'm also investigating recency weighting with exponential decay or half-life, and item frequency normalization techniques like BM25 and TF-IDF."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:23:01.193Z",
          "callId": "run_shell_command__call_192233",
          "name": "run_shell_command",
          "input": {
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Setup done in {time.time() - t0:.2f}s. Target users: {n_users}, Catalog items: {n_items}')\n\"",
            "description": "Test setup and indexing speed for validation benchmarking."
          },
          "recorded": {
            "id": "run_shell_command__call_192233",
            "name": "run_shell_command",
            "args": {
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint(f'Setup done in {time.time() - t0:.2f}s. Target users: {n_users}, Catalog items: {n_items}')\n\"",
              "description": "Test setup and indexing speed for validation benchmarking."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_192233",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Setup done in 0.58s. Target users: 8456, Catalog items: 30000\nProcess Group PGID: 1965\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:23:02.692Z",
            "resultDisplay": "Setup done in 0.58s. Target users: 8456, Catalog items: 30000",
            "description": "Test setup and indexing speed for validation benchmarking.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:23:02.692Z",
            "content": "<untrusted_context>\nOutput: Setup done in 0.58s. Target users: 8456, Catalog items: 30000\nProcess Group PGID: 1965\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "2d69659c-eb1b-4be9-b64d-0885707df3d3"
        }
      ]
    },
    {
      "id": 26,
      "role": "agent",
      "timestamp": "2026-09-29T04:23:15.886Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:23:15.886Z",
          "text": "Developing Interaction Matrix\n\nI am currently focusing on constructing the user interaction matrix, $R$. My immediate steps involve extracting relevant features from the `train_listens` data, specifically utilizing timestamps. I'm calculating the time difference from a cutoff point, $24786800$, and exploring the application of a half-life decay function."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:23:15.886Z",
          "callId": "run_shell_command__call_365524",
          "name": "run_shell_command",
          "input": {
            "description": "Test User-User Collaborative Filtering model.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n# Filter to target users and catalog items\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\nprint(f'Filtered listens count: {len(tl_filtered):,} in {time.time() - t0:.2f}s')\n\n# Only positive listens: played_ratio_pct >= 50\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\nprint(f'Positive listens count: {len(tl_filtered):,}')\n\n# Recency decay: half-life 60 days (5,184,000 s)\ncutoff = 24786800\nhalflife = 60 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\n# Organic boost\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\n# Completion ratio boost\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\nprint(f'Matrix R created with shape {R.shape}, nnz={R.nnz:,} in {time.time() - t0:.2f}s')\n\n# Item-item cosine similarity:\n# Normalize item columns: S = (R_norm^T * R_norm)\n# Or user-item normalized\n# First, let's normalize item vectors: L2 norm of columns of R\nitem_norms = np.sqrt(np.asarray(R.power(2).sum(axis=0)).ravel()) + 1e-8\n# S = R^T * R with column normalization\n# Since n_items = 30000, R is (8456, 30000)\n# Notice R has 8456 rows!\n# We can do User-User CF or Item-Item CF:\n# For User-User CF:\n# U_sim = R_norm * R_norm^T is only (8456, 8456)!\n# That is MUCH smaller and faster to compute than (30000, 30000)!\n# Let's check: (8456, 8456) has only 71 million elements (280 MB)!\nuser_norms = np.sqrt(np.asarray(R.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R\nprint(f'Normalized R in {time.time() - t0:.2f}s')\n\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T # (8456, 8456)\nprint(f'User similarity computed in {time.time() - t_sim:.2f}s, shape: {U_sim.shape}, nnz: {U_sim.nnz:,}')\n\n# Reconstructed scores: Scores = U_sim * R\nt_pred = time.time()\nScores = U_sim @ R # (8456, 30000) sparse or dense\nprint(f'Scores computed in {time.time() - t_pred:.2f}s, type: {type(Scores)}')\n\n# Let's evaluate!\n# For each user, we need top 100 eligible items\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # Get user scores as dense array\n    user_scores = Scores[i].toarray().ravel()\n    # Mask ineligible items by setting their score to -infinity\n    user_scores[mask == 0] = -np.inf\n    # Top 100\n    top100_idx = np.argpartition(user_scores, -100)[-100:]\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'User-User CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_365524",
            "name": "run_shell_command",
            "args": {
              "description": "Test User-User Collaborative Filtering model.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n# Filter to target users and catalog items\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\nprint(f'Filtered listens count: {len(tl_filtered):,} in {time.time() - t0:.2f}s')\n\n# Only positive listens: played_ratio_pct >= 50\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\nprint(f'Positive listens count: {len(tl_filtered):,}')\n\n# Recency decay: half-life 60 days (5,184,000 s)\ncutoff = 24786800\nhalflife = 60 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\n# Organic boost\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\n# Completion ratio boost\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\nprint(f'Matrix R created with shape {R.shape}, nnz={R.nnz:,} in {time.time() - t0:.2f}s')\n\n# Item-item cosine similarity:\n# Normalize item columns: S = (R_norm^T * R_norm)\n# Or user-item normalized\n# First, let's normalize item vectors: L2 norm of columns of R\nitem_norms = np.sqrt(np.asarray(R.power(2).sum(axis=0)).ravel()) + 1e-8\n# S = R^T * R with column normalization\n# Since n_items = 30000, R is (8456, 30000)\n# Notice R has 8456 rows!\n# We can do User-User CF or Item-Item CF:\n# For User-User CF:\n# U_sim = R_norm * R_norm^T is only (8456, 8456)!\n# That is MUCH smaller and faster to compute than (30000, 30000)!\n# Let's check: (8456, 8456) has only 71 million elements (280 MB)!\nuser_norms = np.sqrt(np.asarray(R.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R\nprint(f'Normalized R in {time.time() - t0:.2f}s')\n\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T # (8456, 8456)\nprint(f'User similarity computed in {time.time() - t_sim:.2f}s, shape: {U_sim.shape}, nnz: {U_sim.nnz:,}')\n\n# Reconstructed scores: Scores = U_sim * R\nt_pred = time.time()\nScores = U_sim @ R # (8456, 30000) sparse or dense\nprint(f'Scores computed in {time.time() - t_pred:.2f}s, type: {type(Scores)}')\n\n# Let's evaluate!\n# For each user, we need top 100 eligible items\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    # Get user scores as dense array\n    user_scores = Scores[i].toarray().ravel()\n    # Mask ineligible items by setting their score to -infinity\n    user_scores[mask == 0] = -np.inf\n    # Top 100\n    top100_idx = np.argpartition(user_scores, -100)[-100:]\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'User-User CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_365524",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Filtered listens count: 31,062,540 in 8.91s\nPositive listens count: 19,959,853\nMatrix R created with shape (8456, 30000), nnz=5,514,505 in 10.66s\nNormalized R in 10.71s\nUser similarity computed in 6.85s, shape: (8456, 8456), nnz: 63,275,517\nScores computed in 104.23s, type: <class 'scipy.sparse._csr.csr_matrix'>\nUser-User CF NDCG@10: {'quality_ndcg@10': 0.018139, 'quality_users': 4263} in total 123.95s\nProcess Group PGID: 1992\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:25:20.917Z",
            "resultDisplay": "Filtered listens count: 31,062,540 in 8.91s\nPositive listens count: 19,959,853\nMatrix R created with shape (8456, 30000), nnz=5,514,505 in 10.66s\nNormalized R in 10.71s\nUser similarity computed in 6.85s, shape: (8456, 8456), nnz: 63,275,517\nScores computed in 104.23s, type: <class 'scipy.sparse._csr.csr_matrix'>\nUser-User CF NDCG@10: {'quality_ndcg@10': 0.018139, 'quality_users': 4263} in total 123.95s",
            "description": "Test User-User Collaborative Filtering model.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:25:20.918Z",
            "content": "<untrusted_context>\nOutput: Filtered listens count: 31,062,540 in 8.91s\nPositive listens count: 19,959,853\nMatrix R created with shape (8456, 30000), nnz=5,514,505 in 10.66s\nNormalized R in 10.71s\nUser similarity computed in 6.85s, shape: (8456, 8456), nnz: 63,275,517\nScores computed in 104.23s, type: <class 'scipy.sparse._csr.csr_matrix'>\nUser-User CF NDCG@10: {'quality_ndcg@10': 0.018139, 'quality_users': 4263} in total 123.95s\nProcess Group PGID: 1992\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "12fe79bd-f43b-4b27-9be8-1213571134f6"
        }
      ]
    },
    {
      "id": 27,
      "role": "agent",
      "timestamp": "2026-09-29T04:25:32.421Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:25:32.421Z",
          "text": "Optimizing Similarity Calculation\n\nI've seen a substantial jump in User-User CF performance, a nearly 70% improvement. However, the bottleneck is now the dense matrix multiplication `Scores = U_sim @ R`, which is taking a considerable 104 seconds due to the near-density of $U_{sim}$. I'm now focusing on optimizing this dense matrix operation."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:25:32.421Z",
          "callId": "run_shell_command__call_146890",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark TruncatedSVD with TF-IDF transformation.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# BM25 / TF-IDF transformation:\n# IDF for items:\ndf = np.diff(R.tocsc().indptr) # document frequency (number of users who listened to each item)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_tfidf = R @ sp.diags(idf)\n\nprint(f'R_tfidf built in {time.time() - t0:.2f}s')\n\n# Test different SVD components\nfor k in [64, 128, 256]:\n    t_svd = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', n_iter=4, random_state=42)\n    U = svd.fit_transform(R_tfidf) # shape: (n_users, k)\n    V = svd.components_ # shape: (k, n_items)\n    print(f'SVD k={k} fit in {time.time() - t_svd:.2f}s, explained var: {svd.explained_variance_ratio_.sum():.4f}')\n    \n    # Predict all scores: (n_users, k) @ (k, n_items) -> (n_users, n_items)\n    t_pred = time.time()\n    Scores = U @ V\n    print(f'Scores dense dot product in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'SVD k={k} NDCG@10: {scores}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_146890",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark TruncatedSVD with TF-IDF transformation.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# BM25 / TF-IDF transformation:\n# IDF for items:\ndf = np.diff(R.tocsc().indptr) # document frequency (number of users who listened to each item)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_tfidf = R @ sp.diags(idf)\n\nprint(f'R_tfidf built in {time.time() - t0:.2f}s')\n\n# Test different SVD components\nfor k in [64, 128, 256]:\n    t_svd = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', n_iter=4, random_state=42)\n    U = svd.fit_transform(R_tfidf) # shape: (n_users, k)\n    V = svd.components_ # shape: (k, n_items)\n    print(f'SVD k={k} fit in {time.time() - t_svd:.2f}s, explained var: {svd.explained_variance_ratio_.sum():.4f}')\n    \n    # Predict all scores: (n_users, k) @ (k, n_items) -> (n_users, n_items)\n    t_pred = time.time()\n    Scores = U @ V\n    print(f'Scores dense dot product in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'SVD k={k} NDCG@10: {scores}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_146890",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: R_tfidf built in 10.49s\nSVD k=64 fit in 2.96s, explained var: 0.6227\nScores dense dot product in 0.51s\nSVD k=64 NDCG@10: {'quality_ndcg@10': 0.013623, 'quality_users': 4263}\nSVD k=128 fit in 4.79s, explained var: 0.6928\nScores dense dot product in 0.91s\nSVD k=128 NDCG@10: {'quality_ndcg@10': 0.015787, 'quality_users': 4263}\nSVD k=256 fit in 10.77s, explained var: 0.7622\nScores dense dot product in 1.44s\nSVD k=256 NDCG@10: {'quality_ndcg@10': 0.015593, 'quality_users': 4263}\nProcess Group PGID: 2032\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:26:09.113Z",
            "resultDisplay": "R_tfidf built in 10.49s\nSVD k=64 fit in 2.96s, explained var: 0.6227\nScores dense dot product in 0.51s\nSVD k=64 NDCG@10: {'quality_ndcg@10': 0.013623, 'quality_users': 4263}\nSVD k=128 fit in 4.79s, explained var: 0.6928\nScores dense dot product in 0.91s\nSVD k=128 NDCG@10: {'quality_ndcg@10': 0.015787, 'quality_users': 4263}\nSVD k=256 fit in 10.77s, explained var: 0.7622\nScores dense dot product in 1.44s\nSVD k=256 NDCG@10: {'quality_ndcg@10': 0.015593, 'quality_users': 4263}",
            "description": "Benchmark TruncatedSVD with TF-IDF transformation.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:26:09.114Z",
            "content": "<untrusted_context>\nOutput: R_tfidf built in 10.49s\nSVD k=64 fit in 2.96s, explained var: 0.6227\nScores dense dot product in 0.51s\nSVD k=64 NDCG@10: {'quality_ndcg@10': 0.013623, 'quality_users': 4263}\nSVD k=128 fit in 4.79s, explained var: 0.6928\nScores dense dot product in 0.91s\nSVD k=128 NDCG@10: {'quality_ndcg@10': 0.015787, 'quality_users': 4263}\nSVD k=256 fit in 10.77s, explained var: 0.7622\nScores dense dot product in 1.44s\nSVD k=256 NDCG@10: {'quality_ndcg@10': 0.015593, 'quality_users': 4263}\nProcess Group PGID: 2032\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "9108b9e2-3225-4600-977c-f1d4750087c4"
        }
      ]
    },
    {
      "id": 28,
      "role": "agent",
      "timestamp": "2026-09-29T04:26:21.203Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:26:21.203Z",
          "text": "Optimizing Collaborative Filtering\n\nI'm currently exploring ways to enhance collaborative filtering performance. While SVD is remarkably fast, User-User CF, despite its precise neighborhood approach, is proving slow. I'm investigating techniques to significantly reduce its execution time from 100 seconds to just 2 seconds."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:26:21.203Z",
          "callId": "run_shell_command__call_381125",
          "name": "run_shell_command",
          "input": {
            "description": "Test Top-K User-User CF with BM25 item weighting.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n    t_k = time.time()\n    # Filter U_sim to keep only top_k per row\n    # Efficient row-wise top-k\n    U_sim_csr = U_sim.tocsr()\n    rows, cols, data = [], [], []\n    for i in range(n_users):\n        start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n        if end - start <= top_k:\n            cols.extend(U_sim_csr.indices[start:end])\n            data.extend(U_sim_csr.data[start:end])\n            rows.extend([i] * (end - start))\n        else:\n            row_cols = U_sim_csr.indices[start:end]\n            row_vals = U_sim_csr.data[start:end]\n            top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n            cols.extend(row_cols[top_idx])\n            data.extend(row_vals[top_idx])\n            rows.extend([i] * top_k)\n    U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n    print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n    \n    t_pred = time.time()\n    Scores = U_top @ R # or U_top @ R_weighted\n    print(f'Scores computed in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].toarray().ravel()\n        user_scores[mask == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'Top-{top_k} NDCG@10: {scores}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_381125",
            "name": "run_shell_command",
            "args": {
              "description": "Test Top-K User-User CF with BM25 item weighting.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n    t_k = time.time()\n    # Filter U_sim to keep only top_k per row\n    # Efficient row-wise top-k\n    U_sim_csr = U_sim.tocsr()\n    rows, cols, data = [], [], []\n    for i in range(n_users):\n        start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n        if end - start <= top_k:\n            cols.extend(U_sim_csr.indices[start:end])\n            data.extend(U_sim_csr.data[start:end])\n            rows.extend([i] * (end - start))\n        else:\n            row_cols = U_sim_csr.indices[start:end]\n            row_vals = U_sim_csr.data[start:end]\n            top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n            cols.extend(row_cols[top_idx])\n            data.extend(row_vals[top_idx])\n            rows.extend([i] * top_k)\n    U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n    print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n    \n    t_pred = time.time()\n    Scores = U_top @ R # or U_top @ R_weighted\n    print(f'Scores computed in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].toarray().ravel()\n        user_scores[mask == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'Top-{top_k} NDCG@10: {scores}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_381125",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: /usr/bin/bash: line 99:  2068 Killed                  PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n    t_k = time.time()\n    # Filter U_sim to keep only top_k per row\n    # Efficient row-wise top-k\n    U_sim_csr = U_sim.tocsr()\n    rows, cols, data = [], [], []\n    for i in range(n_users):\n        start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n        if end - start <= top_k:\n            cols.extend(U_sim_csr.indices[start:end])\n            data.extend(U_sim_csr.data[start:end])\n            rows.extend([i] * (end - start))\n        else:\n            row_cols = U_sim_csr.indices[start:end]\n            row_vals = U_sim_csr.data[start:end]\n            top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n            cols.extend(row_cols[top_idx])\n            data.extend(row_vals[top_idx])\n            rows.extend([i] * top_k)\n    U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n    print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n    \n    t_pred = time.time()\n    Scores = U_top @ R # or U_top @ R_weighted\n    print(f'Scores computed in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].toarray().ravel()\n        user_scores[mask == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'Top-{top_k} NDCG@10: {scores}')\n\"\nExit Code: 137\nProcess Group PGID: 2066\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:27:14.544Z",
            "resultDisplay": "/usr/bin/bash: line 99:  2068 Killed                  PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n    t_k = time.time()\n    # Filter U_sim to keep only top_k per row\n    # Efficient row-wise top-k\n    U_sim_csr = U_sim.tocsr()\n    rows, cols, data = [], [], []\n    for i in range(n_users):\n        start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n        if end - start <= top_k:\n            cols.extend(U_sim_csr.indices[start:end])\n            data.extend(U_sim_csr.data[start:end])\n            rows.extend([i] * (end - start))\n        else:\n            row_cols = U_sim_csr.indices[start:end]\n            row_vals = U_sim_csr.data[start:end]\n            top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n            cols.extend(row_cols[top_idx])\n            data.extend(row_vals[top_idx])\n            rows.extend([i] * top_k)\n    U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n    print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n    \n    t_pred = time.time()\n    Scores = U_top @ R # or U_top @ R_weighted\n    print(f'Scores computed in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].toarray().ravel()\n        user_scores[mask == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'Top-{top_k} NDCG@10: {scores}')\n\"",
            "description": "Test Top-K User-User CF with BM25 item weighting.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:27:14.550Z",
            "content": "<untrusted_context>\nOutput: /usr/bin/bash: line 99:  2068 Killed                  PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntl = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nmask = tl['item_id'].isin(item_to_idx) & tl['uid'].isin(uid_to_idx)\ntl_filtered = tl[mask].copy()\ntl_filtered = tl_filtered[tl_filtered['played_ratio_pct'] >= 50]\n\ncutoff = 24786800\nhalflife = 45 * 86400\ndt = np.clip(cutoff - tl_filtered['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(tl_filtered['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(tl_filtered['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = tl_filtered['uid'].map(uid_to_idx).to_numpy()\ni_idx = tl_filtered['item_id'].map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((weight, (u_idx, i_idx)), shape=(n_users, n_items))\n\n# Let's apply BM25 item weighting:\ndf = np.diff(R.tocsc().indptr)\nidf = np.log((n_users + 1.0) / (df + 1.0)) + 1.0\nR_weighted = R @ sp.diags(idf)\n\nuser_norms = np.sqrt(np.asarray(R_weighted.power(2).sum(axis=1)).ravel()) + 1e-8\nR_user_norm = sp.diags(1.0 / user_norms) @ R_weighted\n\n# User similarity: U_sim = R_user_norm @ R_user_norm.T\nt_sim = time.time()\nU_sim = R_user_norm @ R_user_norm.T\n# Zero out diagonal\nU_sim.setdiag(0)\nprint(f'User sim computed in {time.time() - t_sim:.2f}s')\n\n# Test different top_K neighbor counts\nfor top_k in [50, 100, 200, 500]:\n    t_k = time.time()\n    # Filter U_sim to keep only top_k per row\n    # Efficient row-wise top-k\n    U_sim_csr = U_sim.tocsr()\n    rows, cols, data = [], [], []\n    for i in range(n_users):\n        start, end = U_sim_csr.indptr[i], U_sim_csr.indptr[i+1]\n        if end - start <= top_k:\n            cols.extend(U_sim_csr.indices[start:end])\n            data.extend(U_sim_csr.data[start:end])\n            rows.extend([i] * (end - start))\n        else:\n            row_cols = U_sim_csr.indices[start:end]\n            row_vals = U_sim_csr.data[start:end]\n            top_idx = np.argpartition(row_vals, -top_k)[-top_k:]\n            cols.extend(row_cols[top_idx])\n            data.extend(row_vals[top_idx])\n            rows.extend([i] * top_k)\n    U_top = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_users))\n    print(f'Top-{top_k} sparse matrix built in {time.time() - t_k:.2f}s, nnz={U_top.nnz:,}')\n    \n    t_pred = time.time()\n    Scores = U_top @ R # or U_top @ R_weighted\n    print(f'Scores computed in {time.time() - t_pred:.2f}s')\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].toarray().ravel()\n        user_scores[mask == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'Top-{top_k} NDCG@10: {scores}')\n\"\nExit Code: 137\nProcess Group PGID: 2066\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "996d03a1-5e64-4f4e-968d-ed3b6c6b79cd"
        }
      ]
    },
    {
      "id": 29,
      "role": "agent",
      "timestamp": "2026-09-29T04:27:20.458Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:27:20.458Z",
          "text": "Investigating Crashes\n\nI'm currently looking into the `Killed` exit code (137). It seems to happen after reading a large parquet file and performing some matrix operations on user similarity. I suspect an out-of-memory issue given the scale of the data."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:27:20.458Z",
          "callId": "run_shell_command__call_147686",
          "name": "run_shell_command",
          "input": {
            "description": "Test pyarrow pushed-down predicate filter on train_listens.",
            "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\nprint(f'Read filtered table in {time.time() - t0:.2f}s, rows: {table.num_rows:,}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_147686",
            "name": "run_shell_command",
            "args": {
              "description": "Test pyarrow pushed-down predicate filter on train_listens.",
              "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\nprint(f'Read filtered table in {time.time() - t0:.2f}s, rows: {table.num_rows:,}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_147686",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Read filtered table in 0.75s, rows: 27,500,755\nProcess Group PGID: 2094\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:27:21.783Z",
            "resultDisplay": "Read filtered table in 0.75s, rows: 27,500,755",
            "description": "Test pyarrow pushed-down predicate filter on train_listens.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:27:21.784Z",
            "content": "<untrusted_context>\nOutput: Read filtered table in 0.75s, rows: 27,500,755\nProcess Group PGID: 2094\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "c156bb78-651d-4255-8dff-c0b2db8957a5"
        }
      ]
    },
    {
      "id": 30,
      "role": "agent",
      "timestamp": "2026-09-29T04:27:25.526Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:27:25.526Z",
          "text": "Analyzing Data Speed\n\nMy recent focus has been on optimizing data filtering. I'm pleased to report that applying a played ratio filter processed 16 million rows in a mere 0.75 seconds. I'm now exploring the impact of incorporating timestamp filtering."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:27:25.526Z",
          "callId": "run_shell_command__call_209133",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nfor days in [30, 60, 90, 120, 180]:\n    t_thresh = cutoff - days * 86400\n    table = pq.read_table('/task/data/train_listens.parquet',\n                          columns=['timestamp'],\n                          filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_thresh)])\n    print(f'Listens >= 50% in last {days:3d} days: {table.num_rows:,}')\n\"",
            "description": "Check listens count for different recency windows."
          },
          "recorded": {
            "id": "run_shell_command__call_209133",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nfor days in [30, 60, 90, 120, 180]:\n    t_thresh = cutoff - days * 86400\n    table = pq.read_table('/task/data/train_listens.parquet',\n                          columns=['timestamp'],\n                          filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_thresh)])\n    print(f'Listens >= 50% in last {days:3d} days: {table.num_rows:,}')\n\"",
              "description": "Check listens count for different recency windows."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_209133",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Listens >= 50% in last  30 days: 3,883,797\nListens >= 50% in last  60 days: 7,417,190\nListens >= 50% in last  90 days: 10,582,861\nListens >= 50% in last 120 days: 13,510,103\nListens >= 50% in last 180 days: 19,038,702\nProcess Group PGID: 2122\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:27:27.426Z",
            "resultDisplay": "Listens >= 50% in last  30 days: 3,883,797\nListens >= 50% in last  60 days: 7,417,190\nListens >= 50% in last  90 days: 10,582,861\nListens >= 50% in last 120 days: 13,510,103\nListens >= 50% in last 180 days: 19,038,702",
            "description": "Check listens count for different recency windows.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:27:27.427Z",
            "content": "<untrusted_context>\nOutput: Listens >= 50% in last  30 days: 3,883,797\nListens >= 50% in last  60 days: 7,417,190\nListens >= 50% in last  90 days: 10,582,861\nListens >= 50% in last 120 days: 13,510,103\nListens >= 50% in last 180 days: 19,038,702\nProcess Group PGID: 2122\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "a5009134-17be-4642-ac5a-14ebfbf862bf"
        }
      ]
    },
    {
      "id": 31,
      "role": "agent",
      "timestamp": "2026-09-29T04:27:40.025Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:27:40.025Z",
          "text": "Analyzing Listen Metrics\n\nMy current focus is on understanding user engagement trends by examining listen counts over different periods. I'm comparing the last 90 days (10.5M listens) to the last 60 days (7.4M listens) to identify any shifts in listening patterns. This data will inform the definition of \"target users,\" specifically those with a minimum of 20 positive listens in the 90 days preceding training."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:27:40.025Z",
          "text": "Refining Similarity Calculations\n\nI'm shifting my approach from user-user to item-item collaborative filtering to avoid memory issues. Computing a dense item similarity matrix, and then pruning it to the top-K similar items per item, should drastically reduce memory overhead, bringing the non-zero entries down to manageable levels."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:27:40.025Z",
          "text": "Testing Item-Item Similarity\n\nI'm exploring the feasibility of an item-item collaborative filtering approach by calculating the full item similarity matrix. Specifically, I'm examining if normalizing the user-item interaction matrix and computing its transpose product offers an efficient path to generating this matrix, aiming to leverage its proven effectiveness in recommendation systems."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:27:40.025Z",
          "callId": "run_shell_command__call_291661",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create dummy sparse matrix (8456, 30000) with 3M nonzeros\nR = sp.random(8456, 30000, density=0.01, format='csc', dtype=np.float32)\nt0 = time.time()\n# compute 2000 items block:\nblock = R[:, :2000] # (8456, 2000)\nsim_block = (R.T @ block).toarray() # (30000, 2000)\nt1 = time.time()\nprint(f'Block sim shape: {sim_block.shape}, computed in {t1 - t0:.2f}s')\n\"",
            "description": "Benchmark sparse block matrix multiplication for item similarity."
          },
          "recorded": {
            "id": "run_shell_command__call_291661",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create dummy sparse matrix (8456, 30000) with 3M nonzeros\nR = sp.random(8456, 30000, density=0.01, format='csc', dtype=np.float32)\nt0 = time.time()\n# compute 2000 items block:\nblock = R[:, :2000] # (8456, 2000)\nsim_block = (R.T @ block).toarray() # (30000, 2000)\nt1 = time.time()\nprint(f'Block sim shape: {sim_block.shape}, computed in {t1 - t0:.2f}s')\n\"",
              "description": "Benchmark sparse block matrix multiplication for item similarity."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_291661",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Block sim shape: (30000, 2000), computed in 0.64s\nProcess Group PGID: 2146\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:27:51.895Z",
            "resultDisplay": "Block sim shape: (30000, 2000), computed in 0.64s",
            "description": "Benchmark sparse block matrix multiplication for item similarity.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:27:53.276Z",
            "content": "<untrusted_context>\nOutput: Block sim shape: (30000, 2000), computed in 0.64s\nProcess Group PGID: 2146\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "5772a9e0-cc65-482f-b767-f3efa1f791bc"
        }
      ]
    },
    {
      "id": 32,
      "role": "agent",
      "timestamp": "2026-09-29T04:28:09.131Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:28:09.131Z",
          "text": "Optimizing performance and memory\n\nI've been evaluating the efficiency of processing 2,000 items in 0.64 seconds, projecting that all 30,000 items will take roughly 10 seconds. Memory usage is also within acceptable limits at around 240 MB. My current focus is on scaling this with top-K selection, aiming for a sparse similarity matrix $S$ of $30,000 \\times 100$."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:28:09.131Z",
          "callId": "run_shell_command__call_154683",
          "name": "run_shell_command",
          "input": {
            "description": "Run Item-Item CF with block similarity computation and evaluate on validation.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Read train listens with filters: played_ratio_pct >= 50\ncutoff = 24786800\n# Let's take listens from the last 120 days\nt_min = cutoff - 120 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_min)])\ndf_tl = table.to_pandas()\nprint(f'Read {len(df_tl):,} listens in {time.time() - t0:.2f}s')\n\n# Filter to catalog and target users\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\nprint(f'Filtered to {len(df_tl):,} listens')\n\n# Calculate weights\nhalflife = 45 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\n# Group by (uid, item_id) to sum weights (repeat listens indicate strong preference!)\n# Using sparse matrix directly sums duplicates:\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes!\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\nlikes_w = np.full(len(likes), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u])\nall_i = np.concatenate([i_idx, likes_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed: nnz={R.nnz:,}')\n\n# Item-Item Cosine Similarity with BM25 / TF-IDF\n# Item document frequencies:\nitem_freq = np.diff(R.tocsc().indptr)\n# BM25 IDF:\nidf = np.log((n_users - item_freq + 0.5) / (item_freq + 0.5) + 1.0).astype(np.float32)\nR_tfidf = R @ sp.diags(idf)\n\n# Column normalize R_tfidf for cosine similarity\nR_csc = R_tfidf.tocsc()\ncol_norms = np.sqrt(np.asarray(R_csc.power(2).sum(axis=0)).ravel()) + 1e-6\nR_norm = (R_csc @ sp.diags(1.0 / col_norms)).tocsc()\n\nprint(f'Computing Item similarity matrix in blocks...')\nt_sim = time.time()\ntop_k = 100\nblock_size = 2000\nrows, cols, data = [], [], []\n\nfor b_start in range(0, n_items, block_size):\n    b_end = min(b_start + block_size, n_items)\n    # block is (n_users, b_end - b_start)\n    block = R_norm[:, b_start:b_end]\n    # dot product with all items: (n_items, n_users) @ (n_users, b_len) -> (n_items, b_len)\n    sim = (R_norm.T @ block).toarray()\n    \n    # Zero out diagonal\n    for j in range(b_end - b_start):\n        sim[b_start + j, j] = 0.0\n    \n    # Extract top_k for each column\n    for j in range(b_end - b_start):\n        col_vals = sim[:, j]\n        # find top_k indices\n        top_indices = np.argpartition(col_vals, -top_k)[-top_k:]\n        top_vals = col_vals[top_indices]\n        # filter out zero or negative similarities\n        pos_mask = top_vals > 0\n        if np.any(pos_mask):\n            rows.extend(top_indices[pos_mask])\n            cols.extend([b_start + j] * pos_mask.sum())\n            data.extend(top_vals[pos_mask])\n\nS = sp.csc_matrix((data, (rows, cols)), shape=(n_items, n_items), dtype=np.float32).tocsr()\nprint(f'Item similarity S constructed in {time.time() - t_sim:.2f}s, nnz={S.nnz:,}')\n\n# Predict scores: Scores = R * S\nt_pred = time.time()\nScores = (R @ S).toarray()\nprint(f'Prediction Scores computed in {time.time() - t_pred:.2f}s, shape: {Scores.shape}')\n\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    user_scores = Scores[i].copy()\n    user_scores[mask == 0] = -1e9\n    top100_idx = np.argpartition(user_scores, -100)[-100:]\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'Item-Item CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_154683",
            "name": "run_shell_command",
            "args": {
              "description": "Run Item-Item CF with block similarity computation and evaluate on validation.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Read train listens with filters: played_ratio_pct >= 50\ncutoff = 24786800\n# Let's take listens from the last 120 days\nt_min = cutoff - 120 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50), ('timestamp', '>=', t_min)])\ndf_tl = table.to_pandas()\nprint(f'Read {len(df_tl):,} listens in {time.time() - t0:.2f}s')\n\n# Filter to catalog and target users\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\nprint(f'Filtered to {len(df_tl):,} listens')\n\n# Calculate weights\nhalflife = 45 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\n# Group by (uid, item_id) to sum weights (repeat listens indicate strong preference!)\n# Using sparse matrix directly sums duplicates:\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes!\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\nlikes_w = np.full(len(likes), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u])\nall_i = np.concatenate([i_idx, likes_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed: nnz={R.nnz:,}')\n\n# Item-Item Cosine Similarity with BM25 / TF-IDF\n# Item document frequencies:\nitem_freq = np.diff(R.tocsc().indptr)\n# BM25 IDF:\nidf = np.log((n_users - item_freq + 0.5) / (item_freq + 0.5) + 1.0).astype(np.float32)\nR_tfidf = R @ sp.diags(idf)\n\n# Column normalize R_tfidf for cosine similarity\nR_csc = R_tfidf.tocsc()\ncol_norms = np.sqrt(np.asarray(R_csc.power(2).sum(axis=0)).ravel()) + 1e-6\nR_norm = (R_csc @ sp.diags(1.0 / col_norms)).tocsc()\n\nprint(f'Computing Item similarity matrix in blocks...')\nt_sim = time.time()\ntop_k = 100\nblock_size = 2000\nrows, cols, data = [], [], []\n\nfor b_start in range(0, n_items, block_size):\n    b_end = min(b_start + block_size, n_items)\n    # block is (n_users, b_end - b_start)\n    block = R_norm[:, b_start:b_end]\n    # dot product with all items: (n_items, n_users) @ (n_users, b_len) -> (n_items, b_len)\n    sim = (R_norm.T @ block).toarray()\n    \n    # Zero out diagonal\n    for j in range(b_end - b_start):\n        sim[b_start + j, j] = 0.0\n    \n    # Extract top_k for each column\n    for j in range(b_end - b_start):\n        col_vals = sim[:, j]\n        # find top_k indices\n        top_indices = np.argpartition(col_vals, -top_k)[-top_k:]\n        top_vals = col_vals[top_indices]\n        # filter out zero or negative similarities\n        pos_mask = top_vals > 0\n        if np.any(pos_mask):\n            rows.extend(top_indices[pos_mask])\n            cols.extend([b_start + j] * pos_mask.sum())\n            data.extend(top_vals[pos_mask])\n\nS = sp.csc_matrix((data, (rows, cols)), shape=(n_items, n_items), dtype=np.float32).tocsr()\nprint(f'Item similarity S constructed in {time.time() - t_sim:.2f}s, nnz={S.nnz:,}')\n\n# Predict scores: Scores = R * S\nt_pred = time.time()\nScores = (R @ S).toarray()\nprint(f'Prediction Scores computed in {time.time() - t_pred:.2f}s, shape: {Scores.shape}')\n\nval_ranks = {}\nfor i, uid in enumerate(target_uids):\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    user_scores = Scores[i].copy()\n    user_scores[mask == 0] = -1e9\n    top100_idx = np.argpartition(user_scores, -100)[-100:]\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    val_ranks[uid] = item_ids[top100_idx].tolist()\n\nscores = score_rankings(val_ranks, graded)\nprint(f'Item-Item CF NDCG@10: {scores} in total {time.time() - t0:.2f}s')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_154683",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Read 13,510,103 listens in 1.05s\nFiltered to 9,917,232 listens\nR matrix constructed: nnz=3,642,376\nComputing Item similarity matrix in blocks...\nItem similarity S constructed in 22.22s, nnz=2,997,192\nPrediction Scores computed in 3.22s, shape: (8456, 30000)\nItem-Item CF NDCG@10: {'quality_ndcg@10': 0.014436, 'quality_users': 4263} in total 32.65s\nProcess Group PGID: 2163\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:28:42.998Z",
            "resultDisplay": "Read 13,510,103 listens in 1.05s\nFiltered to 9,917,232 listens\nR matrix constructed: nnz=3,642,376\nComputing Item similarity matrix in blocks...\nItem similarity S constructed in 22.22s, nnz=2,997,192\nPrediction Scores computed in 3.22s, shape: (8456, 30000)\nItem-Item CF NDCG@10: {'quality_ndcg@10': 0.014436, 'quality_users': 4263} in total 32.65s",
            "description": "Run Item-Item CF with block similarity computation and evaluate on validation.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:28:42.999Z",
            "content": "<untrusted_context>\nOutput: Read 13,510,103 listens in 1.05s\nFiltered to 9,917,232 listens\nR matrix constructed: nnz=3,642,376\nComputing Item similarity matrix in blocks...\nItem similarity S constructed in 22.22s, nnz=2,997,192\nPrediction Scores computed in 3.22s, shape: (8456, 30000)\nItem-Item CF NDCG@10: {'quality_ndcg@10': 0.014436, 'quality_users': 4263} in total 32.65s\nProcess Group PGID: 2163\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "11806c5b-ee7c-4041-9688-f579dd4ce87d"
        }
      ]
    },
    {
      "id": 33,
      "role": "agent",
      "timestamp": "2026-09-29T04:28:55.156Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:28:55.156Z",
          "text": "Analyzing User-User CF\n\nMy recent focus has been on the unexpected but excellent performance of User-User Collaborative Filtering. I'm digging into why it's outperforming other methods, particularly the baseline and SVD, in this music streaming context, suspecting genre specificity plays a role."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:28:55.156Z",
          "text": "Exploring User-User CF Performance\n\nI'm keenly observing the strong results from User-User CF, particularly its effectiveness in identifying niche listening personas. I'm now investigating the computational aspects, specifically exploring efficient matrix multiplication strategies for the similarity matrix and user-item interaction matrix, aiming to optimize performance."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:28:55.156Z",
          "callId": "run_shell_command__call_229944",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# R_dense (8456, 30000) would be 1 GB.\n# We can do chunks of columns of R!\n# E.g. 5000 columns at a time: (8456, 5000) is 169 MB!\nB_chunk = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B_chunk\nt1 = time.time()\nprint(f'Chunk (8456, 8456) @ (8456, 5000) computed in {t1 - t0:.2f}s')\n\"",
            "description": "Benchmark chunked dense matrix multiplication."
          },
          "recorded": {
            "id": "run_shell_command__call_229944",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# R_dense (8456, 30000) would be 1 GB.\n# We can do chunks of columns of R!\n# E.g. 5000 columns at a time: (8456, 5000) is 169 MB!\nB_chunk = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B_chunk\nt1 = time.time()\nprint(f'Chunk (8456, 8456) @ (8456, 5000) computed in {t1 - t0:.2f}s')\n\"",
              "description": "Benchmark chunked dense matrix multiplication."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_229944",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Chunk (8456, 8456) @ (8456, 5000) computed in 3.39s\nProcess Group PGID: 2198\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:29:01.147Z",
            "resultDisplay": "Chunk (8456, 8456) @ (8456, 5000) computed in 3.39s",
            "description": "Benchmark chunked dense matrix multiplication.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:29:01.148Z",
            "content": "<untrusted_context>\nOutput: Chunk (8456, 8456) @ (8456, 5000) computed in 3.39s\nProcess Group PGID: 2198\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "6cd08b62-c08f-411b-a945-ef2ce0929dc1"
        }
      ]
    },
    {
      "id": 34,
      "role": "agent",
      "timestamp": "2026-09-29T04:29:16.221Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:29:16.221Z",
          "text": "Optimizing Performance Now\n\nI'm thrilled with these performance metrics. Processing 5,000 columns in under 4 seconds is excellent. Scaling this to 30,000 items across six chunks is projected to take just 20 seconds, with a modest memory footprint of 169MB per chunk. I'm also exploring the impact of non-linear transformations on $U_{sim}$."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:29:16.221Z",
          "text": "Refining Similarity Calculations\n\nI'm now focusing on refining similarity metrics. I'm experimenting with non-linear transformations, specifically exponentiating $U_{sim}$ to emphasize closer relationships. I'm also incorporating user feedback signals, considering all listen history with exponential decay, and evaluating BM25/TF-IDF on the interaction matrix. Further exploration involves combining user-user collaborative filtering with item popularity or SVD, and I'm developing a Python script to measure validation NDCG@10 across these variations."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:29:16.221Z",
          "callId": "run_shell_command__call_293158",
          "name": "run_shell_command",
          "input": {
            "description": "Test power alpha on User-User CF with explicit preference state.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Calculate weights with recency decay\nhalflife = 60 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes and validation_preference_state\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n# Likes weight: recency decay as well\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\n# Active preference state liked tracks\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed in {time.time() - t0:.2f}s: shape={R.shape}, nnz={R.nnz:,}')\n\n# BM25 / TF-IDF transformation:\n# Item document frequency\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\n# User cosine normalization\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\n# Compute dense user similarity matrix\nt_sim = time.time()\nU_sim = (R_user_norm @ R_user_norm.T).toarray() # (8456, 8456)\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'User similarity matrix computed in {time.time() - t_sim:.2f}s, min={U_sim.min():.4f}, max={U_sim.max():.4f}, mean={U_sim.mean():.4f}')\n\n# Test power alpha\nfor alpha in [1.0, 1.5, 2.0, 3.0]:\n    t_test = time.time()\n    # Apply power: sign(U_sim) * |U_sim|^alpha\n    U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n    # Threshold out negative or near-zero similarities\n    U_pow[U_pow < 0] = 0.0\n    \n    # Compute scores in chunks of items\n    Scores = np.zeros((n_users, n_items), dtype=np.float32)\n    chunk_size = 5000\n    for c_start in range(0, n_items, chunk_size):\n        c_end = min(c_start + chunk_size, n_items)\n        R_chunk = R_tfidf[:, c_start:c_end].toarray()\n        Scores[:, c_start:c_end] = U_pow @ R_chunk\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'User-User CF (alpha={alpha:.1f}) NDCG@10: {scores} in {time.time() - t_test:.2f}s')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_293158",
            "name": "run_shell_command",
            "args": {
              "description": "Test power alpha on User-User CF with explicit preference state.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Load train listens\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Calculate weights with recency decay\nhalflife = 60 * 86400\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\n# Also add explicit likes from train_likes and validation_preference_state\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n# Likes weight: recency decay as well\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\n# Active preference state liked tracks\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'R matrix constructed in {time.time() - t0:.2f}s: shape={R.shape}, nnz={R.nnz:,}')\n\n# BM25 / TF-IDF transformation:\n# Item document frequency\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\n# User cosine normalization\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\n# Compute dense user similarity matrix\nt_sim = time.time()\nU_sim = (R_user_norm @ R_user_norm.T).toarray() # (8456, 8456)\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'User similarity matrix computed in {time.time() - t_sim:.2f}s, min={U_sim.min():.4f}, max={U_sim.max():.4f}, mean={U_sim.mean():.4f}')\n\n# Test power alpha\nfor alpha in [1.0, 1.5, 2.0, 3.0]:\n    t_test = time.time()\n    # Apply power: sign(U_sim) * |U_sim|^alpha\n    U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n    # Threshold out negative or near-zero similarities\n    U_pow[U_pow < 0] = 0.0\n    \n    # Compute scores in chunks of items\n    Scores = np.zeros((n_users, n_items), dtype=np.float32)\n    chunk_size = 5000\n    for c_start in range(0, n_items, chunk_size):\n        c_end = min(c_start + chunk_size, n_items)\n        R_chunk = R_tfidf[:, c_start:c_end].toarray()\n        Scores[:, c_start:c_end] = U_pow @ R_chunk\n    \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n    \n    scores = score_rankings(val_ranks, graded)\n    print(f'User-User CF (alpha={alpha:.1f}) NDCG@10: {scores} in {time.time() - t_test:.2f}s')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_293158",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: R matrix constructed in 7.80s: shape=(8456, 30000), nnz=5,564,723\nUser similarity matrix computed in 6.52s, min=0.0000, max=0.9947, mean=0.0114\nUser-User CF (alpha=1.0) NDCG@10: {'quality_ndcg@10': 0.027657, 'quality_users': 4263} in 21.83s\nUser-User CF (alpha=1.5) NDCG@10: {'quality_ndcg@10': 0.029169, 'quality_users': 4263} in 22.35s\nUser-User CF (alpha=2.0) NDCG@10: {'quality_ndcg@10': 0.028259, 'quality_users': 4263} in 21.86s\nUser-User CF (alpha=3.0) NDCG@10: {'quality_ndcg@10': 0.021735, 'quality_users': 4263} in 22.36s\nProcess Group PGID: 2208\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:31:00.244Z",
            "resultDisplay": "R matrix constructed in 7.80s: shape=(8456, 30000), nnz=5,564,723\nUser similarity matrix computed in 6.52s, min=0.0000, max=0.9947, mean=0.0114\nUser-User CF (alpha=1.0) NDCG@10: {'quality_ndcg@10': 0.027657, 'quality_users': 4263} in 21.83s\nUser-User CF (alpha=1.5) NDCG@10: {'quality_ndcg@10': 0.029169, 'quality_users': 4263} in 22.35s\nUser-User CF (alpha=2.0) NDCG@10: {'quality_ndcg@10': 0.028259, 'quality_users': 4263} in 21.86s\nUser-User CF (alpha=3.0) NDCG@10: {'quality_ndcg@10': 0.021735, 'quality_users': 4263} in 22.36s",
            "description": "Test power alpha on User-User CF with explicit preference state.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:31:00.245Z",
            "content": "<untrusted_context>\nOutput: R matrix constructed in 7.80s: shape=(8456, 30000), nnz=5,564,723\nUser similarity matrix computed in 6.52s, min=0.0000, max=0.9947, mean=0.0114\nUser-User CF (alpha=1.0) NDCG@10: {'quality_ndcg@10': 0.027657, 'quality_users': 4263} in 21.83s\nUser-User CF (alpha=1.5) NDCG@10: {'quality_ndcg@10': 0.029169, 'quality_users': 4263} in 22.35s\nUser-User CF (alpha=2.0) NDCG@10: {'quality_ndcg@10': 0.028259, 'quality_users': 4263} in 21.86s\nUser-User CF (alpha=3.0) NDCG@10: {'quality_ndcg@10': 0.021735, 'quality_users': 4263} in 22.36s\nProcess Group PGID: 2208\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "271a0ead-819b-4de2-8588-36bcb22da6fd"
        }
      ]
    },
    {
      "id": 35,
      "role": "agent",
      "timestamp": "2026-09-29T04:31:44.399Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:31:44.399Z",
          "text": "Analyzing Performance Gains\n\nI'm reviewing the recent surge in NDCG@10, nearly tripling from 0.01071 to 0.029169. My focus now is dissecting the contributors: recency decay, completion/organic flags, explicit likes and active preference states, and the BM25 IDF item."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:31:44.399Z",
          "text": "Investigating Negative Signals\n\nI'm exploring the impact of negative signals, specifically dislikes. I'm considering how to incorporate explicit dislikes and disliked items from similar users. Additionally, I'm evaluating the sensitivity of performance to the halflife parameter, explicit like weights, and potential blending strategies with other recommendation approaches like Item-Item CF."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:31:44.399Z",
          "callId": "run_shell_command__call_150371",
          "name": "run_shell_command",
          "input": {
            "description": "Tune halflife, log1p transformation, and alpha.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nfor halflife_days in [30, 45, 60, 90]:\n    halflife = halflife_days * 86400\n    dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n    decay = np.exp(-np.log(2) * dt / halflife)\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n    likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    \n    # Sublinear scaling: log(1 + R)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    \n    for alpha in [1.3, 1.5, 1.7]:\n        U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n        U_pow[U_pow < 0] = 0.0\n        \n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        chunk_size = 5000\n        for c_start in range(0, n_items, chunk_size):\n            c_end = min(c_start + chunk_size, n_items)\n            Scores[:, c_start:c_end] = U_pow @ R_tfidf[:, c_start:c_end].toarray()\n        \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n        scores = score_rankings(val_ranks, graded)\n        print(f'Halflife {halflife_days:2d}d, log1p, alpha={alpha:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_150371",
            "name": "run_shell_command",
            "args": {
              "description": "Tune halflife, log1p transformation, and alpha.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nfor halflife_days in [30, 45, 60, 90]:\n    halflife = halflife_days * 86400\n    dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n    decay = np.exp(-np.log(2) * dt / halflife)\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n    likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    \n    # Sublinear scaling: log(1 + R)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    \n    for alpha in [1.3, 1.5, 1.7]:\n        U_pow = np.sign(U_sim) * (np.abs(U_sim) ** alpha)\n        U_pow[U_pow < 0] = 0.0\n        \n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        chunk_size = 5000\n        for c_start in range(0, n_items, chunk_size):\n            c_end = min(c_start + chunk_size, n_items)\n            Scores[:, c_start:c_end] = U_pow @ R_tfidf[:, c_start:c_end].toarray()\n        \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n        scores = score_rankings(val_ranks, graded)\n        print(f'Halflife {halflife_days:2d}d, log1p, alpha={alpha:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_150371",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nCommand was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:36:44.629Z",
            "resultDisplay": "Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output.",
            "description": "Tune halflife, log1p transformation, and alpha.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:36:44.630Z",
            "content": "<untrusted_context>\nCommand was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "3582bfc9-558c-4f90-946b-c3c166d2b36d"
        }
      ]
    },
    {
      "id": 36,
      "role": "agent",
      "timestamp": "2026-09-29T04:36:54.942Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:36:54.942Z",
          "text": "Optimizing the Loop\n\nI'm focusing on enhancing the prediction loop's efficiency. My current efforts are directed at streamlining the nested computations that led to the timeout, specifically addressing the matrix multiplications that consume significant processing time."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:36:54.942Z",
          "callId": "run_shell_command__call_183449",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU_top = sp.random(8456, 8456, density=150.0/8456, format='csr', dtype=np.float32)\nR = sp.random(8456, 30000, density=0.015, format='csr', dtype=np.float32)\n\nt0 = time.time()\nScores = U_top @ R\nt1 = time.time()\nprint(f'Scores sparse matrix product shape: {Scores.shape}, nnz: {Scores.nnz:,}, computed in {t1 - t0:.2f}s')\n\"",
            "description": "Benchmark sparse-sparse matrix multiplication with top-K neighbors."
          },
          "recorded": {
            "id": "run_shell_command__call_183449",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU_top = sp.random(8456, 8456, density=150.0/8456, format='csr', dtype=np.float32)\nR = sp.random(8456, 30000, density=0.015, format='csr', dtype=np.float32)\n\nt0 = time.time()\nScores = U_top @ R\nt1 = time.time()\nprint(f'Scores sparse matrix product shape: {Scores.shape}, nnz: {Scores.nnz:,}, computed in {t1 - t0:.2f}s')\n\"",
              "description": "Benchmark sparse-sparse matrix multiplication with top-K neighbors."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_183449",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Scores sparse matrix product shape: (8456, 30000), nnz: 226,948,381, computed in 5.54s\nProcess Group PGID: 2303\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:37:14.660Z",
            "resultDisplay": "Scores sparse matrix product shape: (8456, 30000), nnz: 226,948,381, computed in 5.54s",
            "description": "Benchmark sparse-sparse matrix multiplication with top-K neighbors.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:37:14.662Z",
            "content": "<untrusted_context>\nOutput: Scores sparse matrix product shape: (8456, 30000), nnz: 226,948,381, computed in 5.54s\nProcess Group PGID: 2303\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "3ff5a698-29d0-4a1c-99c1-7adda88342ba"
        }
      ]
    },
    {
      "id": 37,
      "role": "agent",
      "timestamp": "2026-09-29T04:37:28.849Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:37:28.849Z",
          "text": "Testing Neighbor Influence\n\nI'm currently analyzing the impact of neighbor selection strategies on collaborative filtering accuracy. My focus is on whether limiting neighbors to a top-K set ($K=100, 150$) yields better NDCG scores than considering all neighbors, and initial observations suggest a strong positive correlation with top-K approaches."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:37:28.849Z",
          "callId": "run_shell_command__call_233101",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark top_K user neighbors vs full dense user matrix.",
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nR.data = np.log1p(R.data)\n\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\nU_sim = (R_user_norm @ R_user_norm.T).toarray()\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'Computed U_sim in {time.time() - t0:.2f}s')\n\nfor top_k in [100, 200, 400, None]:\n    t_test = time.time()\n    if top_k is not None:\n        # Keep top_k per row in U_sim\n        idx = np.argpartition(U_sim, -top_k, axis=1)[:, -top_k:]\n        rows = np.repeat(np.arange(n_users), top_k)\n        cols = idx.ravel()\n        vals = U_sim[rows, cols]\n        vals = np.clip(vals, 0, None) ** 1.5\n        U_sparse = sp.csr_matrix((vals, (rows, cols)), shape=(n_users, n_users), dtype=np.float32)\n        Scores = (U_sparse @ R_tfidf).toarray()\n    else:\n        U_pow = np.clip(U_sim, 0, None) ** 1.5\n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n            \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n    scores = score_rankings(val_ranks, graded)\n    print(f'top_k={top_k} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f} in {time.time() - t_test:.2f}s')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_233101",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark top_K user neighbors vs full dense user matrix.",
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\nweight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, 1.5, 1.0)\nweight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n\nu_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\ni_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\nlikes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\nvpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx, likes_u, vpref_u])\nall_i = np.concatenate([i_idx, likes_i, vpref_i])\nall_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n\nR = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nR.data = np.log1p(R.data)\n\ndf_item = np.diff(R.tocsc().indptr)\nidf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\nR_tfidf = (R @ sp.diags(idf)).tocsr()\n\nuser_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\nR_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n\nU_sim = (R_user_norm @ R_user_norm.T).toarray()\nnp.fill_diagonal(U_sim, 0.0)\nprint(f'Computed U_sim in {time.time() - t0:.2f}s')\n\nfor top_k in [100, 200, 400, None]:\n    t_test = time.time()\n    if top_k is not None:\n        # Keep top_k per row in U_sim\n        idx = np.argpartition(U_sim, -top_k, axis=1)[:, -top_k:]\n        rows = np.repeat(np.arange(n_users), top_k)\n        cols = idx.ravel()\n        vals = U_sim[rows, cols]\n        vals = np.clip(vals, 0, None) ** 1.5\n        U_sparse = sp.csr_matrix((vals, (rows, cols)), shape=(n_users, n_users), dtype=np.float32)\n        Scores = (U_sparse @ R_tfidf).toarray()\n    else:\n        U_pow = np.clip(U_sim, 0, None) ** 1.5\n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n            \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n    scores = score_rankings(val_ranks, graded)\n    print(f'top_k={top_k} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f} in {time.time() - t_test:.2f}s')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_233101",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Computed U_sim in 14.57s\ntop_k=100 -> NDCG@10: 0.029285 in 6.17s\ntop_k=200 -> NDCG@10: 0.030562 in 8.50s\ntop_k=400 -> NDCG@10: 0.031580 in 12.49s\ntop_k=None -> NDCG@10: 0.032413 in 22.33s\nProcess Group PGID: 2320\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:38:34.003Z",
            "resultDisplay": "Computed U_sim in 14.57s\ntop_k=100 -> NDCG@10: 0.029285 in 6.17s\ntop_k=200 -> NDCG@10: 0.030562 in 8.50s\ntop_k=400 -> NDCG@10: 0.031580 in 12.49s\ntop_k=None -> NDCG@10: 0.032413 in 22.33s",
            "description": "Benchmark top_K user neighbors vs full dense user matrix.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:38:34.004Z",
            "content": "<untrusted_context>\nOutput: Computed U_sim in 14.57s\ntop_k=100 -> NDCG@10: 0.029285 in 6.17s\ntop_k=200 -> NDCG@10: 0.030562 in 8.50s\ntop_k=400 -> NDCG@10: 0.031580 in 12.49s\ntop_k=None -> NDCG@10: 0.032413 in 22.33s\nProcess Group PGID: 2320\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "7846b745-34b4-4c1a-9a7d-9625b3725ddc"
        }
      ]
    },
    {
      "id": 38,
      "role": "agent",
      "timestamp": "2026-09-29T04:39:06.437Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:39:06.437Z",
          "text": "Improving NDCG Score\n\nI've achieved a significant improvement, with NDCG@10 now at 0.032413, more than tripling the baseline. This jump was primarily driven by incorporating `np.log1p(R.data)` and utilizing `top_k=None` with soft-thresholding."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:39:06.437Z",
          "callId": "run_shell_command__call_191743",
          "name": "run_shell_command",
          "input": {
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Compare organic multiplier: 1.5 vs 2.0 vs 2.5\nfor org_mult in [1.5, 2.0, 2.5]:\n    dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n    decay = np.exp(-np.log(2) * dt / halflife)\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    likes = pd.read_parquet('/task/data/train_likes.parquet')\n    likes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\n    likes_u = likes['uid'].map(uid_to_idx).to_numpy()\n    likes_i = likes['item_id'].map(item_to_idx).to_numpy()\n    dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n    likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    \n    vpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n    vpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\n    vpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\n    vpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n    vpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    U_pow = np.clip(U_sim, 0, None) ** 1.5\n    \n    # Test scoring with R_tfidf vs R vs R_idf_half\n    for beta, R_score_mat in [('idf1.0', R_tfidf), ('idf0.5', (R @ sp.diags(np.sqrt(idf))).tocsr()), ('idf0.0', R)]:\n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_score_mat[:, c:c+5000].toarray()\n            \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n            \n        scores = score_rankings(val_ranks, graded)\n        print(f'org_mult={org_mult:.1f}, beta={beta} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
            "description": "Benchmark organic multiplier and scoring matrix IDF weighting."
          },
          "recorded": {
            "id": "run_shell_command__call_191743",
            "name": "run_shell_command",
            "args": {
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\n# Compare organic multiplier: 1.5 vs 2.0 vs 2.5\nfor org_mult in [1.5, 2.0, 2.5]:\n    dt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\n    decay = np.exp(-np.log(2) * dt / halflife)\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.2, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    likes = pd.read_parquet('/task/data/train_likes.parquet')\n    likes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\n    likes_u = likes['uid'].map(uid_to_idx).to_numpy()\n    likes_i = likes['item_id'].map(item_to_idx).to_numpy()\n    dt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n    likes_w = 4.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    \n    vpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n    vpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\n    vpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\n    vpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n    vpref_w = np.full(len(vpref_liked), 5.0, dtype=np.float32)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    U_pow = np.clip(U_sim, 0, None) ** 1.5\n    \n    # Test scoring with R_tfidf vs R vs R_idf_half\n    for beta, R_score_mat in [('idf1.0', R_tfidf), ('idf0.5', (R @ sp.diags(np.sqrt(idf))).tocsr()), ('idf0.0', R)]:\n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_score_mat[:, c:c+5000].toarray()\n            \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n            \n        scores = score_rankings(val_ranks, graded)\n        print(f'org_mult={org_mult:.1f}, beta={beta} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
              "description": "Benchmark organic multiplier and scoring matrix IDF weighting."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_191743",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: org_mult=1.5, beta=idf1.0 -> NDCG@10: 0.032413\norg_mult=1.5, beta=idf0.5 -> NDCG@10: 0.026770\norg_mult=1.5, beta=idf0.0 -> NDCG@10: 0.021123\norg_mult=2.0, beta=idf1.0 -> NDCG@10: 0.033848\norg_mult=2.0, beta=idf0.5 -> NDCG@10: 0.027664\norg_mult=2.0, beta=idf0.0 -> NDCG@10: 0.021314\norg_mult=2.5, beta=idf1.0 -> NDCG@10: 0.034533\norg_mult=2.5, beta=idf0.5 -> NDCG@10: 0.027952\norg_mult=2.5, beta=idf0.0 -> NDCG@10: 0.021446\nProcess Group PGID: 2361\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:42:57.424Z",
            "resultDisplay": "org_mult=1.5, beta=idf1.0 -> NDCG@10: 0.032413\norg_mult=1.5, beta=idf0.5 -> NDCG@10: 0.026770\norg_mult=1.5, beta=idf0.0 -> NDCG@10: 0.021123\norg_mult=2.0, beta=idf1.0 -> NDCG@10: 0.033848\norg_mult=2.0, beta=idf0.5 -> NDCG@10: 0.027664\norg_mult=2.0, beta=idf0.0 -> NDCG@10: 0.021314\norg_mult=2.5, beta=idf1.0 -> NDCG@10: 0.034533\norg_mult=2.5, beta=idf0.5 -> NDCG@10: 0.027952\norg_mult=2.5, beta=idf0.0 -> NDCG@10: 0.021446",
            "description": "Benchmark organic multiplier and scoring matrix IDF weighting.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:42:57.425Z",
            "content": "<untrusted_context>\nOutput: org_mult=1.5, beta=idf1.0 -> NDCG@10: 0.032413\norg_mult=1.5, beta=idf0.5 -> NDCG@10: 0.026770\norg_mult=1.5, beta=idf0.0 -> NDCG@10: 0.021123\norg_mult=2.0, beta=idf1.0 -> NDCG@10: 0.033848\norg_mult=2.0, beta=idf0.5 -> NDCG@10: 0.027664\norg_mult=2.0, beta=idf0.0 -> NDCG@10: 0.021314\norg_mult=2.5, beta=idf1.0 -> NDCG@10: 0.034533\norg_mult=2.5, beta=idf0.5 -> NDCG@10: 0.027952\norg_mult=2.5, beta=idf0.0 -> NDCG@10: 0.021446\nProcess Group PGID: 2361\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "64e412d5-06b2-4638-b7a3-714032e59df5"
        }
      ]
    },
    {
      "id": 39,
      "role": "agent",
      "timestamp": "2026-09-29T04:43:09.696Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:43:09.696Z",
          "text": "Observing Promising Results\n\nI've noted a significant improvement in NDCG@10 to 0.034533 with `org_mult=2.5`. This configuration's effectiveness seems tied to how strictly relevance is defined by organic user engagement."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:43:09.696Z",
          "callId": "run_shell_command__call_256767",
          "name": "run_shell_command",
          "input": {
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [2.5, 3.5, 5.0]:\n    for strong_mult in [1.5, 2.0]:\n        weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n        weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, strong_mult, 1.0)\n        \n        u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n        i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n        \n        likes_w = 8.0 * np.exp(-np.log(2) * dt_likes / halflife)\n        vpref_w = np.full(len(vpref_liked), 10.0, dtype=np.float32)\n        \n        all_u = np.concatenate([u_idx, likes_u, vpref_u])\n        all_i = np.concatenate([i_idx, likes_i, vpref_i])\n        all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n        \n        R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n        R.data = np.log1p(R.data)\n        \n        df_item = np.diff(R.tocsc().indptr)\n        idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n        R_tfidf = (R @ sp.diags(idf)).tocsr()\n        \n        user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n        R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n        \n        U_sim = (R_user_norm @ R_user_norm.T).toarray()\n        np.fill_diagonal(U_sim, 0.0)\n        U_pow = np.clip(U_sim, 0, None) ** 1.5\n        \n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n            \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n            \n        scores = score_rankings(val_ranks, graded)\n        print(f'org_mult={org_mult:.1f}, strong_mult={strong_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
            "description": "Test stronger organic and strong listen multipliers."
          },
          "recorded": {
            "id": "run_shell_command__call_256767",
            "name": "run_shell_command",
            "args": {
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [2.5, 3.5, 5.0]:\n    for strong_mult in [1.5, 2.0]:\n        weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n        weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, strong_mult, 1.0)\n        \n        u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n        i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n        \n        likes_w = 8.0 * np.exp(-np.log(2) * dt_likes / halflife)\n        vpref_w = np.full(len(vpref_liked), 10.0, dtype=np.float32)\n        \n        all_u = np.concatenate([u_idx, likes_u, vpref_u])\n        all_i = np.concatenate([i_idx, likes_i, vpref_i])\n        all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n        \n        R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n        R.data = np.log1p(R.data)\n        \n        df_item = np.diff(R.tocsc().indptr)\n        idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n        R_tfidf = (R @ sp.diags(idf)).tocsr()\n        \n        user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n        R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n        \n        U_sim = (R_user_norm @ R_user_norm.T).toarray()\n        np.fill_diagonal(U_sim, 0.0)\n        U_pow = np.clip(U_sim, 0, None) ** 1.5\n        \n        Scores = np.zeros((n_users, n_items), dtype=np.float32)\n        for c in range(0, n_items, 5000):\n            Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n            \n        val_ranks = {}\n        for i, uid in enumerate(target_uids):\n            row = eligibility.uid_row[uid]\n            mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n            user_scores = Scores[i].copy()\n            user_scores[mask == 0] = -1e9\n            top100_idx = np.argpartition(user_scores, -100)[-100:]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            val_ranks[uid] = item_ids[top100_idx].tolist()\n            \n        scores = score_rankings(val_ranks, graded)\n        print(f'org_mult={org_mult:.1f}, strong_mult={strong_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
              "description": "Test stronger organic and strong listen multipliers."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_256767",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: org_mult=2.5, strong_mult=1.5 -> NDCG@10: 0.034033\norg_mult=2.5, strong_mult=2.0 -> NDCG@10: 0.033668\norg_mult=3.5, strong_mult=1.5 -> NDCG@10: 0.035254\norg_mult=3.5, strong_mult=2.0 -> NDCG@10: 0.034271\norg_mult=5.0, strong_mult=1.5 -> NDCG@10: 0.036124\norg_mult=5.0, strong_mult=2.0 -> NDCG@10: 0.035221\nProcess Group PGID: 2408\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:46:20.309Z",
            "resultDisplay": "org_mult=2.5, strong_mult=1.5 -> NDCG@10: 0.034033\norg_mult=2.5, strong_mult=2.0 -> NDCG@10: 0.033668\norg_mult=3.5, strong_mult=1.5 -> NDCG@10: 0.035254\norg_mult=3.5, strong_mult=2.0 -> NDCG@10: 0.034271\norg_mult=5.0, strong_mult=1.5 -> NDCG@10: 0.036124\norg_mult=5.0, strong_mult=2.0 -> NDCG@10: 0.035221",
            "description": "Test stronger organic and strong listen multipliers.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:46:20.310Z",
            "content": "<untrusted_context>\nOutput: org_mult=2.5, strong_mult=1.5 -> NDCG@10: 0.034033\norg_mult=2.5, strong_mult=2.0 -> NDCG@10: 0.033668\norg_mult=3.5, strong_mult=1.5 -> NDCG@10: 0.035254\norg_mult=3.5, strong_mult=2.0 -> NDCG@10: 0.034271\norg_mult=5.0, strong_mult=1.5 -> NDCG@10: 0.036124\norg_mult=5.0, strong_mult=2.0 -> NDCG@10: 0.035221\nProcess Group PGID: 2408\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "eb8542a7-8c9d-4f2b-8fd5-1a0f2ebc91d0"
        }
      ]
    },
    {
      "id": 40,
      "role": "agent",
      "timestamp": "2026-09-29T04:46:36.811Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:46:36.811Z",
          "text": "Exploring Organic Weighting\n\nI'm currently investigating the impact of adjusting the multiplier for organic listens, considering values like 6.0 and 8.0, and even exploring a scenario where only organic listens are used for user similarity calculations. The initial results showing an NDCG@10 of 0.036124 are prompting deeper exploration into this parameter."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:46:36.811Z",
          "callId": "run_shell_command__call_378066",
          "name": "run_shell_command",
          "input": {
            "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [6.0, 8.0, 12.0, 20.0]:\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.5, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    likes_w = 10.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    vpref_w = np.full(len(vpref_liked), 12.0, dtype=np.float32)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    U_pow = np.clip(U_sim, 0, None) ** 1.5\n    \n    Scores = np.zeros((n_users, n_items), dtype=np.float32)\n    for c in range(0, n_items, 5000):\n        Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n        \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n    scores = score_rankings(val_ranks, graded)\n    print(f'org_mult={org_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
            "description": "Test higher organic multipliers."
          },
          "recorded": {
            "id": "run_shell_command__call_378066",
            "name": "run_shell_command",
            "args": {
              "command": "PYTHONPATH=/task python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_df.uid.astype(int))\ntarget_uids = sorted(targets)\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat['item_id'].to_numpy()\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\nn_users = len(target_uids)\nn_items = len(item_ids)\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncutoff = 24786800\nhalflife = 45 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet',\n                      columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n                      filters=[('played_ratio_pct', '>=', 50)])\ndf_tl = table.to_pandas()\ndf_tl = df_tl[df_tl['item_id'].isin(item_to_idx) & df_tl['uid'].isin(uid_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes['item_id'].isin(item_to_idx) & likes['uid'].isin(uid_to_idx)]\nlikes_u = likes['uid'].map(uid_to_idx).to_numpy()\nlikes_i = likes['item_id'].map(item_to_idx).to_numpy()\n\nvpref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nvpref_liked = vpref[(vpref['state'] == 'liked') & vpref['item_id'].isin(item_to_idx) & vpref['uid'].isin(uid_to_idx)]\nvpref_u = vpref_liked['uid'].map(uid_to_idx).to_numpy()\nvpref_i = vpref_liked['item_id'].map(item_to_idx).to_numpy()\n\ndt = np.clip(cutoff - df_tl['timestamp'].to_numpy(), 0, None)\ndecay = np.exp(-np.log(2) * dt / halflife)\ndt_likes = np.clip(cutoff - likes['timestamp'].to_numpy(), 0, None)\n\nfor org_mult in [6.0, 8.0, 12.0, 20.0]:\n    weight = decay * np.where(df_tl['is_organic'].to_numpy() == 1, org_mult, 1.0)\n    weight *= np.where(df_tl['played_ratio_pct'].to_numpy() >= 80, 1.5, 1.0)\n    \n    u_idx = df_tl['uid'].map(uid_to_idx).to_numpy()\n    i_idx = df_tl['item_id'].map(item_to_idx).to_numpy()\n    \n    likes_w = 10.0 * np.exp(-np.log(2) * dt_likes / halflife)\n    vpref_w = np.full(len(vpref_liked), 12.0, dtype=np.float32)\n    \n    all_u = np.concatenate([u_idx, likes_u, vpref_u])\n    all_i = np.concatenate([i_idx, likes_i, vpref_i])\n    all_w = np.concatenate([weight.astype(np.float32), likes_w.astype(np.float32), vpref_w])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n    R.data = np.log1p(R.data)\n    \n    df_item = np.diff(R.tocsc().indptr)\n    idf = np.log((n_users - df_item + 0.5) / (df_item + 0.5) + 1.0).astype(np.float32)\n    R_tfidf = (R @ sp.diags(idf)).tocsr()\n    \n    user_norms = np.sqrt(np.asarray(R_tfidf.power(2).sum(axis=1)).ravel()) + 1e-6\n    R_user_norm = (sp.diags(1.0 / user_norms) @ R_tfidf).tocsr()\n    \n    U_sim = (R_user_norm @ R_user_norm.T).toarray()\n    np.fill_diagonal(U_sim, 0.0)\n    U_pow = np.clip(U_sim, 0, None) ** 1.5\n    \n    Scores = np.zeros((n_users, n_items), dtype=np.float32)\n    for c in range(0, n_items, 5000):\n        Scores[:, c:c+5000] = U_pow @ R_tfidf[:, c:c+5000].toarray()\n        \n    val_ranks = {}\n    for i, uid in enumerate(target_uids):\n        row = eligibility.uid_row[uid]\n        mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n        user_scores = Scores[i].copy()\n        user_scores[mask == 0] = -1e9\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        val_ranks[uid] = item_ids[top100_idx].tolist()\n        \n    scores = score_rankings(val_ranks, graded)\n    print(f'org_mult={org_mult:.1f} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
              "description": "Test higher organic multipliers."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_378066",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: org_mult=6.0 -> NDCG@10: 0.036063\norg_mult=8.0 -> NDCG@10: 0.036132\norg_mult=12.0 -> NDCG@10: 0.036065\norg_mult=20.0 -> NDCG@10: 0.035436\nProcess Group PGID: 2455\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:48:46.218Z",
            "resultDisplay": "org_mult=6.0 -> NDCG@10: 0.036063\norg_mult=8.0 -> NDCG@10: 0.036132\norg_mult=12.0 -> NDCG@10: 0.036065\norg_mult=20.0 -> NDCG@10: 0.035436",
            "description": "Test higher organic multipliers.",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:48:46.219Z",
            "content": "<untrusted_context>\nOutput: org_mult=6.0 -> NDCG@10: 0.036063\norg_mult=8.0 -> NDCG@10: 0.036132\norg_mult=12.0 -> NDCG@10: 0.036065\norg_mult=20.0 -> NDCG@10: 0.035436\nProcess Group PGID: 2455\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "80da408c-6461-4087-8396-f4094ad7b987"
        }
      ]
    },
    {
      "id": 41,
      "role": "agent",
      "timestamp": "2026-09-29T04:49:02.475Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:49:02.475Z",
          "text": "Exploring Negative Signals\n\nI'm currently investigating negative user feedback, specifically dislikes and unlikes, and how they might influence candidate item selection. My focus is on understanding their impact, especially when similar users have expressed negative sentiment."
        }
      ]
    }
  ],
  "source": {
    "format": "gemini-cli-session",
    "sha256": "f8001669aaed5a9ebaff13db309df97a74e2fdb1ace78d2b29d20381f2bf8d42",
    "records": 196,
    "sessionMetadata": {
      "sessionId": "22eb3288-1efd-4162-96ce-2835ddd16d72",
      "projectHash": "f53b52ad6d21cceb72dfa78fb67614fe14f110c58e68412b01508d6a485501c3",
      "startTime": "2026-09-29T04:19:32.586Z",
      "lastUpdated": "2026-09-29T04:19:32.586Z",
      "kind": "main"
    },
    "sessionUpdates": 79,
    "usageRecords": [
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        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 9890,
          "output": 161,
          "cached": 0,
          "thoughts": 325,
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        "tokens": {
          "input": 14801,
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        "timestamp": "2026-09-29T04:19:52.722Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 16124,
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        }
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        "messageId": "0fa9bb82-d507-4c23-92a8-2e2ed7eb7eb8",
        "timestamp": "2026-09-29T04:19:55.593Z",
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        "tokens": {
          "input": 16244,
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        "timestamp": "2026-09-29T04:20:20.890Z",
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        "tokens": {
          "input": 18006,
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