{
  "schemaVersion": 1,
  "taskId": "yambda-discovery-ranking-v01",
  "attemptId": "gemini-3-8-flash-high-basic-fixed-r01-d0433654",
  "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
  "agentVersion": "0.61.0",
  "model": "gemini-3.8-flash",
  "sessionId": "60e34f21-2eba-4fe1-822f-83a1c914ba13",
  "steps": [
    {
      "id": 1,
      "role": "user",
      "timestamp": "2026-09-29T03:36:53.180Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-29T03:36:53.180Z",
          "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": "2026-09-29T03:36:53.225Z",
      "blocks": [
        {
          "kind": "message",
          "timestamp": "2026-09-29T03:36:53.225Z",
          "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"
        }
      ]
    },
    {
      "id": 3,
      "role": "agent",
      "timestamp": "2026-09-29T03:36:59.314Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:36:59.313Z",
          "text": "Understanding Core Requirements\n\nI'm currently focusing on grasping the prompt, environment, mandates, and the tools at my disposal. My objective is to operate in YOLO mode, adhering strictly to the provided guidelines and ensuring all deliverables are placed within the `/app/solution/` directory."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:36:59.314Z",
          "callId": "update_topic__call_164646",
          "name": "update_topic",
          "input": {
            "title": "Initial Exploration & Baseline Setup",
            "strategic_intent": "Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
            "summary": "Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early."
          },
          "recorded": {
            "id": "update_topic__call_164646",
            "name": "update_topic",
            "args": {
              "title": "Initial Exploration & Baseline Setup",
              "strategic_intent": "Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
              "summary": "Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "update_topic__call_164646",
                  "name": "update_topic",
                  "response": {
                    "output": "Current topic: \"Initial Exploration & Baseline Setup\"\nTopic summary: Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early.\n\nStrategic Intent: Explore environment, inspect evaluation scripts and data sizes, and establish a baseline."
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:36:59.362Z",
            "resultDisplay": "## 📂 Topic: **Initial Exploration & Baseline Setup**\n\n**Summary:**\nStarting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early.\n\n> [!STRATEGY]\n> **Intent:** Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
            "description": "Update topic to: \"Initial Exploration & Baseline Setup\"",
            "displayName": "Update Topic Context",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:36:59.364Z",
            "content": "Current topic: \"Initial Exploration & Baseline Setup\"\nTopic summary: Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early.\n\nStrategic Intent: Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
            "isError": false
          },
          "responseMessageId": "da955701-40e4-4bfb-9924-c5060281b757"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:36:59.314Z",
          "callId": "read_file__call_164658",
          "name": "read_file",
          "input": {
            "file_path": "/task/evaluate_public.py",
            "end_line": 100,
            "start_line": 1
          },
          "recorded": {
            "id": "read_file__call_164658",
            "name": "read_file",
            "args": {
              "file_path": "/task/evaluate_public.py",
              "end_line": 100,
              "start_line": 1
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_164658",
                  "name": "read_file",
                  "response": {
                    "output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n#!/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."
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:36:59.362Z",
            "resultDisplay": "Read lines 1-100 of 158 from ../task/evaluate_public.py",
            "description": "../task/evaluate_public.py",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:36:59.364Z",
            "content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-100 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.\n\n--- FILE CONTENT (truncated) ---\n#!/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.",
            "isError": false
          },
          "responseMessageId": "da955701-40e4-4bfb-9924-c5060281b757"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:36:59.314Z",
          "callId": "read_file__call_164664",
          "name": "read_file",
          "input": {
            "end_line": 150,
            "file_path": "/task/ranking_evaluation.py",
            "start_line": 1
          },
          "recorded": {
            "id": "read_file__call_164664",
            "name": "read_file",
            "args": {
              "end_line": 150,
              "file_path": "/task/ranking_evaluation.py",
              "start_line": 1
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_164664",
                  "name": "read_file",
                  "response": {
                    "output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"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\""
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:36:59.362Z",
            "resultDisplay": "Read lines 1-150 of 232 from ../task/ranking_evaluation.py",
            "description": "../task/ranking_evaluation.py",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:36:59.364Z",
            "content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 1-150 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.\n\n--- FILE CONTENT (truncated) ---\n\"\"\"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\"",
            "isError": false
          },
          "responseMessageId": "da955701-40e4-4bfb-9924-c5060281b757"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:36:59.314Z",
          "callId": "read_file__call_164676",
          "name": "read_file",
          "input": {
            "start_line": 1,
            "file_path": "/task/requirements.txt",
            "end_line": 100
          },
          "recorded": {
            "id": "read_file__call_164676",
            "name": "read_file",
            "args": {
              "start_line": 1,
              "file_path": "/task/requirements.txt",
              "end_line": 100
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_164676",
                  "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": "2026-09-29T03:36:59.362Z",
            "resultDisplay": "",
            "description": "../task/requirements.txt",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:36:59.364Z",
            "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
          },
          "responseMessageId": "da955701-40e4-4bfb-9924-c5060281b757"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:36:59.314Z",
          "callId": "run_shell_command__call_164682",
          "name": "run_shell_command",
          "input": {
            "description": "Check CPU, RAM, GPU availability and data file sizes",
            "command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -lh /task/data/"
          },
          "recorded": {
            "id": "run_shell_command__call_164682",
            "name": "run_shell_command",
            "args": {
              "description": "Check CPU, RAM, GPU availability and data file sizes",
              "command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -lh /task/data/"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_164682",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: CPU(s):                                  8\nModel name:                              AMD EPYC 7R13 Processor\nNUMA node0 CPU(s):                       0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 03:30 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 29 03:30 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 03:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 03:30 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 03:30 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 03:30 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 03:30 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 03:30 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 29 03:30 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 03:30 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 03:30 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 03:30 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 29 03:30 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 03:30 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 validation_preference_state.parquet\nProcess Group PGID: 1612\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:36:59.362Z",
            "resultDisplay": "CPU(s):                                  8\nModel name:                              AMD EPYC 7R13 Processor\nNUMA node0 CPU(s):                       0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 03:30 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 29 03:30 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 03:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 03:30 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 03:30 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 03:30 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 03:30 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 03:30 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 29 03:30 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 03:30 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 03:30 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 03:30 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 29 03:30 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 03:30 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 validation_preference_state.parquet",
            "description": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -lh /task/data/",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:36:59.364Z",
            "content": "<untrusted_context>\nOutput: CPU(s):                                  8\nModel name:                              AMD EPYC 7R13 Processor\nNUMA node0 CPU(s):                       0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 340M\n-r--r--r-- 1 root root 342K Sep 29 03:30 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 29 03:30 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 03:31 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 03:30 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 03:30 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 03:30 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 03:30 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 03:30 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 29 03:30 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 03:30 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 03:30 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 03:30 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 29 03:30 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 03:30 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 03:31 validation_preference_state.parquet\nProcess Group PGID: 1612\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "da955701-40e4-4bfb-9924-c5060281b757"
        }
      ]
    },
    {
      "id": 4,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:03.579Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:03.579Z",
          "callId": "run_shell_command__call_223082",
          "name": "run_shell_command",
          "input": {
            "description": "Check total RAM from /proc/meminfo",
            "command": "cat /proc/meminfo | grep MemTotal"
          },
          "recorded": {
            "id": "run_shell_command__call_223082",
            "name": "run_shell_command",
            "args": {
              "description": "Check total RAM from /proc/meminfo",
              "command": "cat /proc/meminfo | grep MemTotal"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_223082",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: MemTotal:       15988968 kB\nProcess Group PGID: 1619\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:03.594Z",
            "resultDisplay": "MemTotal:       15988968 kB",
            "description": "cat /proc/meminfo | grep MemTotal",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:03.595Z",
            "content": "<untrusted_context>\nOutput: MemTotal:       15988968 kB\nProcess Group PGID: 1619\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "80498a34-d84d-4398-a85a-7d6248ccbfb2"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:03.579Z",
          "callId": "read_file__call_223085",
          "name": "read_file",
          "input": {
            "file_path": "/task/evaluate_public.py",
            "end_line": 160,
            "start_line": 100
          },
          "recorded": {
            "id": "read_file__call_223085",
            "name": "read_file",
            "args": {
              "file_path": "/task/evaluate_public.py",
              "end_line": 160,
              "start_line": 100
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_223085",
                  "name": "read_file",
                  "response": {
                    "output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 100-158 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.\n\n--- FILE CONTENT (truncated) ---\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"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:03.594Z",
            "resultDisplay": "Read lines 100-158 of 158 from ../task/evaluate_public.py",
            "description": "../task/evaluate_public.py",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:37:03.595Z",
            "content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 100-158 of 158 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.\n\n--- FILE CONTENT (truncated) ---\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",
            "isError": false
          },
          "responseMessageId": "80498a34-d84d-4398-a85a-7d6248ccbfb2"
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:03.579Z",
          "callId": "read_file__call_223094",
          "name": "read_file",
          "input": {
            "file_path": "/task/ranking_evaluation.py",
            "start_line": 150,
            "end_line": 232
          },
          "recorded": {
            "id": "read_file__call_223094",
            "name": "read_file",
            "args": {
              "file_path": "/task/ranking_evaluation.py",
              "start_line": 150,
              "end_line": 232
            },
            "result": [
              {
                "functionResponse": {
                  "id": "read_file__call_223094",
                  "name": "read_file",
                  "response": {
                    "output": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 150-232 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.\n\n--- FILE CONTENT (truncated) ---\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": "2026-09-29T03:37:03.594Z",
            "resultDisplay": "Read lines 150-232 of 232 from ../task/ranking_evaluation.py",
            "description": "../task/ranking_evaluation.py",
            "displayName": "ReadFile",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:37:03.595Z",
            "content": "\nIMPORTANT: The file content has been truncated.\nStatus: Showing lines 150-232 of 232 total lines.\nAction: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.\n\n--- FILE CONTENT (truncated) ---\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": "80498a34-d84d-4398-a85a-7d6248ccbfb2"
        }
      ]
    },
    {
      "id": 5,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:21.172Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:37:21.172Z",
          "text": "Analyzing Hardware Limitations\n\nMy current thinking is focused on optimizing for the available hardware. I'm noting the absence of GPUs and the substantial core count of AMD EPYC CPUs, alongside the 30-minute time constraint. The available libraries are also being considered as I plan the next steps."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:21.172Z",
          "callId": "run_shell_command__call_144052",
          "name": "run_shell_command",
          "input": {
            "description": "Check available ML libraries in python3",
            "command": "python3 -c \"for pkg in ['scipy', 'sklearn', 'lightgbm', 'xgboost', 'torch', 'implicit']:\n    try:\n        __import__(pkg)\n        print(pkg, 'AVAILABLE')\n    except ImportError:\n        print(pkg, 'NOT available')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_144052",
            "name": "run_shell_command",
            "args": {
              "description": "Check available ML libraries in python3",
              "command": "python3 -c \"for pkg in ['scipy', 'sklearn', 'lightgbm', 'xgboost', 'torch', 'implicit']:\n    try:\n        __import__(pkg)\n        print(pkg, 'AVAILABLE')\n    except ImportError:\n        print(pkg, 'NOT available')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_144052",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: scipy AVAILABLE\nsklearn AVAILABLE\nlightgbm NOT available\nxgboost NOT available\ntorch NOT available\nimplicit NOT available\nProcess Group PGID: 1623\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:22.592Z",
            "resultDisplay": "scipy AVAILABLE\nsklearn AVAILABLE\nlightgbm NOT available\nxgboost NOT available\ntorch NOT available\nimplicit NOT available",
            "description": "Check available ML libraries in python3",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:22.593Z",
            "content": "<untrusted_context>\nOutput: scipy AVAILABLE\nsklearn AVAILABLE\nlightgbm NOT available\nxgboost NOT available\ntorch NOT available\nimplicit NOT available\nProcess Group PGID: 1623\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "72443a1a-fc7e-4543-96fd-df11f16545fa"
        }
      ]
    },
    {
      "id": 6,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:26.436Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:26.436Z",
          "callId": "run_shell_command__call_225849",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog count:', len(catalog), catalog.columns.tolist())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', list(val_npz.keys()), 'uids len:', len(val_npz['uids']), 'items len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', list(test_npz.keys()), 'uids len:', len(test_npz['uids']), 'items len:', len(test_npz['item_ids']), 'packed shape:', test_npz['packed_eligible'].shape)\n\"",
            "description": "Inspect target users, catalog, and eligible candidate shapes"
          },
          "recorded": {
            "id": "run_shell_command__call_225849",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog count:', len(catalog), catalog.columns.tolist())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', list(val_npz.keys()), 'uids len:', len(val_npz['uids']), 'items len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', list(test_npz.keys()), 'uids len:', len(test_npz['uids']), 'items len:', len(test_npz['item_ids']), 'packed shape:', test_npz['packed_eligible'].shape)\n\"",
              "description": "Inspect target users, catalog, and eligible candidate shapes"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_225849",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ncatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nval_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)\nProcess Group PGID: 1647\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:27.359Z",
            "resultDisplay": "targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ncatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nval_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)",
            "description": "Inspect target users, catalog, and eligible candidate shapes",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:27.360Z",
            "content": "<untrusted_context>\nOutput: targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ncatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nval_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)\nProcess Group PGID: 1647\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "5bab923f-43ce-4a88-9511-ba5530573251"
        }
      ]
    },
    {
      "id": 7,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:36.712Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:37:36.712Z",
          "text": "Verifying Data Integrity\n\nI'm currently digging into the details of our user and item data. My immediate focus is on confirming the alignment between the user IDs in our NPZ files and the `target_users.parquet` file. I also need to determine the average number of eligible items per user. I'll be running an exploratory script shortly to gather these crucial metrics."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:36.712Z",
          "callId": "run_shell_command__call_117778",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('targets equals val uids:', np.array_equal(targets.uid.values, val_npz['uids']))\nprint('targets equals test uids:', np.array_equal(targets.uid.values, test_npz['uids']))\nprint('val items equals test items:', np.array_equal(val_npz['item_ids'], test_npz['item_ids']))\n\n# Unpack eligible bits count\npacked = val_npz['packed_eligible']\n# count bits set per user\nbits_per_user = np.unpackbits(packed, axis=1)[:, :30000].sum(axis=1)\nprint(f'Eligible items per user in val: min={bits_per_user.min()}, max={bits_per_user.max()}, mean={bits_per_user.mean():.1f}, median={np.median(bits_per_user)}')\n\"",
            "description": "Check alignment and eligible candidate statistics"
          },
          "recorded": {
            "id": "run_shell_command__call_117778",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('targets equals val uids:', np.array_equal(targets.uid.values, val_npz['uids']))\nprint('targets equals test uids:', np.array_equal(targets.uid.values, test_npz['uids']))\nprint('val items equals test items:', np.array_equal(val_npz['item_ids'], test_npz['item_ids']))\n\n# Unpack eligible bits count\npacked = val_npz['packed_eligible']\n# count bits set per user\nbits_per_user = np.unpackbits(packed, axis=1)[:, :30000].sum(axis=1)\nprint(f'Eligible items per user in val: min={bits_per_user.min()}, max={bits_per_user.max()}, mean={bits_per_user.mean():.1f}, median={np.median(bits_per_user)}')\n\"",
              "description": "Check alignment and eligible candidate statistics"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_117778",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: targets equals val uids: True\ntargets equals test uids: True\nval items equals test items: True\nEligible items per user in val: min=24657, max=30000, mean=29067.3, median=29242.0\nProcess Group PGID: 1670\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:37.630Z",
            "resultDisplay": "targets equals val uids: True\ntargets equals test uids: True\nval items equals test items: True\nEligible items per user in val: min=24657, max=30000, mean=29067.3, median=29242.0",
            "description": "Check alignment and eligible candidate statistics",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:37.630Z",
            "content": "<untrusted_context>\nOutput: targets equals val uids: True\ntargets equals test uids: True\nval items equals test items: True\nEligible items per user in val: min=24657, max=30000, mean=29067.3, median=29242.0\nProcess Group PGID: 1670\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "1640e96f-2461-4bf0-a76d-987d8ab1dafc"
        }
      ]
    },
    {
      "id": 8,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:45.360Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:37:45.360Z",
          "text": "Confirming Data Consistency\n\nI've confirmed the exact alignment across `targets.uid.values`, `val_npz['uids']`, and `test_npz['uids']`. Furthermore, `val_npz['item_ids']` and `test_npz['item_ids']` are identical, encompassing 30,000 items. The next step is to verify that the catalog's `item_id` aligns with these found in `val_npz['item_ids']`."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:45.360Z",
          "callId": "run_shell_command__call_270662",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('catalog items equal val_npz item_ids:', np.array_equal(catalog.item_id.values, val_npz['item_ids']))\n\"",
            "description": "Check if catalog items match val_npz item_ids"
          },
          "recorded": {
            "id": "run_shell_command__call_270662",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('catalog items equal val_npz item_ids:', np.array_equal(catalog.item_id.values, val_npz['item_ids']))\n\"",
              "description": "Check if catalog items match val_npz item_ids"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_270662",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: catalog items equal val_npz item_ids: True\nProcess Group PGID: 1686\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:46.116Z",
            "resultDisplay": "catalog items equal val_npz item_ids: True",
            "description": "Check if catalog items match val_npz item_ids",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:46.116Z",
            "content": "<untrusted_context>\nOutput: catalog items equal val_npz item_ids: True\nProcess Group PGID: 1686\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "d4cef7a4-1e8d-4081-941d-4365bac33d61"
        }
      ]
    },
    {
      "id": 9,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:50.544Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:37:50.544Z",
          "text": "Confirming Data Source\n\nI'm verifying that the `item_id` values from the catalog directly correspond to the `item_ids` found within the `val_npz` data. My current focus is on understanding the `candidate_catalog.parquet` file, which appears to be a curated list of 30,000 tracks based on their listening popularity during training."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:50.544Z",
          "callId": "run_shell_command__call_371813",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, 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# Test top 100 eligible by catalog popularity\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\n# For each user, find first 100 set bits\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\"",
            "description": "Evaluate catalog popularity baseline on validation set"
          },
          "recorded": {
            "id": "run_shell_command__call_371813",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, 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# Test top 100 eligible by catalog popularity\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\n# For each user, find first 100 set bits\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\"",
              "description": "Evaluate catalog popularity baseline on validation set"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_371813",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1709\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:51.235Z",
            "resultDisplay": "Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'",
            "description": "Evaluate catalog popularity baseline on validation set",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:51.236Z",
            "content": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1709\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "477c6b42-2eb3-4579-8717-c9cab04e6143"
        }
      ]
    },
    {
      "id": 10,
      "role": "agent",
      "timestamp": "2026-09-29T03:37:54.487Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:37:54.487Z",
          "callId": "run_shell_command__call_223717",
          "name": "run_shell_command",
          "input": {
            "description": "Evaluate catalog popularity baseline on validation set",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, 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\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_223717",
            "name": "run_shell_command",
            "args": {
              "description": "Evaluate catalog popularity baseline on validation set",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, 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\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\nranked = {}\nfor i, uid in enumerate(uids):\n    bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n    eligible_indices = np.where(bits == 1)[0][:100]\n    ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_223717",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Catalog popularity baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1720\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:37:56.118Z",
            "resultDisplay": "Catalog popularity baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}",
            "description": "Evaluate catalog popularity baseline on validation set",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:37:56.119Z",
            "content": "<untrusted_context>\nOutput: Catalog popularity baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1720\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "2fa0b230-4685-48f9-b30d-bd6dbc761b8f"
        }
      ]
    },
    {
      "id": 11,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:01.704Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:01.704Z",
          "text": "Creating Baseline Submission\n\nI've just achieved an NDCG@10 of 0.01071 with catalog popularity, involving 4263 users. My immediate next step is to generate a valid submission file, `/app/solution/submission.parquet`, based on this baseline, ensuring it adheres to the specified contract."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:01.704Z",
          "callId": "run_shell_command__call_223825",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntest_npz = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid.astype(int))\n\nrows = []\nfor i, uid in enumerate(test_npz.uids):\n    bits = np.unpackbits(test_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = test_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(df, target_set)\nprint('Validation passed! Row count:', len(validated))\nfor uid, item in df[['uid', 'item_id']].itertuples(index=False, name=None):\n    if not test_npz.is_eligible(uid, item):\n        raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('All items verified eligible!')\nPath('/app/solution').mkdir(parents=True, exist_ok=True)\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission.parquet saved successfully!')\n\"",
            "description": "Generate initial baseline submission and verify test eligibility"
          },
          "recorded": {
            "id": "run_shell_command__call_223825",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntest_npz = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid.astype(int))\n\nrows = []\nfor i, uid in enumerate(test_npz.uids):\n    bits = np.unpackbits(test_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = test_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(df, target_set)\nprint('Validation passed! Row count:', len(validated))\nfor uid, item in df[['uid', 'item_id']].itertuples(index=False, name=None):\n    if not test_npz.is_eligible(uid, item):\n        raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('All items verified eligible!')\nPath('/app/solution').mkdir(parents=True, exist_ok=True)\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission.parquet saved successfully!')\n\"",
              "description": "Generate initial baseline submission and verify test eligibility"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_223825",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Validation passed! Row count: 845600\nAll items verified eligible!\nBaseline submission.parquet saved successfully!\nProcess Group PGID: 1740\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:04.589Z",
            "resultDisplay": "Validation passed! Row count: 845600\nAll items verified eligible!\nBaseline submission.parquet saved successfully!",
            "description": "Generate initial baseline submission and verify test eligibility",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:04.590Z",
            "content": "<untrusted_context>\nOutput: Validation passed! Row count: 845600\nAll items verified eligible!\nBaseline submission.parquet saved successfully!\nProcess Group PGID: 1740\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "1c1da8d8-2ad7-4af9-8762-39de32172c5b"
        }
      ]
    },
    {
      "id": 12,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:08.838Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:08.838Z",
          "text": "Establishing Initial Baseline\n\nI've successfully generated a valid baseline submission file. My next step is to confirm the integrity of the `evaluate_public.py` script by running it against a validation version of this newly created baseline."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:08.838Z",
          "callId": "run_shell_command__call_222466",
          "name": "run_shell_command",
          "input": {
            "description": "Run evaluate_public.py on validation baseline",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nrows = []\nfor i, uid in enumerate(val_npz.uids):\n    bits = np.unpackbits(val_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = val_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet && rm /app/solution/val_baseline.parquet"
          },
          "recorded": {
            "id": "run_shell_command__call_222466",
            "name": "run_shell_command",
            "args": {
              "description": "Run evaluate_public.py on validation baseline",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nrows = []\nfor i, uid in enumerate(val_npz.uids):\n    bits = np.unpackbits(val_npz.packed[i], count=30000, bitorder='little')\n    chosen_items = val_npz.item_ids[np.where(bits == 1)[0][:100]]\n    for rank, item in enumerate(chosen_items, 1):\n        rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet && rm /app/solution/val_baseline.parquet"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_222466",
                  "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: 1756\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:13.320Z",
            "resultDisplay": "{\n  \"model\": \"/app/solution/val_baseline.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}",
            "description": "Run evaluate_public.py on validation baseline",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:13.321Z",
            "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: 1756\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "a1c769c8-14d0-4f8f-af11-46a3a1a7b789"
        }
      ]
    },
    {
      "id": 13,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:16.627Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:16.627Z",
          "callId": "update_topic__call_166019",
          "name": "update_topic",
          "input": {
            "title": "Data Analysis & Recommender Architecture Design",
            "strategic_intent": "Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
            "summary": "We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery."
          },
          "recorded": {
            "id": "update_topic__call_166019",
            "name": "update_topic",
            "args": {
              "title": "Data Analysis & Recommender Architecture Design",
              "strategic_intent": "Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
              "summary": "We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "update_topic__call_166019",
                  "name": "update_topic",
                  "response": {
                    "output": "Current topic: \"Data Analysis & Recommender Architecture Design\"\nTopic summary: We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery.\n\nStrategic Intent: Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models."
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:16.629Z",
            "resultDisplay": "## 📂 Topic: **Data Analysis & Recommender Architecture Design**\n\n**Summary:**\nWe established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery.\n\n> [!STRATEGY]\n> **Intent:** Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
            "description": "Update topic to: \"Data Analysis & Recommender Architecture Design\"",
            "displayName": "Update Topic Context",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:38:16.630Z",
            "content": "Current topic: \"Data Analysis & Recommender Architecture Design\"\nTopic summary: We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery.\n\nStrategic Intent: Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
            "isError": false
          },
          "responseMessageId": "c184c585-cd25-4c1e-87b9-ca882d63ca6c"
        }
      ]
    },
    {
      "id": 14,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:24.240Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:24.240Z",
          "text": "Exploring Data Properties\n\nI'm currently inspecting the `train_listens.parquet` file, focusing on its row and column counts, timestamp range, and distributions for `played_ratio_pct` and `is_organic`. This will help me understand the fundamental characteristics of this dataset."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:24.240Z",
          "callId": "run_shell_command__call_144998",
          "name": "run_shell_command",
          "input": {
            "description": "Inspect timestamps, event counts, and preference state tables",
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'val_listens', 'val_likes']:\n    df = pd.read_parquet(f'/task/data/{name}.parquet')\n    print(f'{name}: shape={df.shape}, min_ts={df.timestamp.min()}, max_ts={df.timestamp.max()}, unique_uids={df.uid.nunique()}, unique_items={df.item_id.nunique()}')\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', pref.shape, pref.head(5))\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_preference_state:', test_pref.shape, test_pref.head(5))\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_144998",
            "name": "run_shell_command",
            "args": {
              "description": "Inspect timestamps, event counts, and preference state tables",
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'val_listens', 'val_likes']:\n    df = pd.read_parquet(f'/task/data/{name}.parquet')\n    print(f'{name}: shape={df.shape}, min_ts={df.timestamp.min()}, max_ts={df.timestamp.max()}, unique_uids={df.uid.nunique()}, unique_items={df.item_id.nunique()}')\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', pref.shape, pref.head(5))\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_preference_state:', test_pref.shape, test_pref.head(5))\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_144998",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: train_listens: shape=(43484159, 6), min_ts=0, max_ts=24786795, unique_uids=9238, unique_items=845395\ntrain_likes: shape=(814831, 4), min_ts=80, max_ts=24786790, unique_uids=8144, unique_items=172303\ntrain_dislikes: shape=(101075, 4), min_ts=75, max_ts=24786580, unique_uids=5732, unique_items=50876\nval_listens: shape=(1501697, 6), min_ts=24788600, max_ts=25393395, unique_uids=8196, unique_items=191747\nval_likes: shape=(37795, 4), min_ts=24788815, max_ts=25393390, unique_uids=4114, unique_items=22902\nvalidation_preference_state: (469124, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntest_preference_state: (484776, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nProcess Group PGID: 1786\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:26.174Z",
            "resultDisplay": "train_listens: shape=(43484159, 6), min_ts=0, max_ts=24786795, unique_uids=9238, unique_items=845395\ntrain_likes: shape=(814831, 4), min_ts=80, max_ts=24786790, unique_uids=8144, unique_items=172303\ntrain_dislikes: shape=(101075, 4), min_ts=75, max_ts=24786580, unique_uids=5732, unique_items=50876\nval_listens: shape=(1501697, 6), min_ts=24788600, max_ts=25393395, unique_uids=8196, unique_items=191747\nval_likes: shape=(37795, 4), min_ts=24788815, max_ts=25393390, unique_uids=4114, unique_items=22902\nvalidation_preference_state: (469124, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntest_preference_state: (484776, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked",
            "description": "Inspect timestamps, event counts, and preference state tables",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:26.175Z",
            "content": "<untrusted_context>\nOutput: train_listens: shape=(43484159, 6), min_ts=0, max_ts=24786795, unique_uids=9238, unique_items=845395\ntrain_likes: shape=(814831, 4), min_ts=80, max_ts=24786790, unique_uids=8144, unique_items=172303\ntrain_dislikes: shape=(101075, 4), min_ts=75, max_ts=24786580, unique_uids=5732, unique_items=50876\nval_listens: shape=(1501697, 6), min_ts=24788600, max_ts=25393395, unique_uids=8196, unique_items=191747\nval_likes: shape=(37795, 4), min_ts=24788815, max_ts=25393390, unique_uids=4114, unique_items=22902\nvalidation_preference_state: (469124, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntest_preference_state: (484776, 3)    uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\nProcess Group PGID: 1786\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "65baab5a-0b93-4f40-9280-74d1ba981b7a"
        }
      ]
    },
    {
      "id": 15,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:38.176Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:38.176Z",
          "text": "Analyzing User Data\n\nI'm currently examining the `train_listens` dataset. It's quite large, with over 43 million entries, yet it represents a surprisingly small cohort of approximately 9,000 unique users, which closely matches the target user count. This suggests a deep dive into user behavior within this specific group is warranted."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:38.176Z",
          "text": "Investigating Candidate Data\n\nMy analysis confirms the 30,000 candidate items are consistent across datasets. I'm now focused on understanding the criteria for \"candidate eligibility\" – why some items are not accessible to specific users, and I'm digging into the `ranking_evaluation.py` script for clues on this selective availability."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:38.176Z",
          "callId": "run_shell_command__call_169037",
          "name": "run_shell_command",
          "input": {
            "description": "Check relationship between preference state and candidate eligibility",
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = 0\nfor item in user_pref.item_id:\n    if not val_npz.is_eligible(uid, item):\n        ineligible_count += 1\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_169037",
            "name": "run_shell_command",
            "args": {
              "description": "Check relationship between preference state and candidate eligibility",
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = 0\nfor item in user_pref.item_id:\n    if not val_npz.is_eligible(uid, item):\n        ineligible_count += 1\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_169037",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1806\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:38.881Z",
            "resultDisplay": "Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'",
            "description": "Check relationship between preference state and candidate eligibility",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:38.882Z",
            "content": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 4, in <module>\nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1806\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "51cf5cbe-d669-436a-97fa-7989720a3dbb"
        }
      ]
    },
    {
      "id": 16,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:43.258Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:43.258Z",
          "callId": "run_shell_command__call_222891",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:\\n', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = sum(not val_npz.is_eligible(uid, item) for item in user_pref.item_id)\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\"",
            "description": "Check preference state counts and eligibility"
          },
          "recorded": {
            "id": "run_shell_command__call_222891",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:\\n', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = sum(not val_npz.is_eligible(uid, item) for item in user_pref.item_id)\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\"",
              "description": "Check preference state counts and eligibility"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_222891",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Preference state values:\n state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100 pref count: 15\nUser 100 pref items ineligible: 15 / 15\nProcess Group PGID: 1823\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:44.157Z",
            "resultDisplay": "Preference state values:\n state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100 pref count: 15\nUser 100 pref items ineligible: 15 / 15",
            "description": "Check preference state counts and eligibility",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:44.158Z",
            "content": "<untrusted_context>\nOutput: Preference state values:\n state\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nUser 100 pref count: 15\nUser 100 pref items ineligible: 15 / 15\nProcess Group PGID: 1823\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "4cbe58cd-380c-43b6-9b07-f37abfed7d79"
        }
      ]
    },
    {
      "id": 17,
      "role": "agent",
      "timestamp": "2026-09-29T03:38:51.925Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:51.925Z",
          "text": "Refining Eligibility Filters\n\nI'm focusing on how the existing candidate eligibility mask already handles exclusions. This means I don't need to reinvent eligibility logic, as the platform provides `validation_eligible_candidates.npz` which should cover it."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:38:51.925Z",
          "text": "Prioritizing Eligibility Checks\n\nI'm confirming that the existing candidate eligibility mask, specifically `validation_eligible_candidates.npz`, adequately covers exclusions. This means I can leverage this for ranking eligible candidates per user, rather than developing custom logic for it."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:38:51.925Z",
          "callId": "run_shell_command__call_185040",
          "name": "run_shell_command",
          "input": {
            "description": "Inspect validation truth distributions",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\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\ngrades_counts = []\ngrade3_counts = []\ngrade1_counts = []\nfor uid, items in graded.items():\n    grades_counts.append(len(items))\n    grade3_counts.append(sum(1 for g in items.values() if g == 3))\n    grade1_counts.append(sum(1 for g in items.values() if g == 1))\n\nprint(f'Users with any graded item: {len(graded)} / {len(targets)} ({len(graded)/len(targets)*100:.1f}%)')\nprint(f'Items per user: mean={np.mean(grades_counts):.2f}, median={np.median(grades_counts)}, max={np.max(grades_counts)}')\nprint(f'Grade 3 per user: mean={np.mean(grade3_counts):.2f}, Grade 1 per user: mean={np.mean(grade1_counts):.2f}')\ntotal_g3 = sum(grade3_counts)\ntotal_g1 = sum(grade1_counts)\nprint(f'Total grade 3 items: {total_g3}, Total grade 1 items: {total_g1}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_185040",
            "name": "run_shell_command",
            "args": {
              "description": "Inspect validation truth distributions",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\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\ngrades_counts = []\ngrade3_counts = []\ngrade1_counts = []\nfor uid, items in graded.items():\n    grades_counts.append(len(items))\n    grade3_counts.append(sum(1 for g in items.values() if g == 3))\n    grade1_counts.append(sum(1 for g in items.values() if g == 1))\n\nprint(f'Users with any graded item: {len(graded)} / {len(targets)} ({len(graded)/len(targets)*100:.1f}%)')\nprint(f'Items per user: mean={np.mean(grades_counts):.2f}, median={np.median(grades_counts)}, max={np.max(grades_counts)}')\nprint(f'Grade 3 per user: mean={np.mean(grade3_counts):.2f}, Grade 1 per user: mean={np.mean(grade1_counts):.2f}')\ntotal_g3 = sum(grade3_counts)\ntotal_g1 = sum(grade1_counts)\nprint(f'Total grade 3 items: {total_g3}, Total grade 1 items: {total_g1}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_185040",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Users with any graded item: 4263 / 8456 (50.4%)\nItems per user: mean=8.86, median=4.0, max=442\nGrade 3 per user: mean=1.04, Grade 1 per user: mean=7.82\nTotal grade 3 items: 4417, Total grade 1 items: 33352\nProcess Group PGID: 1840\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:38:53.177Z",
            "resultDisplay": "Users with any graded item: 4263 / 8456 (50.4%)\nItems per user: mean=8.86, median=4.0, max=442\nGrade 3 per user: mean=1.04, Grade 1 per user: mean=7.82\nTotal grade 3 items: 4417, Total grade 1 items: 33352",
            "description": "Inspect validation truth distributions",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:38:53.178Z",
            "content": "<untrusted_context>\nOutput: Users with any graded item: 4263 / 8456 (50.4%)\nItems per user: mean=8.86, median=4.0, max=442\nGrade 3 per user: mean=1.04, Grade 1 per user: mean=7.82\nTotal grade 3 items: 4417, Total grade 1 items: 33352\nProcess Group PGID: 1840\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "6ddbe5ae-12e0-455f-82bf-f2529c72499a"
        }
      ]
    },
    {
      "id": 18,
      "role": "agent",
      "timestamp": "2026-09-29T03:39:03.722Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:03.722Z",
          "text": "Analyzing User Engagement Metrics\n\nMy current focus is on understanding user engagement within the 7-day validation window. I've identified 4263 users with relevant items, averaging 8.86 items each, specifically tracking grade 3 (likes) and grade 1 (strong listens >= 80%). I'm evaluating performance using NDCG@10, aiming to quantify the gain for each grade."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:03.722Z",
          "text": "Calculating Engagement Gains\n\nMy analysis now incorporates the calculated gains: grade 3 items offer a 7x gain over grade 1 items. This is critical as we target top positions, particularly within the NDCG@10 metric. I'm also starting to inventory the available training data, noting the substantial volume of listens data."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:03.722Z",
          "text": "Evaluating Data Memory Usage\n\nI've confirmed that the `train_listens` dataset, though 305MB compressed, will fit comfortably into RAM with pandas, around 1.2GB. This leaves ample room for modeling. My next step is to conduct EDA on this dataset to identify features correlating with future organic discovery. I'm curious how many of the 30,000 candidate items actually appear in `train_listens`."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:39:03.722Z",
          "callId": "run_shell_command__call_323688",
          "name": "run_shell_command",
          "input": {
            "description": "Inspect candidate catalog data and distributions",
            "command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(10))\nprint('Popularity rank min/max:', catalog.popularity_rank.min(), catalog.popularity_rank.max())\nprint('Positive listens min/max:', catalog.positive_listens.min(), catalog.positive_listens.max())\nprint('Organic positive listens min/max:', catalog.organic_positive_listens.min(), catalog.organic_positive_listens.max())\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_323688",
            "name": "run_shell_command",
            "args": {
              "description": "Inspect candidate catalog data and distributions",
              "command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(10))\nprint('Popularity rank min/max:', catalog.popularity_rank.min(), catalog.popularity_rank.max())\nprint('Positive listens min/max:', catalog.positive_listens.min(), catalog.positive_listens.max())\nprint('Organic positive listens min/max:', catalog.organic_positive_listens.min(), catalog.organic_positive_listens.max())\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_323688",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n5  5463340             22746                     17398                6\n6  8213481             22505                     17071                7\n7   906358             22238                     17183                8\n8  2859641             18380                     13704                9\n9  3033749             16682                     12267               10\n       item_id  positive_listens  organic_positive_listens  popularity_rank\n29990  1044085               163                        83            29991\n29991  1159301               163                         2            29992\n29992  1174892               163                       122            29993\n29993  1235235               163                        29            29994\n29994  1239621               163                        86            29995\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\nPopularity rank min/max: 1 30000\nPositive listens min/max: 163 26615\nOrganic positive listens min/max: 0 20620\nProcess Group PGID: 1860\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:39:04.474Z",
            "resultDisplay": "item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n5  5463340             22746                     17398                6\n6  8213481             22505                     17071                7\n7   906358             22238                     17183                8\n8  2859641             18380                     13704                9\n9  3033749             16682                     12267               10\n       item_id  positive_listens  organic_positive_listens  popularity_rank\n29990  1044085               163                        83            29991\n29991  1159301               163                         2            29992\n29992  1174892               163                       122            29993\n29993  1235235               163                        29            29994\n29994  1239621               163                        86            29995\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\nPopularity rank min/max: 1 30000\nPositive listens min/max: 163 26615\nOrganic positive listens min/max: 0 20620",
            "description": "Inspect candidate catalog data and distributions",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:39:04.475Z",
            "content": "<untrusted_context>\nOutput: item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n5  5463340             22746                     17398                6\n6  8213481             22505                     17071                7\n7   906358             22238                     17183                8\n8  2859641             18380                     13704                9\n9  3033749             16682                     12267               10\n       item_id  positive_listens  organic_positive_listens  popularity_rank\n29990  1044085               163                        83            29991\n29991  1159301               163                         2            29992\n29992  1174892               163                       122            29993\n29993  1235235               163                        29            29994\n29994  1239621               163                        86            29995\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\nPopularity rank min/max: 1 30000\nPositive listens min/max: 163 26615\nOrganic positive listens min/max: 0 20620\nProcess Group PGID: 1860\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "9529931d-9b64-4cba-91cc-c13535c9404f"
        }
      ]
    },
    {
      "id": 19,
      "role": "agent",
      "timestamp": "2026-09-29T03:39:14.355Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:14.355Z",
          "text": "Confirming Catalog Inclusion\n\nI'm verifying that all items designated in the validation ground truth are indeed present within the 30,000-item catalog. My current checks confirm this is the case, as the `validation_truth` logic inherently filters for catalog membership."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:39:14.355Z",
          "callId": "run_shell_command__call_153337",
          "name": "run_shell_command",
          "input": {
            "description": "Check whether graded items appeared in user's prior train listens",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\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# Sample 100 users from graded\nsample_uids = list(graded.keys())[:100]\n\n# Check train_listens for these users\n# Read train_listens with columns uid, item_id\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_sample = train[train.uid.isin(sample_uids)]\n\n# For the sample users, what fraction of graded items were in train_sample?\nin_train_count = 0\ntotal_graded_sample = 0\nfor uid in sample_uids:\n    user_train_items = set(train_sample[train_sample.uid == uid].item_id)\n    user_graded_items = set(graded[uid].keys())\n    in_train_count += len(user_graded_items.intersection(user_train_items))\n    total_graded_sample += len(user_graded_items)\n\nprint(f'Sample: {in_train_count} / {total_graded_sample} ({in_train_count/total_graded_sample*100:.2f}%) of graded items appeared in user train listens')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_153337",
            "name": "run_shell_command",
            "args": {
              "description": "Check whether graded items appeared in user's prior train listens",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\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# Sample 100 users from graded\nsample_uids = list(graded.keys())[:100]\n\n# Check train_listens for these users\n# Read train_listens with columns uid, item_id\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_sample = train[train.uid.isin(sample_uids)]\n\n# For the sample users, what fraction of graded items were in train_sample?\nin_train_count = 0\ntotal_graded_sample = 0\nfor uid in sample_uids:\n    user_train_items = set(train_sample[train_sample.uid == uid].item_id)\n    user_graded_items = set(graded[uid].keys())\n    in_train_count += len(user_graded_items.intersection(user_train_items))\n    total_graded_sample += len(user_graded_items)\n\nprint(f'Sample: {in_train_count} / {total_graded_sample} ({in_train_count/total_graded_sample*100:.2f}%) of graded items appeared in user train listens')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_153337",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Sample: 0 / 858 (0.00%) of graded items appeared in user train listens\nProcess Group PGID: 1877\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:39:18.517Z",
            "resultDisplay": "Sample: 0 / 858 (0.00%) of graded items appeared in user train listens",
            "description": "Check whether graded items appeared in user's prior train listens",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:39:18.518Z",
            "content": "<untrusted_context>\nOutput: Sample: 0 / 858 (0.00%) of graded items appeared in user train listens\nProcess Group PGID: 1877\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "499098a9-47d2-4885-a621-bb589cab16bf"
        }
      ]
    },
    {
      "id": 20,
      "role": "agent",
      "timestamp": "2026-09-29T03:39:22.810Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:22.810Z",
          "text": "Verifying User Engagement\n\nI'm currently cross-referencing eligible items with user listening history. My focus is on clarifying the precise definition of \"eligible\" – specifically, whether it encompasses items never listened to, or if it also excludes those with past like/dislike interactions. This will ensure accurate analysis of graded items."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:39:22.810Z",
          "callId": "run_shell_command__call_246821",
          "name": "run_shell_command",
          "input": {
            "description": "Check if any user train items are marked eligible in validation candidate set",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nuid = 100\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nuser_train = set(train[train.uid == uid].item_id)\nprint('User 100 total train items:', len(user_train))\n# Check how many user_train items are in eligibility.item_ids\noverlap = user_train.intersection(set(eligibility.item_ids))\nprint('User 100 train items in catalog:', len(overlap))\n# Check how many are eligible\neligible_overlap = sum(eligibility.is_eligible(uid, it) for it in overlap)\nprint('User 100 train items that are eligible:', eligible_overlap)\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_246821",
            "name": "run_shell_command",
            "args": {
              "description": "Check if any user train items are marked eligible in validation candidate set",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nuid = 100\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nuser_train = set(train[train.uid == uid].item_id)\nprint('User 100 total train items:', len(user_train))\n# Check how many user_train items are in eligibility.item_ids\noverlap = user_train.intersection(set(eligibility.item_ids))\nprint('User 100 train items in catalog:', len(overlap))\n# Check how many are eligible\neligible_overlap = sum(eligibility.is_eligible(uid, it) for it in overlap)\nprint('User 100 train items that are eligible:', eligible_overlap)\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_246821",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: User 100 total train items: 830\nUser 100 train items in catalog: 682\nUser 100 train items that are eligible: 0\nProcess Group PGID: 1900\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:39:24.007Z",
            "resultDisplay": "User 100 total train items: 830\nUser 100 train items in catalog: 682\nUser 100 train items that are eligible: 0",
            "description": "Check if any user train items are marked eligible in validation candidate set",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:39:24.008Z",
            "content": "<untrusted_context>\nOutput: User 100 total train items: 830\nUser 100 train items in catalog: 682\nUser 100 train items that are eligible: 0\nProcess Group PGID: 1900\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "009008c9-5fa3-4818-a277-00089983370c"
        }
      ]
    },
    {
      "id": 21,
      "role": "agent",
      "timestamp": "2026-09-29T03:39:33.819Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:33.819Z",
          "text": "Prioritizing Novelty\n\nI'm focusing on building a robust ranking model for music discovery. My core insight is that every user has a history of listened-to items marked as ineligible. This means my primary objective is to ensure pure novelty and introduce users to entirely new content they haven't encountered before."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:33.819Z",
          "text": "Refining Novelty Prediction\n\nI'm now focused on predicting which entirely new tracks users will actively engage with. This involves a classic collaborative filtering challenge: ranking unseen items based on implicit and explicit feedback. I'm evaluating methods like Item-Item CF and implicit matrix factorization to identify top candidates."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:33.819Z",
          "text": "Exploring Matrix Factorization\n\nI'm investigating matrix factorization techniques like Truncated SVD and item-item similarity for predicting user engagement with novel music. The interaction matrix, even with 30,000 items, is surprisingly manageable, making these approaches computationally feasible."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:39:33.819Z",
          "callId": "run_shell_command__call_186028",
          "name": "run_shell_command",
          "input": {
            "description": "Check interaction counts of target users with catalog items",
            "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = likes[likes.uid.isin(target_uids) & likes.item_id.isin(catalog_items)]\nprint(f'Target user likes in catalog: {len(target_likes)} rows, {target_likes.uid.nunique()} users, {target_likes.item_id.nunique()} items')\n\n# Check listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nprint(f'Target user listens in catalog: {len(target_listens)} rows, {target_listens.uid.nunique()} users, {target_listens.item_id.nunique()} items')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_186028",
            "name": "run_shell_command",
            "args": {
              "description": "Check interaction counts of target users with catalog items",
              "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = likes[likes.uid.isin(target_uids) & likes.item_id.isin(catalog_items)]\nprint(f'Target user likes in catalog: {len(target_likes)} rows, {target_likes.uid.nunique()} users, {target_likes.item_id.nunique()} items')\n\n# Check listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nprint(f'Target user listens in catalog: {len(target_listens)} rows, {target_listens.uid.nunique()} users, {target_listens.item_id.nunique()} items')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_186028",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Target user likes in catalog: 472889 rows, 6874 users, 28606 items\nTarget user listens in catalog: 31062540 rows, 8452 users, 30000 items\nProcess Group PGID: 1922\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:39:41.862Z",
            "resultDisplay": "Target user likes in catalog: 472889 rows, 6874 users, 28606 items\nTarget user listens in catalog: 31062540 rows, 8452 users, 30000 items",
            "description": "Check interaction counts of target users with catalog items",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:39:41.863Z",
            "content": "<untrusted_context>\nOutput: Target user likes in catalog: 472889 rows, 6874 users, 28606 items\nTarget user listens in catalog: 31062540 rows, 8452 users, 30000 items\nProcess Group PGID: 1922\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "8b8d34e5-da72-4688-b9ce-71acd81fc0fd"
        }
      ]
    },
    {
      "id": 22,
      "role": "agent",
      "timestamp": "2026-09-29T03:39:46.202Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:39:46.202Z",
          "text": "Analyzing Catalog Data\n\nI'm examining the catalog listening data. It appears all 30,000 catalog items have been heard by our target users, and a significant majority, over 8400, of our target users have engaged with the catalog. This is quite promising."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:39:46.202Z",
          "callId": "run_shell_command__call_324445",
          "name": "run_shell_command",
          "input": {
            "description": "Calculate unique user-item interaction pairs and matrix density",
            "command": "python3 -c \"\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nunique_pairs = len(target_listens.drop_duplicates())\nprint(f'Distinct (uid, item) pairs: {unique_pairs}')\nprint(f'Sparsity: {unique_pairs / (len(target_uids) * len(catalog_items)) * 100:.2f}% of user-catalog matrix is filled!')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_324445",
            "name": "run_shell_command",
            "args": {
              "description": "Calculate unique user-item interaction pairs and matrix density",
              "command": "python3 -c \"\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nunique_pairs = len(target_listens.drop_duplicates())\nprint(f'Distinct (uid, item) pairs: {unique_pairs}')\nprint(f'Sparsity: {unique_pairs / (len(target_uids) * len(catalog_items)) * 100:.2f}% of user-catalog matrix is filled!')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_324445",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Distinct (uid, item) pairs: 7868664\nSparsity: 3.10% of user-catalog matrix is filled!\nProcess Group PGID: 1949\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:39:55.410Z",
            "resultDisplay": "Distinct (uid, item) pairs: 7868664\nSparsity: 3.10% of user-catalog matrix is filled!",
            "description": "Calculate unique user-item interaction pairs and matrix density",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:39:55.411Z",
            "content": "<untrusted_context>\nOutput: Distinct (uid, item) pairs: 7868664\nSparsity: 3.10% of user-catalog matrix is filled!\nProcess Group PGID: 1949\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "465732da-b5f6-43ba-96c4-94c92ef5ad3a"
        }
      ]
    },
    {
      "id": 23,
      "role": "agent",
      "timestamp": "2026-09-29T03:40:10.420Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:40:10.420Z",
          "text": "Analyzing Data Density\n\nI've identified that the current matrix density for collaborative filtering is a remarkable 3.10%, significantly exceeding the typical 0.01%-0.1% range. This translates to approximately 7.87 million distinct (uid, item) pairs. I'm also noting the presence of `train_likes.parquet` and other training data files."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:40:10.420Z",
          "text": "Exploring New Data Sources\n\nI'm now considering additional data files such as `dislikes.parquet`, `train_unlikes.parquet`, and `train_undislikes.parquet`. I'm also examining `validation_preference_state.parquet` for explicit states and noting features like `played_ratio_pct` and `is_organic` within `train_listens.parquet`. The timing of data splits is also being refined."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:40:10.420Z",
          "text": "Calculating Item Similarities\n\nI'm calculating item similarities, considering both dense and sparse approaches for the similarity matrix $S$. A dense $30000 \\times 30000$ matrix would fit in RAM, but I'm also exploring sparse representations using top-$k$ neighbors to significantly reduce memory usage. This could bring the memory footprint down to around 50 MB."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:40:10.420Z",
          "text": "Testing Matrix Inversion\n\nI'm evaluating the feasibility of inverting a large $30000 \\times 30000$ matrix within 16 GB of RAM, a crucial step for the EASE algorithm. This inversion is a primary bottleneck I need to overcome."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:40:10.420Z",
          "callId": "run_shell_command__call_212973",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nA = A.T @ A + 100 * np.eye(8456, dtype=np.float32)\nt0 = time.time()\ninv = np.linalg.inv(A)\nt1 = time.time()\nprint(f'8456x8456 matrix inverse time: {t1 - t0:.2f} seconds')\n\"",
            "description": "Benchmark matrix inverse time for 8456x8456 matrix"
          },
          "recorded": {
            "id": "run_shell_command__call_212973",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nA = A.T @ A + 100 * np.eye(8456, dtype=np.float32)\nt0 = time.time()\ninv = np.linalg.inv(A)\nt1 = time.time()\nprint(f'8456x8456 matrix inverse time: {t1 - t0:.2f} seconds')\n\"",
              "description": "Benchmark matrix inverse time for 8456x8456 matrix"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_212973",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: 8456x8456 matrix inverse time: 16.49 seconds\nProcess Group PGID: 1971\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:40:31.539Z",
            "resultDisplay": "8456x8456 matrix inverse time: 16.49 seconds",
            "description": "Benchmark matrix inverse time for 8456x8456 matrix",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:40:31.539Z",
            "content": "<untrusted_context>\nOutput: 8456x8456 matrix inverse time: 16.49 seconds\nProcess Group PGID: 1971\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "0c6c2acc-b700-427d-9beb-e3d79bfc14bb"
        }
      ]
    },
    {
      "id": 24,
      "role": "agent",
      "timestamp": "2026-09-29T03:40:35.318Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:40:35.318Z",
          "text": "Exploring SVD Options\n\nI'm currently evaluating different Singular Value Decomposition methods. Initial tests with full inversion on an $8456 \\times 8456$ matrix were promising, achieving completion in under 17 seconds. My next step is to explore Truncated SVD and Randomized SVD via scikit-learn, particularly focusing on their performance with a sparse $8456 \\times 30000$ matrix."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:40:35.318Z",
          "callId": "run_shell_command__call_117378",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport numpy as np\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    t1 = time.time()\n    print(f'TruncatedSVD k={k}: {t1 - t0:.2f} s, shape U={U.shape}, shape V={V.shape}')\n\"",
            "description": "Benchmark TruncatedSVD on realistic matrix dimensions"
          },
          "recorded": {
            "id": "run_shell_command__call_117378",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport numpy as np\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    t1 = time.time()\n    print(f'TruncatedSVD k={k}: {t1 - t0:.2f} s, shape U={U.shape}, shape V={V.shape}')\n\"",
              "description": "Benchmark TruncatedSVD on realistic matrix dimensions"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_117378",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: TruncatedSVD k=64: 2.83 s, shape U=(8456, 64), shape V=(64, 30000)\nTruncatedSVD k=128: 4.04 s, shape U=(8456, 128), shape V=(128, 30000)\nTruncatedSVD k=256: 7.62 s, shape U=(8456, 256), shape V=(256, 30000)\nProcess Group PGID: 1981\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:41:02.080Z",
            "resultDisplay": "TruncatedSVD k=64: 2.83 s, shape U=(8456, 64), shape V=(64, 30000)\nTruncatedSVD k=128: 4.04 s, shape U=(8456, 128), shape V=(128, 30000)\nTruncatedSVD k=256: 7.62 s, shape U=(8456, 256), shape V=(256, 30000)",
            "description": "Benchmark TruncatedSVD on realistic matrix dimensions",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:41:02.080Z",
            "content": "<untrusted_context>\nOutput: TruncatedSVD k=64: 2.83 s, shape U=(8456, 64), shape V=(64, 30000)\nTruncatedSVD k=128: 4.04 s, shape U=(8456, 128), shape V=(128, 30000)\nTruncatedSVD k=256: 7.62 s, shape U=(8456, 256), shape V=(256, 30000)\nProcess Group PGID: 1981\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "c837c93e-c42e-4827-8b6e-d14c4e5e59f4"
        }
      ]
    },
    {
      "id": 25,
      "role": "agent",
      "timestamp": "2026-09-29T03:41:09.300Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:41:09.300Z",
          "text": "Exploring Similarity Options\n\nMy current focus is on evaluating various item-item similarity approaches, noting that TruncatedSVD with k=128 is remarkably fast at 4 seconds. I'm now planning benchmarks for cosine similarity, BM25, and co-occurrence, aiming to compare their performance against the SVD results, especially considering the $8456 \\times 30000$ dimensions of the rating matrix."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:41:09.300Z",
          "callId": "run_shell_command__call_325906",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport scipy.sparse as sp\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\"",
            "description": "Benchmark user-user similarity and prediction time"
          },
          "recorded": {
            "id": "run_shell_command__call_325906",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport scipy.sparse as sp\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\"",
              "description": "Benchmark user-user similarity and prediction time"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_325906",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 5, in <module>\nNameError: name 'np' is not defined. Did you mean: 'sp'?\nExit Code: 1\nProcess Group PGID: 2005\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:41:09.988Z",
            "resultDisplay": "Traceback (most recent call last):\n  File \"<string>\", line 5, in <module>\nNameError: name 'np' is not defined. Did you mean: 'sp'?",
            "description": "Benchmark user-user similarity and prediction time",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:41:09.989Z",
            "content": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 5, in <module>\nNameError: name 'np' is not defined. Did you mean: 'sp'?\nExit Code: 1\nProcess Group PGID: 2005\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "ace6ed52-1d0b-41a3-ac2a-f6c44c8b7ff3"
        }
      ]
    },
    {
      "id": 26,
      "role": "agent",
      "timestamp": "2026-09-29T03:41:15.827Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:41:15.827Z",
          "callId": "run_shell_command__call_134961",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark user-user similarity and prediction time with numpy import",
            "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_134961",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark user-user similarity and prediction time with numpy import",
              "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_134961",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: R @ R.T took: 5.92 s, shape=(8456, 8456), nonzeros=71503936\nU_sim @ R took: 152.54 s\nProcess Group PGID: 2022\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:44:05.709Z",
            "resultDisplay": "R @ R.T took: 5.92 s, shape=(8456, 8456), nonzeros=71503936\nU_sim @ R took: 152.54 s",
            "description": "Benchmark user-user similarity and prediction time with numpy import",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:44:05.710Z",
            "content": "<untrusted_context>\nOutput: R @ R.T took: 5.92 s, shape=(8456, 8456), nonzeros=71503936\nU_sim @ R took: 152.54 s\nProcess Group PGID: 2022\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "44681be7-facc-48e9-a7a0-eb1e4390eef1"
        }
      ]
    },
    {
      "id": 27,
      "role": "agent",
      "timestamp": "2026-09-29T03:44:09.636Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:09.636Z",
          "text": "Analyzing Performance Bottlenecks\n\nI'm investigating why the `U_sim @ R` operation with a large, dense $8456 \\times 8456$ matrix took 152 seconds. It seems dense matrix multiplication is the culprit here, significantly impacting performance. I'm now turning my attention to understanding the performance within TruncatedSVD."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:44:09.636Z",
          "callId": "run_shell_command__call_310001",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark dense matrix multiplication time",
            "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(128, 30000).astype(np.float32)\nt0 = time.time()\nscores = U @ V\nt1 = time.time()\nprint(f'Dense U @ V took: {t1 - t0:.3f} s, shape={scores.shape}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_310001",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark dense matrix multiplication time",
              "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(128, 30000).astype(np.float32)\nt0 = time.time()\nscores = U @ V\nt1 = time.time()\nprint(f'Dense U @ V took: {t1 - t0:.3f} s, shape={scores.shape}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_310001",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Dense U @ V took: 0.408 s, shape=(8456, 30000)\nProcess Group PGID: 2057\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:44:10.401Z",
            "resultDisplay": "Dense U @ V took: 0.408 s, shape=(8456, 30000)",
            "description": "Benchmark dense matrix multiplication time",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:44:10.402Z",
            "content": "<untrusted_context>\nOutput: Dense U @ V took: 0.408 s, shape=(8456, 30000)\nProcess Group PGID: 2057\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "12b8dad7-a47f-4a04-bcd2-0202f8f4dade"
        }
      ]
    },
    {
      "id": 28,
      "role": "agent",
      "timestamp": "2026-09-29T03:44:14.842Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:14.842Z",
          "text": "Exploring Item-Item Efficiency\n\nI'm currently investigating the computational feasibility of item-item similarity, specifically focusing on sparse matrix representations and their performance implications. My current analysis centers on the speed of scoring all users against items when only retaining the top-K most similar items per item, a critical step for optimizing recommendations."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:44:14.842Z",
          "callId": "run_shell_command__call_120186",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark sparse R @ S matrix multiplication",
            "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\n# sparse S with 100 nonzeros per column\ncols = np.repeat(np.arange(30000), 100)\nrows = np.random.randint(0, 30000, size=30000 * 100)\ndata = np.random.rand(30000 * 100).astype(np.float32)\nS = sp.csr_matrix((data, (rows, cols)), shape=(30000, 30000))\nt0 = time.time()\nscores = R @ S\nt1 = time.time()\nprint(f'Sparse R @ S (top-100 items) took: {t1 - t0:.3f} s, nonzeros={scores.nnz}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_120186",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark sparse R @ S matrix multiplication",
              "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\n# sparse S with 100 nonzeros per column\ncols = np.repeat(np.arange(30000), 100)\nrows = np.random.randint(0, 30000, size=30000 * 100)\ndata = np.random.rand(30000 * 100).astype(np.float32)\nS = sp.csr_matrix((data, (rows, cols)), shape=(30000, 30000))\nt0 = time.time()\nscores = R @ S\nt1 = time.time()\nprint(f'Sparse R @ S (top-100 items) took: {t1 - t0:.3f} s, nonzeros={scores.nnz}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_120186",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Sparse R @ S (top-100 items) took: 6.638 s, nonzeros=241554916\nProcess Group PGID: 2067\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:44:33.076Z",
            "resultDisplay": "Sparse R @ S (top-100 items) took: 6.638 s, nonzeros=241554916",
            "description": "Benchmark sparse R @ S matrix multiplication",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:44:33.077Z",
            "content": "<untrusted_context>\nOutput: Sparse R @ S (top-100 items) took: 6.638 s, nonzeros=241554916\nProcess Group PGID: 2067\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "fbe2252d-bdbd-42ab-bebf-a2e3f7ec0a23"
        }
      ]
    },
    {
      "id": 29,
      "role": "agent",
      "timestamp": "2026-09-29T03:44:39.380Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:39.380Z",
          "text": "Analyzing Validation Data\n\nI'm currently examining the validation data to define track relevance. My focus is on understanding what constitutes a \"liked\" organic track versus one that was simply listened to. This will inform how I construct the interaction matrix and associated weights."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:39.380Z",
          "text": "Refining Interaction Signals\n\nI'm now incorporating signals from the training data. Specifically, I'm analyzing `train_likes` and `train_listens` to better define taste profiles. This involves considering explicit likes, weighted listens based on `played_ratio_pct` and organic status, and recency through timestamps. Negative signals from `train_dislikes` are also being evaluated."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:44:39.380Z",
          "callId": "run_shell_command__call_179286",
          "name": "run_shell_command",
          "input": {
            "description": "Measure load time of train_listens.parquet",
            "command": "python3 -c \"\nimport pandas as pd\nimport time\n\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'Loading train_listens took {t1 - t0:.2f} s. Rows: {len(df)}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_179286",
            "name": "run_shell_command",
            "args": {
              "description": "Measure load time of train_listens.parquet",
              "command": "python3 -c \"\nimport pandas as pd\nimport time\n\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'Loading train_listens took {t1 - t0:.2f} s. Rows: {len(df)}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_179286",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Loading train_listens took 0.53 s. Rows: 43484159\nProcess Group PGID: 2090\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:44:40.719Z",
            "resultDisplay": "Loading train_listens took 0.53 s. Rows: 43484159",
            "description": "Measure load time of train_listens.parquet",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:44:40.721Z",
            "content": "<untrusted_context>\nOutput: Loading train_listens took 0.53 s. Rows: 43484159\nProcess Group PGID: 2090\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "ebf1d74f-3e8e-40fa-b8f1-916cb03e32cf"
        }
      ]
    },
    {
      "id": 30,
      "role": "agent",
      "timestamp": "2026-09-29T03:44:54.312Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:54.312Z",
          "text": "Loading Data Rapidly\n\nI've successfully loaded a substantial dataset of 43.4 million rows in a mere 0.53 seconds using pyarrow – impressive speed! My current focus is on analyzing the connection between user listening histories and catalog items, particularly within the `train_listens` dataset, which currently involves 8,456 target users."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:54.312Z",
          "text": "Evaluating Model Viability\n\nI'm currently exploring whether non-catalog items within `train_listens` offer significant value for our models, given the extensive interaction data target users already have with catalog items. I’m setting up an evaluation framework to test this experimentally, aiming to quickly assess model performance on the public validation set."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:44:54.312Z",
          "text": "Designing Interaction Matrix\n\nI'm now focusing on the core of collaborative filtering: defining the user-item interaction matrix. I'm weighing several options for entry values, from simple binary presence to weighted counts incorporating event type and even time decay, aiming for the most informative representation for my models."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:44:54.312Z",
          "callId": "run_shell_command__call_230814",
          "name": "run_shell_command",
          "input": {
            "description": "Run TruncatedSVD baseline on public validation set",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nprint(f'Target users: {len(target_uids)}, Catalog items: {len(item_ids)}')\n\n# Load train listens for target users & catalog items\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s: {len(listens)} rows')\n\n# Build user-item positive counts\n# Only positive listens (played_ratio_pct >= 50)\npos_listens = listens[listens.played_ratio_pct >= 50]\nu_idx = pos_listens.uid.map(uid_to_idx).to_numpy()\ni_idx = pos_listens.item_id.map(item_to_idx).to_numpy()\n\n# Sparse matrix with counts\nR_counts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = R_counts.copy()\nR_log.data = np.log1p(R_log.data)\n\nprint(f'R_log constructed, nnz={R_log.nnz}')\n\n# Evaluate SVD for different k\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    \n    # Predict scores for all users\n    # Batch predict top 100 eligible per user\n    scores = U @ V # shape (8456, 30000)\n    \n    # Mask ineligible items by setting their score to -infinity\n    # Use packed eligibility\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        # Top 100\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'TruncatedSVD k={k}: {res} (took {time.time()-t0:.2f}s)')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_230814",
            "name": "run_shell_command",
            "args": {
              "description": "Run TruncatedSVD baseline on public validation set",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nprint(f'Target users: {len(target_uids)}, Catalog items: {len(item_ids)}')\n\n# Load train listens for target users & catalog items\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s: {len(listens)} rows')\n\n# Build user-item positive counts\n# Only positive listens (played_ratio_pct >= 50)\npos_listens = listens[listens.played_ratio_pct >= 50]\nu_idx = pos_listens.uid.map(uid_to_idx).to_numpy()\ni_idx = pos_listens.item_id.map(item_to_idx).to_numpy()\n\n# Sparse matrix with counts\nR_counts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = R_counts.copy()\nR_log.data = np.log1p(R_log.data)\n\nprint(f'R_log constructed, nnz={R_log.nnz}')\n\n# Evaluate SVD for different k\nfor k in [64, 128, 256]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    \n    # Predict scores for all users\n    # Batch predict top 100 eligible per user\n    scores = U @ V # shape (8456, 30000)\n    \n    # Mask ineligible items by setting their score to -infinity\n    # Use packed eligibility\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        # Top 100\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'TruncatedSVD k={k}: {res} (took {time.time()-t0:.2f}s)')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_230814",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Target users: 8456, Catalog items: 30000\nFiltered listens in 8.21s: 31062540 rows\nR_log constructed, nnz=5514505\nTruncatedSVD k=64: {'quality_ndcg@10': 0.017838, 'quality_users': 4263} (took 3.74s)\nTruncatedSVD k=128: {'quality_ndcg@10': 0.019115, 'quality_users': 4263} (took 4.66s)\nTruncatedSVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (took 7.17s)\nProcess Group PGID: 2111\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:45:21.810Z",
            "resultDisplay": "Target users: 8456, Catalog items: 30000\nFiltered listens in 8.21s: 31062540 rows\nR_log constructed, nnz=5514505\nTruncatedSVD k=64: {'quality_ndcg@10': 0.017838, 'quality_users': 4263} (took 3.74s)\nTruncatedSVD k=128: {'quality_ndcg@10': 0.019115, 'quality_users': 4263} (took 4.66s)\nTruncatedSVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (took 7.17s)",
            "description": "Run TruncatedSVD baseline on public validation set",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:45:21.811Z",
            "content": "<untrusted_context>\nOutput: Target users: 8456, Catalog items: 30000\nFiltered listens in 8.21s: 31062540 rows\nR_log constructed, nnz=5514505\nTruncatedSVD k=64: {'quality_ndcg@10': 0.017838, 'quality_users': 4263} (took 3.74s)\nTruncatedSVD k=128: {'quality_ndcg@10': 0.019115, 'quality_users': 4263} (took 4.66s)\nTruncatedSVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (took 7.17s)\nProcess Group PGID: 2111\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "9d625f8e-b1b9-416e-b68d-6729b4240be7"
        }
      ]
    },
    {
      "id": 31,
      "role": "agent",
      "timestamp": "2026-09-29T03:45:40.145Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:45:40.145Z",
          "text": "Exploring SVD Improvements\n\nI'm seeing significant gains with TruncatedSVD, with catalog popularity nearly doubling at k=256. This progress, achieved in just 7 seconds, is very encouraging. I'm now eager to investigate further model possibilities to build on these promising results."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:45:40.145Z",
          "callId": "update_topic__call_240474",
          "name": "update_topic",
          "input": {
            "title": "Model Architecture Exploration & Feature Engineering",
            "summary": "We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.",
            "strategic_intent": "Develop, experiment with, and optimize diverse recommendation models and feature weighting."
          },
          "recorded": {
            "id": "update_topic__call_240474",
            "name": "update_topic",
            "args": {
              "title": "Model Architecture Exploration & Feature Engineering",
              "summary": "We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.",
              "strategic_intent": "Develop, experiment with, and optimize diverse recommendation models and feature weighting."
            },
            "result": [
              {
                "functionResponse": {
                  "id": "update_topic__call_240474",
                  "name": "update_topic",
                  "response": {
                    "output": "Current topic: \"Model Architecture Exploration & Feature Engineering\"\nTopic summary: We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.\n\nStrategic Intent: Develop, experiment with, and optimize diverse recommendation models and feature weighting."
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:45:40.147Z",
            "resultDisplay": "## 📂 Topic: **Model Architecture Exploration & Feature Engineering**\n\n**Summary:**\nWe established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.\n\n> [!STRATEGY]\n> **Intent:** Develop, experiment with, and optimize diverse recommendation models and feature weighting.",
            "description": "Update topic to: \"Model Architecture Exploration & Feature Engineering\"",
            "displayName": "Update Topic Context",
            "renderOutputAsMarkdown": true
          },
          "result": {
            "timestamp": "2026-09-29T03:45:41.660Z",
            "content": "Current topic: \"Model Architecture Exploration & Feature Engineering\"\nTopic summary: We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.\n\nStrategic Intent: Develop, experiment with, and optimize diverse recommendation models and feature weighting.",
            "isError": false
          },
          "responseMessageId": "61f7be95-9161-4a88-b3e4-db19e606effa"
        }
      ]
    },
    {
      "id": 32,
      "role": "agent",
      "timestamp": "2026-09-29T03:45:55.984Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:45:55.984Z",
          "text": "Exploring Recommendation Efficacy\n\nI'm currently dissecting the effectiveness of recommendation systems for discovery. My focus is on analyzing the available signals, particularly user likes. I've noted that 'grade 3' in validation preference state signifies organic liking, a crucial insight for understanding user engagement."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:45:55.984Z",
          "text": "Analyzing User Engagement Metrics\n\nI'm now prioritizing the impact of organic listens and completion ratios on predicting taste. I'm also looking into how to properly interpret \"skips\" based on the `played_ratio_pct` distribution and the temporal relevance of data, considering potential taste drift over time."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:45:55.984Z",
          "callId": "run_shell_command__call_124072",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\n# Filter positive listens\npos = listens[listens.played_ratio_pct >= 50].copy()\nu_idx = pos.uid.map(uid_to_idx).to_numpy()\ni_idx = pos.item_id.map(item_to_idx).to_numpy()\ncounts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = counts.copy()\nR_log.data = np.log1p(R_log.data)\n\n# Test SVD with k=384 and k=512\nfor k in [256, 384, 512]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    scores = U @ V\n    \n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'SVD k={k}: {res} ({time.time()-t0:.2f}s)')\n\"",
            "description": "Test SVD with larger latent dimensions (256, 384, 512)"
          },
          "recorded": {
            "id": "run_shell_command__call_124072",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\n# Filter positive listens\npos = listens[listens.played_ratio_pct >= 50].copy()\nu_idx = pos.uid.map(uid_to_idx).to_numpy()\ni_idx = pos.item_id.map(item_to_idx).to_numpy()\ncounts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = counts.copy()\nR_log.data = np.log1p(R_log.data)\n\n# Test SVD with k=384 and k=512\nfor k in [256, 384, 512]:\n    t0 = time.time()\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_log)\n    V = svd.components_\n    scores = U @ V\n    \n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    \n    res = score_rankings(ranked, graded)\n    print(f'SVD k={k}: {res} ({time.time()-t0:.2f}s)')\n\"",
              "description": "Test SVD with larger latent dimensions (256, 384, 512)"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_124072",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: SVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (7.30s)\nSVD k=384: {'quality_ndcg@10': 0.019694, 'quality_users': 4263} (12.11s)\nSVD k=512: {'quality_ndcg@10': 0.020185, 'quality_users': 4263} (16.01s)\nProcess Group PGID: 2145\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:46:43.316Z",
            "resultDisplay": "SVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (7.30s)\nSVD k=384: {'quality_ndcg@10': 0.019694, 'quality_users': 4263} (12.11s)\nSVD k=512: {'quality_ndcg@10': 0.020185, 'quality_users': 4263} (16.01s)",
            "description": "Test SVD with larger latent dimensions (256, 384, 512)",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:46:43.317Z",
            "content": "<untrusted_context>\nOutput: SVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (7.30s)\nSVD k=384: {'quality_ndcg@10': 0.019694, 'quality_users': 4263} (12.11s)\nSVD k=512: {'quality_ndcg@10': 0.020185, 'quality_users': 4263} (16.01s)\nProcess Group PGID: 2145\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "567d0ca1-25cf-4506-891a-3f2643417c72"
        }
      ]
    },
    {
      "id": 33,
      "role": "agent",
      "timestamp": "2026-09-29T03:47:08.168Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:47:08.168Z",
          "text": "Exploring Collaborative Filtering\n\nI'm now shifting focus from SVD to Item-Item Collaborative Filtering. My current goal is to evaluate several weighting schemes (Cosine, BM25, TFIDF, Co-occurrence) within this framework, starting with understanding its core mechanics using the provided user-item matrix."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:47:08.168Z",
          "callId": "run_shell_command__call_253760",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.02, format='csr', dtype=np.float32)\n# Normalize columns (items) for cosine similarity\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R, norm='l2', axis=0) # unit norm items\n\nt0 = time.time()\n# compute block by block or sparse dot\n# Let's test R.dot(R.T.dot(R)) or similar:\n# Note: user predictions = R @ S = R @ (R.T @ R) = (R @ R.T) @ R !!\n# Notice the associativity of matrix multiplication!\n# (R @ R.T) is (8456 x 8456)!\n# And ((R @ R.T) @ R) has shape (8456 x 30000)!\n\"",
            "description": "Check matrix multiplication associativity for Item-Item vs User-User"
          },
          "recorded": {
            "id": "run_shell_command__call_253760",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.02, format='csr', dtype=np.float32)\n# Normalize columns (items) for cosine similarity\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R, norm='l2', axis=0) # unit norm items\n\nt0 = time.time()\n# compute block by block or sparse dot\n# Let's test R.dot(R.T.dot(R)) or similar:\n# Note: user predictions = R @ S = R @ (R.T @ R) = (R @ R.T) @ R !!\n# Notice the associativity of matrix multiplication!\n# (R @ R.T) is (8456 x 8456)!\n# And ((R @ R.T) @ R) has shape (8456 x 30000)!\n\"",
              "description": "Check matrix multiplication associativity for Item-Item vs User-User"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_253760",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: (empty)\nProcess Group PGID: 2179\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:47:20.371Z",
            "resultDisplay": "",
            "description": "Check matrix multiplication associativity for Item-Item vs User-User",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:47:20.373Z",
            "content": "<untrusted_context>\nOutput: (empty)\nProcess Group PGID: 2179\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "0abc90b0-1cdd-4351-910c-3e232456bda6"
        }
      ]
    },
    {
      "id": 34,
      "role": "agent",
      "timestamp": "2026-09-29T03:47:28.152Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:47:28.152Z",
          "text": "Exploring Mathematical Equivalence\n\nI'm currently examining the mathematical equivalence in item-item collaborative filtering. Specifically, I'm interested in how the formula transforms with and without top-k truncation, highlighting the user-user co-occurrence aspect."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:47:28.152Z",
          "callId": "run_shell_command__call_282301",
          "name": "run_shell_command",
          "input": {
            "description": "Benchmark fast block-wise top-K item similarity computation",
            "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom sklearn.preprocessing import normalize\n\n# Create normalized item matrix R: shape (N_users, N_items)\nN_users, N_items = 8456, 30000\nR = sp.random(N_users, N_items, density=0.02, format='csr', dtype=np.float32)\nR_items = normalize(R, norm='l2', axis=0) # columns are unit norm\n# Convert to csc for fast column slicing or csr of R.T\nRt = R_items.T.tocsr() # shape (30000, 8456)\n\nt0 = time.time()\nK = 100\nrows, cols, vals = [], [], []\nbatch_size = 1000\nfor start in range(0, N_items, batch_size):\n    end = min(start + batch_size, N_items)\n    block = Rt[start:end] # (batch, 8456)\n    sims = (block @ R_items).toarray() # (batch, 30000) dense\n    # zero out self similarity\n    for b in range(end - start):\n        sims[b, start + b] = 0.0\n        # top K\n        top_k = np.argpartition(sims[b], -K)[-K:]\n        top_k = top_k[sims[b, top_k] > 0]\n        rows.extend([start + b] * len(top_k))\n        cols.extend(top_k)\n        vals.extend(sims[b, top_k])\n\nS = sp.csr_matrix((vals, (rows, cols)), shape=(N_items, N_items), dtype=np.float32)\nt1 = time.time()\nprint(f'Constructed top-{K} item-item similarity in {t1 - t0:.2f} s, nnz={S.nnz}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_282301",
            "name": "run_shell_command",
            "args": {
              "description": "Benchmark fast block-wise top-K item similarity computation",
              "command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom sklearn.preprocessing import normalize\n\n# Create normalized item matrix R: shape (N_users, N_items)\nN_users, N_items = 8456, 30000\nR = sp.random(N_users, N_items, density=0.02, format='csr', dtype=np.float32)\nR_items = normalize(R, norm='l2', axis=0) # columns are unit norm\n# Convert to csc for fast column slicing or csr of R.T\nRt = R_items.T.tocsr() # shape (30000, 8456)\n\nt0 = time.time()\nK = 100\nrows, cols, vals = [], [], []\nbatch_size = 1000\nfor start in range(0, N_items, batch_size):\n    end = min(start + batch_size, N_items)\n    block = Rt[start:end] # (batch, 8456)\n    sims = (block @ R_items).toarray() # (batch, 30000) dense\n    # zero out self similarity\n    for b in range(end - start):\n        sims[b, start + b] = 0.0\n        # top K\n        top_k = np.argpartition(sims[b], -K)[-K:]\n        top_k = top_k[sims[b, top_k] > 0]\n        rows.extend([start + b] * len(top_k))\n        cols.extend(top_k)\n        vals.extend(sims[b, top_k])\n\nS = sp.csr_matrix((vals, (rows, cols)), shape=(N_items, N_items), dtype=np.float32)\nt1 = time.time()\nprint(f'Constructed top-{K} item-item similarity in {t1 - t0:.2f} s, nnz={S.nnz}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_282301",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Constructed top-100 item-item similarity in 29.30 s, nnz=3000000\nProcess Group PGID: 2197\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:48:09.615Z",
            "resultDisplay": "Constructed top-100 item-item similarity in 29.30 s, nnz=3000000",
            "description": "Benchmark fast block-wise top-K item similarity computation",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:48:09.616Z",
            "content": "<untrusted_context>\nOutput: Constructed top-100 item-item similarity in 29.30 s, nnz=3000000\nProcess Group PGID: 2197\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "0be5b3b7-dc9f-41a4-9835-95727c313f4e"
        }
      ]
    },
    {
      "id": 35,
      "role": "agent",
      "timestamp": "2026-09-29T03:48:30.590Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:48:30.590Z",
          "text": "Exploring Faster Approaches\n\nI'm currently investigating ways to accelerate our experimentation process, as the 29-second per run is proving to be a bottleneck. My focus is on evaluating the feasibility of techniques like user-user collaborative filtering, dual EASE, SVD, and TF-IDF/BM25 to achieve faster results, starting with an analysis of user-user CF given our $N_{\\text{users}} = 8456$."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:48:30.590Z",
          "text": "Calculating User-User Similarity\n\nMy initial exploration into user-user collaborative filtering is showing promise. The matrix multiplication to compute user-user similarity, given our user base size, completes in approximately 3 seconds. I am now focused on testing the speed of generating predictions using this dense similarity matrix."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:48:30.590Z",
          "callId": "run_shell_command__call_173033",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 8456).astype(np.float32)\nR = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nout = U @ R\nt1 = time.time()\nprint(f'Dense 8456x8456 @ 8456x30000 took {t1 - t0:.2f} s')\n\"",
            "description": "Benchmark dense BLAS multiplication of User similarity by R"
          },
          "recorded": {
            "id": "run_shell_command__call_173033",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 8456).astype(np.float32)\nR = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nout = U @ R\nt1 = time.time()\nprint(f'Dense 8456x8456 @ 8456x30000 took {t1 - t0:.2f} s')\n\"",
              "description": "Benchmark dense BLAS multiplication of User similarity by R"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_173033",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Dense 8456x8456 @ 8456x30000 took 19.73 s\nProcess Group PGID: 2221\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:48:56.786Z",
            "resultDisplay": "Dense 8456x8456 @ 8456x30000 took 19.73 s",
            "description": "Benchmark dense BLAS multiplication of User similarity by R",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:48:56.786Z",
            "content": "<untrusted_context>\nOutput: Dense 8456x8456 @ 8456x30000 took 19.73 s\nProcess Group PGID: 2221\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "8623ebe5-9a99-4a7c-bb87-1740b6d9c476"
        }
      ]
    },
    {
      "id": 36,
      "role": "agent",
      "timestamp": "2026-09-29T03:49:14.486Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:49:14.486Z",
          "text": "Exploring SVD Efficiency\n\nI've been exploring Singular Value Decomposition and found that truncated SVD, specifically with matrices U and V of dimensions (8456 x 256) and (256 x 30000) respectively, is remarkably fast. The fitting process took approximately 5 seconds, and the subsequent matrix multiplication of U and V was completed in just 0.4 seconds, for a total of around 6 seconds. My next step is to investigate randomized SVD, PCA, and NMF for potentially even greater speed."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:49:14.486Z",
          "text": "Improving Input Matrix\n\nMy current focus is on enhancing the input matrix $R$ by incorporating various weighting schemes. I'm particularly interested in how TF-IDF, BM25, time decay, and explicit signals like likes and dislikes will impact validation NDCG. I'll begin by experimenting with these feature weightings, comparing them to the previous `R_log` baseline."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:49:14.486Z",
          "text": "Investigating Decay Parameters\n\nI'm currently evaluating different half-lives for exponential decay, specifically considering 30, 60, and 90 days to see their impact on training. The maximum timestamp observed is 24,786,800, which will be a key reference point."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:49:14.486Z",
          "callId": "run_shell_command__call_120792",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nprint('Loading train data...')\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.uid.isin(targets) & dislikes.item_id.isin(item_to_idx)].copy()\nprint(f'Data loaded in {time.time()-t0:.2f}s')\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    t0 = time.time()\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (eval took {time.time()-t0:.2f}s)')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Experiment 1: Baseline log counts\npos = listens[listens.played_ratio_pct >= 50].copy()\nu = pos.uid.map(uid_to_idx).to_numpy()\nit = pos.item_id.map(item_to_idx).to_numpy()\nR_base = sp.csr_matrix((np.ones(len(u), dtype=np.float32), (u, it)), shape=(len(target_uids), len(item_ids)))\nR_base.data = np.log1p(R_base.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_base)\nV = svd.components_\nevaluate_predictions(U @ V, 'Baseline pos >= 50, k=256')\n\n# Experiment 2: Event weighting (likes, organic, completion)\n# Let's assign weights to events:\n# like: 5.0\n# organic listen >= 80%: 3.0\n# organic listen >= 50%: 2.0\n# non-organic listen >= 80%: 1.5\n# non-organic listen >= 50%: 1.0\n\n# Calculate listen weights\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\n\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nweights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_w = np.concatenate([weights, weights_lk])\n\nR_weighted = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_w_log = R_weighted.copy()\nR_w_log.data = np.log1p(R_w_log.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_w_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Weighted events (likes + organic + strong)')\n\"",
            "description": "Run event weighting experiment and evaluate NDCG@10"
          },
          "recorded": {
            "id": "run_shell_command__call_120792",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nprint('Loading train data...')\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.uid.isin(targets) & dislikes.item_id.isin(item_to_idx)].copy()\nprint(f'Data loaded in {time.time()-t0:.2f}s')\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    t0 = time.time()\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (eval took {time.time()-t0:.2f}s)')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Experiment 1: Baseline log counts\npos = listens[listens.played_ratio_pct >= 50].copy()\nu = pos.uid.map(uid_to_idx).to_numpy()\nit = pos.item_id.map(item_to_idx).to_numpy()\nR_base = sp.csr_matrix((np.ones(len(u), dtype=np.float32), (u, it)), shape=(len(target_uids), len(item_ids)))\nR_base.data = np.log1p(R_base.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_base)\nV = svd.components_\nevaluate_predictions(U @ V, 'Baseline pos >= 50, k=256')\n\n# Experiment 2: Event weighting (likes, organic, completion)\n# Let's assign weights to events:\n# like: 5.0\n# organic listen >= 80%: 3.0\n# organic listen >= 50%: 2.0\n# non-organic listen >= 80%: 1.5\n# non-organic listen >= 50%: 1.0\n\n# Calculate listen weights\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\n\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nweights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_w = np.concatenate([weights, weights_lk])\n\nR_weighted = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_w_log = R_weighted.copy()\nR_w_log.data = np.log1p(R_w_log.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_w_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Weighted events (likes + organic + strong)')\n\"",
              "description": "Run event weighting experiment and evaluate NDCG@10"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_120792",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Loading train data...\nData loaded in 8.38s\nBaseline pos >= 50, k=256 -> NDCG@10: 0.020296 (eval took 1.07s)\nWeighted events (likes + organic + strong) -> NDCG@10: 0.022618 (eval took 1.07s)\nProcess Group PGID: 2231\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:49:44.871Z",
            "resultDisplay": "Loading train data...\nData loaded in 8.38s\nBaseline pos >= 50, k=256 -> NDCG@10: 0.020296 (eval took 1.07s)\nWeighted events (likes + organic + strong) -> NDCG@10: 0.022618 (eval took 1.07s)",
            "description": "Run event weighting experiment and evaluate NDCG@10",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:49:44.872Z",
            "content": "<untrusted_context>\nOutput: Loading train data...\nData loaded in 8.38s\nBaseline pos >= 50, k=256 -> NDCG@10: 0.020296 (eval took 1.07s)\nWeighted events (likes + organic + strong) -> NDCG@10: 0.022618 (eval took 1.07s)\nProcess Group PGID: 2231\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "deffe865-4a5d-4664-a669-782ff56a00ec"
        }
      ]
    },
    {
      "id": 37,
      "role": "agent",
      "timestamp": "2026-09-29T03:49:53.854Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:49:53.854Z",
          "text": "Improving NDCG Performance\n\nI'm excited to report a significant NDCG improvement, a jump from 0.020296 to 0.022618, which is an 11.4% gain attributed to event weighting. My next step involves investigating the impact of incorporating time decay into these events. I'll be examining how this adjustment might influence user engagement, particularly considering the dynamic nature of preferences in contexts like music streaming."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:49:53.854Z",
          "callId": "run_shell_command__call_125210",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [15, 30, 45, 60, 90, 120, 180]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\"",
            "description": "Test time decay half-lives from 15 to 180 days"
          },
          "recorded": {
            "id": "run_shell_command__call_125210",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [15, 30, 45, 60, 90, 120, 180]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\"",
              "description": "Test time decay half-lives from 15 to 180 days"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_125210",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Half-life H=15 days -> NDCG@10: 0.035707\nHalf-life H=30 days -> NDCG@10: 0.033335\nHalf-life H=45 days -> NDCG@10: 0.031045\nHalf-life H=60 days -> NDCG@10: 0.029109\nHalf-life H=90 days -> NDCG@10: 0.026911\nHalf-life H=120 days -> NDCG@10: 0.026084\nHalf-life H=180 days -> NDCG@10: 0.024919\nProcess Group PGID: 2265\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:51:20.469Z",
            "resultDisplay": "Half-life H=15 days -> NDCG@10: 0.035707\nHalf-life H=30 days -> NDCG@10: 0.033335\nHalf-life H=45 days -> NDCG@10: 0.031045\nHalf-life H=60 days -> NDCG@10: 0.029109\nHalf-life H=90 days -> NDCG@10: 0.026911\nHalf-life H=120 days -> NDCG@10: 0.026084\nHalf-life H=180 days -> NDCG@10: 0.024919",
            "description": "Test time decay half-lives from 15 to 180 days",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:51:20.470Z",
            "content": "<untrusted_context>\nOutput: Half-life H=15 days -> NDCG@10: 0.035707\nHalf-life H=30 days -> NDCG@10: 0.033335\nHalf-life H=45 days -> NDCG@10: 0.031045\nHalf-life H=60 days -> NDCG@10: 0.029109\nHalf-life H=90 days -> NDCG@10: 0.026911\nHalf-life H=120 days -> NDCG@10: 0.026084\nHalf-life H=180 days -> NDCG@10: 0.024919\nProcess Group PGID: 2265\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "125bc4b4-e198-4b00-9b83-4a4c22b692a3"
        }
      ]
    },
    {
      "id": 38,
      "role": "agent",
      "timestamp": "2026-09-29T03:51:29.868Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:51:29.868Z",
          "callId": "run_shell_command__call_238012",
          "name": "run_shell_command",
          "input": {
            "description": "Test fine-grained half-lives from 3 to 18 days",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [3, 5, 7, 10, 12, 14, 18]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_238012",
            "name": "run_shell_command",
            "args": {
              "description": "Test fine-grained half-lives from 3 to 18 days",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [3, 5, 7, 10, 12, 14, 18]:\n    decay = np.power(0.5, age_days / H).astype(np.float32)\n    w = all_w_base * decay\n    R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'Half-life H={H} days')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_238012",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Half-life H=3 days -> NDCG@10: 0.033782\nHalf-life H=5 days -> NDCG@10: 0.036387\nHalf-life H=7 days -> NDCG@10: 0.037852\nHalf-life H=10 days -> NDCG@10: 0.037834\nHalf-life H=12 days -> NDCG@10: 0.036565\nHalf-life H=14 days -> NDCG@10: 0.036082\nHalf-life H=18 days -> NDCG@10: 0.035692\nProcess Group PGID: 2305\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:52:56.073Z",
            "resultDisplay": "Half-life H=3 days -> NDCG@10: 0.033782\nHalf-life H=5 days -> NDCG@10: 0.036387\nHalf-life H=7 days -> NDCG@10: 0.037852\nHalf-life H=10 days -> NDCG@10: 0.037834\nHalf-life H=12 days -> NDCG@10: 0.036565\nHalf-life H=14 days -> NDCG@10: 0.036082\nHalf-life H=18 days -> NDCG@10: 0.035692",
            "description": "Test fine-grained half-lives from 3 to 18 days",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:52:56.074Z",
            "content": "<untrusted_context>\nOutput: Half-life H=3 days -> NDCG@10: 0.033782\nHalf-life H=5 days -> NDCG@10: 0.036387\nHalf-life H=7 days -> NDCG@10: 0.037852\nHalf-life H=10 days -> NDCG@10: 0.037834\nHalf-life H=12 days -> NDCG@10: 0.036565\nHalf-life H=14 days -> NDCG@10: 0.036082\nHalf-life H=18 days -> NDCG@10: 0.035692\nProcess Group PGID: 2305\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "edd7c8e3-a38c-4a70-b15a-5c744bb3f92b"
        }
      ]
    },
    {
      "id": 39,
      "role": "agent",
      "timestamp": "2026-09-29T03:53:08.228Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:53:08.228Z",
          "text": "Optimizing Exposure Duration\n\nI've found that a 7 to 10-day window significantly boosts performance, exceeding the baseline by nearly 3.8 times. My current focus is exploring how to integrate this short-term insight with longer-term taste preferences to further refine our approach."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:53:08.228Z",
          "callId": "run_shell_command__call_333935",
          "name": "run_shell_command",
          "input": {
            "description": "Test blending short-term and medium-term models",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_333935",
            "name": "run_shell_command",
            "args": {
              "description": "Test blending short-term and medium-term models",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_333935",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: /usr/bin/bash: line 98:  2341 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\"\nExit Code: 137\nProcess Group PGID: 2339\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:53:43.232Z",
            "resultDisplay": "/usr/bin/bash: line 98:  2341 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\"",
            "description": "Test blending short-term and medium-term models",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:53:43.236Z",
            "content": "<untrusted_context>\nOutput: /usr/bin/bash: line 98:  2341 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n    # Normalize standard deviations or scale\n    std7 = np.std(score7)\n    std45 = np.std(score45)\n    blend = (score7 / std7) + alpha * (score45 / std45)\n    evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\"\nExit Code: 137\nProcess Group PGID: 2339\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "c95bb3f8-2bed-414a-a766-aac395053a93"
        }
      ]
    },
    {
      "id": 40,
      "role": "agent",
      "timestamp": "2026-09-29T03:53:52.246Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:53:52.246Z",
          "text": "Investigating Memory Bottleneck\n\nI'm currently dissecting the root cause of the \"Process was killed\" exit code, specifically noting the substantial memory footprint from large sparse and dense matrices, coupled with the `svd.fit_transform` operation, which I suspect triggered the OOM killer."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:53:52.246Z",
          "callId": "run_shell_command__call_203520",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport pandas as pd\nimport gc\n\nt_cut = 24786800\nt_90d = t_cut - 90 * 86400\nt_120d = t_cut - 120 * 86400\n\n# Read with pyarrow filter\ndf_90 = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nprint(f'Listens in last 90 days: {len(df_90)} rows (vs 43.4M total)!')\n\"",
            "description": "Check row count of train_listens filtered to last 90 days"
          },
          "recorded": {
            "id": "run_shell_command__call_203520",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport pandas as pd\nimport gc\n\nt_cut = 24786800\nt_90d = t_cut - 90 * 86400\nt_120d = t_cut - 120 * 86400\n\n# Read with pyarrow filter\ndf_90 = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nprint(f'Listens in last 90 days: {len(df_90)} rows (vs 43.4M total)!')\n\"",
              "description": "Check row count of train_listens filtered to last 90 days"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_203520",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Listens in last 90 days: 16769274 rows (vs 43.4M total)!\nProcess Group PGID: 2373\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:53:53.511Z",
            "resultDisplay": "Listens in last 90 days: 16769274 rows (vs 43.4M total)!",
            "description": "Check row count of train_listens filtered to last 90 days",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:53:53.512Z",
            "content": "<untrusted_context>\nOutput: Listens in last 90 days: 16769274 rows (vs 43.4M total)!\nProcess Group PGID: 2373\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "839874ba-74a4-4df8-a227-fec43718ac35"
        }
      ]
    },
    {
      "id": 41,
      "role": "agent",
      "timestamp": "2026-09-29T03:53:59.271Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:53:59.271Z",
          "text": "Refining Target Users\n\nI'm focusing on defining our target user group. The initial filtering based on a 90-day window and positive listens has loaded a substantial dataset, efficiently discarding stale records. My current thought is to solidify this 90-day criteria for defining users with at least 20 positive listens prior to the training cutoff."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:53:59.271Z",
          "callId": "run_shell_command__call_240268",
          "name": "run_shell_command",
          "input": {
            "description": "Check validation preference state overlap with catalog",
            "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\ncat_set = set(catalog.item_id)\ntarget_set = set(targets.uid)\n\npref_cat = pref[pref.uid.isin(target_set) & pref.item_id.isin(cat_set)]\nprint('Preference state in catalog:', len(pref_cat))\nprint(pref_cat.state.value_counts())\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_240268",
            "name": "run_shell_command",
            "args": {
              "description": "Check validation preference state overlap with catalog",
              "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\ncat_set = set(catalog.item_id)\ntarget_set = set(targets.uid)\n\npref_cat = pref[pref.uid.isin(target_set) & pref.item_id.isin(cat_set)]\nprint('Preference state in catalog:', len(pref_cat))\nprint(pref_cat.state.value_counts())\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_240268",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Preference state in catalog: 469124\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nProcess Group PGID: 2394\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:54:00.146Z",
            "resultDisplay": "Preference state in catalog: 469124\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64",
            "description": "Check validation preference state overlap with catalog",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:54:00.147Z",
            "content": "<untrusted_context>\nOutput: Preference state in catalog: 469124\nstate\nliked       418435\ndisliked     50689\nName: count, dtype: int64\nProcess Group PGID: 2394\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "0b239493-accd-4db2-894c-8783c58eff03"
        }
      ]
    },
    {
      "id": 42,
      "role": "agent",
      "timestamp": "2026-09-29T03:54:17.427Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:54:17.427Z",
          "text": "Cataloging preferences complete\n\nI’ve successfully cataloged all 469,124 preference state entries. They accurately reflect the precise net active likes and dislikes at the cutoff, presenting a clean, explicit signal."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:54:17.427Z",
          "text": "Refining the approach\n\nMy current focus is on validating the impact of incorporating `validation_preference_state` and refining our weighting strategies. I'm exploring how to best integrate organic status, completion, and time decay into listen counts, while simultaneously assessing the efficacy of different matrix factorization techniques like SVD against ALS, and considering pre-processing with TF-IDF/BM25."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:54:17.427Z",
          "callId": "run_shell_command__call_128108",
          "name": "run_shell_command",
          "input": {
            "description": "Evaluate effect of Item IDF weighting on SVD",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\n# Load filtered listens\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet(\n    '/task/data/train_likes.parquet',\n    filters=[('timestamp', '>=', t_90d)]\n)\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nprint(f'Filtered loaded: listens={len(listens)}, likes={len(likes)}, pref={len(pref)}')\n\n# Compute weights\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\n# Likes\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\n# Active preference state (give base weight 3.0 for liked, -2.0 for disliked)\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Without IDF\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Standard log1p, k=256')\n\n# 2. With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr) # count of nonzeros per item\nidf = np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0\nR_idf = R_log.multiply(sp.diags(idf))\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# 3. With IDF power 0.5 (smoother)\nidf_half = np.power(idf, 0.5)\nR_idf_half = R_log.multiply(sp.diags(idf_half))\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_128108",
            "name": "run_shell_command",
            "args": {
              "description": "Evaluate effect of Item IDF weighting on SVD",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\n# Load filtered listens\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet(\n    '/task/data/train_likes.parquet',\n    filters=[('timestamp', '>=', t_90d)]\n)\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nprint(f'Filtered loaded: listens={len(listens)}, likes={len(likes)}, pref={len(pref)}')\n\n# Compute weights\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\n# Likes\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\n# Active preference state (give base weight 3.0 for liked, -2.0 for disliked)\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Without IDF\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Standard log1p, k=256')\n\n# 2. With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr) # count of nonzeros per item\nidf = np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0\nR_idf = R_log.multiply(sp.diags(idf))\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# 3. With IDF power 0.5 (smoother)\nidf_half = np.power(idf, 0.5)\nR_idf_half = R_log.multiply(sp.diags(idf_half))\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_128108",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 109, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 440, in multiply\n    raise ValueError(\"inconsistent shapes\")\nValueError: inconsistent shapes\nFiltered loaded: listens=12099707, likes=193175, pref=469124\nStandard log1p, k=256 -> NDCG@10: 0.037856\nExit Code: 1\nProcess Group PGID: 2411\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:54:30.972Z",
            "resultDisplay": "Traceback (most recent call last):\n  File \"<string>\", line 109, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 440, in multiply\n    raise ValueError(\"inconsistent shapes\")\nValueError: inconsistent shapes\nFiltered loaded: listens=12099707, likes=193175, pref=469124\nStandard log1p, k=256 -> NDCG@10: 0.037856",
            "description": "Evaluate effect of Item IDF weighting on SVD",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:54:30.973Z",
            "content": "<untrusted_context>\nOutput: Traceback (most recent call last):\n  File \"<string>\", line 109, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 440, in multiply\n    raise ValueError(\"inconsistent shapes\")\nValueError: inconsistent shapes\nFiltered loaded: listens=12099707, likes=193175, pref=469124\nStandard log1p, k=256 -> NDCG@10: 0.037856\nExit Code: 1\nProcess Group PGID: 2411\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "8cd091c7-61f9-4a3b-903d-ac400f9b61a3"
        }
      ]
    },
    {
      "id": 43,
      "role": "agent",
      "timestamp": "2026-09-29T03:54:43.560Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:54:43.560Z",
          "text": "Clarifying Matrix Multiplication\n\nI've realized my previous approach for multiplying columns by `idf` was incorrect. The `sp.diags(idf)` function generates a large sparse matrix, which is incompatible with elementwise multiplication in `R_log.multiply`. I now understand the correct method is to use matrix multiplication: `R_log @ sp.diags(idf)`."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:54:43.560Z",
          "callId": "run_shell_command__call_137672",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_idf = R_log @ sp.diags(idf)\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# With IDF^0.5\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf_half = R_log @ sp.diags(idf_half)\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\"",
            "description": "Run corrected IDF test"
          },
          "recorded": {
            "id": "run_shell_command__call_137672",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_idf = R_log @ sp.diags(idf)\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# With IDF^0.5\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf_half = R_log @ sp.diags(idf_half)\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\"",
              "description": "Run corrected IDF test"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_137672",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Log1p + Item IDF, k=256 -> NDCG@10: 0.038861\nLog1p + Item IDF^0.5, k=256 -> NDCG@10: 0.040174\nProcess Group PGID: 2445\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:55:03.843Z",
            "resultDisplay": "Log1p + Item IDF, k=256 -> NDCG@10: 0.038861\nLog1p + Item IDF^0.5, k=256 -> NDCG@10: 0.040174",
            "description": "Run corrected IDF test",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:55:03.844Z",
            "content": "<untrusted_context>\nOutput: Log1p + Item IDF, k=256 -> NDCG@10: 0.038861\nLog1p + Item IDF^0.5, k=256 -> NDCG@10: 0.040174\nProcess Group PGID: 2445\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "c5663916-94b7-4a50-aa84-57a86bb11838"
        }
      ]
    },
    {
      "id": 44,
      "role": "agent",
      "timestamp": "2026-09-29T03:55:18.488Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:55:18.488Z",
          "text": "Improving NDCG@10\n\nI'm pleased to report our NDCG@10 has reached 0.040174. This reflects a consistent upward trend from our initial baseline, with our latest IDF-scaled time-decayed SVD model showing significant improvement. I'm now focused on further enhancements."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:55:18.488Z",
          "text": "Refining Latent Factor Scaling\n\nI'm exploring strategies to better balance user and item influences within the latent space. Specifically, I'm investigating the impact of normalizing user vectors before SVD and experimenting with different singular value scaling exponents ($\\alpha$) beyond the standard SVD, aiming to reduce the dominance of top eigenvalues. The goal is to improve the model's ability to capture nuanced user preferences."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:55:18.488Z",
          "callId": "run_shell_command__call_392515",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nsvd = TruncatedSVD(n_components=300, algorithm='randomized', random_state=42)\nU_sigma = svd.fit_transform(R_idf) # U * Sigma\nsigma = svd.singular_values_ # Sigma\nV = svd.components_ # V^T\nU_ortho = U_sigma / sigma # orthonormal U\n\nfor alpha in [1.0, 0.8, 0.6, 0.5, 0.4, 0.2, 0.0]:\n    scores = (U_ortho * (sigma ** alpha)) @ V\n    evaluate_predictions(scores, f'Sigma power alpha={alpha}')\n\"",
            "description": "Test singular value exponent alpha on validation NDCG"
          },
          "recorded": {
            "id": "run_shell_command__call_392515",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nsvd = TruncatedSVD(n_components=300, algorithm='randomized', random_state=42)\nU_sigma = svd.fit_transform(R_idf) # U * Sigma\nsigma = svd.singular_values_ # Sigma\nV = svd.components_ # V^T\nU_ortho = U_sigma / sigma # orthonormal U\n\nfor alpha in [1.0, 0.8, 0.6, 0.5, 0.4, 0.2, 0.0]:\n    scores = (U_ortho * (sigma ** alpha)) @ V\n    evaluate_predictions(scores, f'Sigma power alpha={alpha}')\n\"",
              "description": "Test singular value exponent alpha on validation NDCG"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_392515",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Sigma power alpha=1.0 -> NDCG@10: 0.039454\nSigma power alpha=0.8 -> NDCG@10: 0.037045\nSigma power alpha=0.6 -> NDCG@10: 0.035139\nSigma power alpha=0.5 -> NDCG@10: 0.034337\nSigma power alpha=0.4 -> NDCG@10: 0.033474\nSigma power alpha=0.2 -> NDCG@10: 0.031411\nSigma power alpha=0.0 -> NDCG@10: 0.029817\nProcess Group PGID: 2479\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:55:44.462Z",
            "resultDisplay": "Sigma power alpha=1.0 -> NDCG@10: 0.039454\nSigma power alpha=0.8 -> NDCG@10: 0.037045\nSigma power alpha=0.6 -> NDCG@10: 0.035139\nSigma power alpha=0.5 -> NDCG@10: 0.034337\nSigma power alpha=0.4 -> NDCG@10: 0.033474\nSigma power alpha=0.2 -> NDCG@10: 0.031411\nSigma power alpha=0.0 -> NDCG@10: 0.029817",
            "description": "Test singular value exponent alpha on validation NDCG",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:55:44.463Z",
            "content": "<untrusted_context>\nOutput: Sigma power alpha=1.0 -> NDCG@10: 0.039454\nSigma power alpha=0.8 -> NDCG@10: 0.037045\nSigma power alpha=0.6 -> NDCG@10: 0.035139\nSigma power alpha=0.5 -> NDCG@10: 0.034337\nSigma power alpha=0.4 -> NDCG@10: 0.033474\nSigma power alpha=0.2 -> NDCG@10: 0.031411\nSigma power alpha=0.0 -> NDCG@10: 0.029817\nProcess Group PGID: 2479\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "6d5c1ba9-ed6d-45e2-8f11-1273a18e444f"
        }
      ]
    },
    {
      "id": 45,
      "role": "agent",
      "timestamp": "2026-09-29T03:55:56.395Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:55:56.395Z",
          "text": "Exploring Dimension Effects\n\nMy current focus is on the impact of the dimension parameter, $k$. I've observed an improvement in performance by increasing it from 256 to 300, with the score dropping from 0.040174 to 0.039454. I'm now considering further increases to ascertain the optimal value."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:55:56.395Z",
          "callId": "run_shell_command__call_240892",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [128, 160, 192, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_idf)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_idf, k={k}')\n\n# Test L2 row normalization\nR_norm = normalize(R_idf, norm='l2', axis=1)\nfor k in [160, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_norm)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_norm (L2), k={k}')\n\"",
            "description": "Test dimensions k and row normalization"
          },
          "recorded": {
            "id": "run_shell_command__call_240892",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [128, 160, 192, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_idf)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_idf, k={k}')\n\n# Test L2 row normalization\nR_norm = normalize(R_idf, norm='l2', axis=1)\nfor k in [160, 224, 256]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_norm)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'R_norm (L2), k={k}')\n\"",
              "description": "Test dimensions k and row normalization"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_240892",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: R_idf, k=128 -> NDCG@10: 0.040999\nR_idf, k=160 -> NDCG@10: 0.041634\nR_idf, k=192 -> NDCG@10: 0.039876\nR_idf, k=224 -> NDCG@10: 0.040027\nR_idf, k=256 -> NDCG@10: 0.040174\nR_norm (L2), k=160 -> NDCG@10: 0.037777\nR_norm (L2), k=224 -> NDCG@10: 0.036706\nR_norm (L2), k=256 -> NDCG@10: 0.036614\nProcess Group PGID: 2513\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:56:47.656Z",
            "resultDisplay": "R_idf, k=128 -> NDCG@10: 0.040999\nR_idf, k=160 -> NDCG@10: 0.041634\nR_idf, k=192 -> NDCG@10: 0.039876\nR_idf, k=224 -> NDCG@10: 0.040027\nR_idf, k=256 -> NDCG@10: 0.040174\nR_norm (L2), k=160 -> NDCG@10: 0.037777\nR_norm (L2), k=224 -> NDCG@10: 0.036706\nR_norm (L2), k=256 -> NDCG@10: 0.036614",
            "description": "Test dimensions k and row normalization",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:56:47.657Z",
            "content": "<untrusted_context>\nOutput: R_idf, k=128 -> NDCG@10: 0.040999\nR_idf, k=160 -> NDCG@10: 0.041634\nR_idf, k=192 -> NDCG@10: 0.039876\nR_idf, k=224 -> NDCG@10: 0.040027\nR_idf, k=256 -> NDCG@10: 0.040174\nR_norm (L2), k=160 -> NDCG@10: 0.037777\nR_norm (L2), k=224 -> NDCG@10: 0.036706\nR_norm (L2), k=256 -> NDCG@10: 0.036614\nProcess Group PGID: 2513\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "8567532a-bfa3-4a23-917e-7a510abd6deb"
        }
      ]
    },
    {
      "id": 46,
      "role": "agent",
      "timestamp": "2026-09-29T03:56:58.590Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:56:58.590Z",
          "text": "Evaluating R_idf Parameters\n\nI'm excited by the R_idf results, hitting NDCG@10 at 0.041634 with k=160. It's interesting that L2 normalization actually decreased performance, suggesting that unnormalized user magnitude offers a valuable insight into engagement levels. I'm now considering the next steps to refine this."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:56:58.590Z",
          "callId": "run_shell_command__call_199581",
          "name": "run_shell_command",
          "input": {
            "description": "Grid search on half-life H and IDF power p",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nfor H in [5.0, 7.0, 8.0, 9.0, 10.0]:\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref])\n    all_it = np.concatenate([it_l, it_lk, it_pref])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, weights_pref])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    \n    for p in [0.3, 0.4, 0.5, 0.6]:\n        R_p = R @ sp.diags(np.power(idf, p).astype(np.float32))\n        svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        evaluate_predictions(U @ V, f'H={H}, p={p}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_199581",
            "name": "run_shell_command",
            "args": {
              "description": "Grid search on half-life H and IDF power p",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nfor H in [5.0, 7.0, 8.0, 9.0, 10.0]:\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref])\n    all_it = np.concatenate([it_l, it_lk, it_pref])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, weights_pref])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    \n    for p in [0.3, 0.4, 0.5, 0.6]:\n        R_p = R @ sp.diags(np.power(idf, p).astype(np.float32))\n        svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        evaluate_predictions(U @ V, f'H={H}, p={p}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_199581",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: H=5.0, p=0.3 -> NDCG@10: 0.04074\nH=5.0, p=0.4 -> NDCG@10: 0.040963\nH=5.0, p=0.5 -> NDCG@10: 0.041177\nH=5.0, p=0.6 -> NDCG@10: 0.041378\nH=7.0, p=0.3 -> NDCG@10: 0.040681\nH=7.0, p=0.4 -> NDCG@10: 0.040945\nH=7.0, p=0.5 -> NDCG@10: 0.041128\nH=7.0, p=0.6 -> NDCG@10: 0.041682\nH=8.0, p=0.3 -> NDCG@10: 0.040347\nH=8.0, p=0.4 -> NDCG@10: 0.040697\nH=8.0, p=0.5 -> NDCG@10: 0.041634\nH=8.0, p=0.6 -> NDCG@10: 0.04126\nH=9.0, p=0.3 -> NDCG@10: 0.039766\nH=9.0, p=0.4 -> NDCG@10: 0.040537\nH=9.0, p=0.5 -> NDCG@10: 0.04086\nH=9.0, p=0.6 -> NDCG@10: 0.040916\nH=10.0, p=0.3 -> NDCG@10: 0.03948\nH=10.0, p=0.4 -> NDCG@10: 0.040259\nH=10.0, p=0.5 -> NDCG@10: 0.039998\nH=10.0, p=0.6 -> NDCG@10: 0.040176\nProcess Group PGID: 2547\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:58:46.264Z",
            "resultDisplay": "H=5.0, p=0.3 -> NDCG@10: 0.04074\nH=5.0, p=0.4 -> NDCG@10: 0.040963\nH=5.0, p=0.5 -> NDCG@10: 0.041177\nH=5.0, p=0.6 -> NDCG@10: 0.041378\nH=7.0, p=0.3 -> NDCG@10: 0.040681\nH=7.0, p=0.4 -> NDCG@10: 0.040945\nH=7.0, p=0.5 -> NDCG@10: 0.041128\nH=7.0, p=0.6 -> NDCG@10: 0.041682\nH=8.0, p=0.3 -> NDCG@10: 0.040347\nH=8.0, p=0.4 -> NDCG@10: 0.040697\nH=8.0, p=0.5 -> NDCG@10: 0.041634\nH=8.0, p=0.6 -> NDCG@10: 0.04126\nH=9.0, p=0.3 -> NDCG@10: 0.039766\nH=9.0, p=0.4 -> NDCG@10: 0.040537\nH=9.0, p=0.5 -> NDCG@10: 0.04086\nH=9.0, p=0.6 -> NDCG@10: 0.040916\nH=10.0, p=0.3 -> NDCG@10: 0.03948\nH=10.0, p=0.4 -> NDCG@10: 0.040259\nH=10.0, p=0.5 -> NDCG@10: 0.039998\nH=10.0, p=0.6 -> NDCG@10: 0.040176",
            "description": "Grid search on half-life H and IDF power p",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:58:48.093Z",
            "content": "<untrusted_context>\nOutput: H=5.0, p=0.3 -> NDCG@10: 0.04074\nH=5.0, p=0.4 -> NDCG@10: 0.040963\nH=5.0, p=0.5 -> NDCG@10: 0.041177\nH=5.0, p=0.6 -> NDCG@10: 0.041378\nH=7.0, p=0.3 -> NDCG@10: 0.040681\nH=7.0, p=0.4 -> NDCG@10: 0.040945\nH=7.0, p=0.5 -> NDCG@10: 0.041128\nH=7.0, p=0.6 -> NDCG@10: 0.041682\nH=8.0, p=0.3 -> NDCG@10: 0.040347\nH=8.0, p=0.4 -> NDCG@10: 0.040697\nH=8.0, p=0.5 -> NDCG@10: 0.041634\nH=8.0, p=0.6 -> NDCG@10: 0.04126\nH=9.0, p=0.3 -> NDCG@10: 0.039766\nH=9.0, p=0.4 -> NDCG@10: 0.040537\nH=9.0, p=0.5 -> NDCG@10: 0.04086\nH=9.0, p=0.6 -> NDCG@10: 0.040916\nH=10.0, p=0.3 -> NDCG@10: 0.03948\nH=10.0, p=0.4 -> NDCG@10: 0.040259\nH=10.0, p=0.5 -> NDCG@10: 0.039998\nH=10.0, p=0.6 -> NDCG@10: 0.040176\nProcess Group PGID: 2547\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "e9090664-57bb-4cd7-9be6-36203eb32158"
        }
      ]
    },
    {
      "id": 47,
      "role": "agent",
      "timestamp": "2026-09-29T03:59:12.174Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:59:12.174Z",
          "text": "Exploring Dislikes' Impact\n\nI'm now investigating the effect of incorporating dislikes as negative signals. Specifically, I'm examining the `train_dislikes.parquet` dataset and `validation_preference_state` where the state is 'disliked' to see how this penalization influences performance."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:59:12.174Z",
          "callId": "run_shell_command__call_211086",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\npref_disliked = pref[pref.state == 'disliked']\nu_pref_d = pref_disliked.uid.map(uid_to_idx).to_numpy()\nit_pref_d = pref_disliked.item_id.map(item_to_idx).to_numpy()\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor neg_w in [0.0, -1.0, -3.0, -5.0]:\n    if neg_w == 0.0:\n        cur_u, cur_it, cur_w = all_u, all_it, all_w\n    else:\n        w_pref_d = np.full(len(pref_disliked), neg_w, dtype=np.float32)\n        cur_u = np.concatenate([all_u, u_pref_d])\n        cur_it = np.concatenate([all_it, it_pref_d])\n        cur_w = np.concatenate([all_w, w_pref_d])\n    \n    R = sp.csr_matrix((cur_w, (cur_u, cur_it)), shape=(len(target_uids), len(item_ids)))\n    # Sign-preserving log1p: sign(x) * log1p(abs(x))\n    R.data = np.sign(R.data) * np.log1p(np.abs(R.data))\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'neg_weight={neg_w}')\n\"",
            "description": "Test negative preference weights for dislikes"
          },
          "recorded": {
            "id": "run_shell_command__call_211086",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\npref_disliked = pref[pref.state == 'disliked']\nu_pref_d = pref_disliked.uid.map(uid_to_idx).to_numpy()\nit_pref_d = pref_disliked.item_id.map(item_to_idx).to_numpy()\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor neg_w in [0.0, -1.0, -3.0, -5.0]:\n    if neg_w == 0.0:\n        cur_u, cur_it, cur_w = all_u, all_it, all_w\n    else:\n        w_pref_d = np.full(len(pref_disliked), neg_w, dtype=np.float32)\n        cur_u = np.concatenate([all_u, u_pref_d])\n        cur_it = np.concatenate([all_it, it_pref_d])\n        cur_w = np.concatenate([all_w, w_pref_d])\n    \n    R = sp.csr_matrix((cur_w, (cur_u, cur_it)), shape=(len(target_uids), len(item_ids)))\n    # Sign-preserving log1p: sign(x) * log1p(abs(x))\n    R.data = np.sign(R.data) * np.log1p(np.abs(R.data))\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'neg_weight={neg_w}')\n\"",
              "description": "Test negative preference weights for dislikes"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_211086",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: neg_weight=0.0 -> NDCG@10: 0.041682\nneg_weight=-1.0 -> NDCG@10: 0.041229\nneg_weight=-3.0 -> NDCG@10: 0.040782\nneg_weight=-5.0 -> NDCG@10: 0.041575\nProcess Group PGID: 2587\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T03:59:40.574Z",
            "resultDisplay": "neg_weight=0.0 -> NDCG@10: 0.041682\nneg_weight=-1.0 -> NDCG@10: 0.041229\nneg_weight=-3.0 -> NDCG@10: 0.040782\nneg_weight=-5.0 -> NDCG@10: 0.041575",
            "description": "Test negative preference weights for dislikes",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T03:59:40.575Z",
            "content": "<untrusted_context>\nOutput: neg_weight=0.0 -> NDCG@10: 0.041682\nneg_weight=-1.0 -> NDCG@10: 0.041229\nneg_weight=-3.0 -> NDCG@10: 0.040782\nneg_weight=-5.0 -> NDCG@10: 0.041575\nProcess Group PGID: 2587\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "86b76e37-a836-488a-a1f4-51d9c69e5bf3"
        }
      ]
    },
    {
      "id": 48,
      "role": "agent",
      "timestamp": "2026-09-29T03:59:53.247Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T03:59:53.247Z",
          "text": "Refining Preference Matrix\n\nI've adjusted the `neg_weight` to 0.0, which effectively excludes disliked items from the positive preference matrix. This approach is cleaner and has shown a slight improvement in results, as explicit dislikes are already filtered out of candidate eligibility."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T03:59:53.247Z",
          "callId": "run_shell_command__call_252902",
          "name": "run_shell_command",
          "input": {
            "description": "Evaluate popularity prior blended with CF scores",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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)\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Prepare catalog popularity vector aligned with item_ids\ncat_map = {row.item_id: row for row in catalog.itertuples()}\norg_pop = np.array([cat_map[it].organic_positive_listens for it in item_ids], dtype=np.float32)\ntot_pop = np.array([cat_map[it].positive_listens for it in item_ids], dtype=np.float32)\nlog_org = np.log1p(org_pop)\nlog_tot = np.log1p(tot_pop)\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\nsvd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_p)\nV = svd.components_\ncf_scores = U @ V # (8456, 30000)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nevaluate_predictions(cf_scores, 'Pure CF scores')\n\n# Standardize CF scores\ncf_std = np.std(cf_scores)\npop_std = np.std(log_org)\n\nfor beta in [0.01, 0.02, 0.05, 0.1, 0.2]:\n    comb = (cf_scores / cf_std) + beta * (log_org / pop_std)\n    evaluate_predictions(comb, f'CF + {beta} * organic_pop')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_252902",
            "name": "run_shell_command",
            "args": {
              "description": "Evaluate popularity prior blended with CF scores",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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)\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Prepare catalog popularity vector aligned with item_ids\ncat_map = {row.item_id: row for row in catalog.itertuples()}\norg_pop = np.array([cat_map[it].organic_positive_listens for it in item_ids], dtype=np.float32)\ntot_pop = np.array([cat_map[it].positive_listens for it in item_ids], dtype=np.float32)\nlog_org = np.log1p(org_pop)\nlog_tot = np.log1p(tot_pop)\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\nsvd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_p)\nV = svd.components_\ncf_scores = U @ V # (8456, 30000)\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nevaluate_predictions(cf_scores, 'Pure CF scores')\n\n# Standardize CF scores\ncf_std = np.std(cf_scores)\npop_std = np.std(log_org)\n\nfor beta in [0.01, 0.02, 0.05, 0.1, 0.2]:\n    comb = (cf_scores / cf_std) + beta * (log_org / pop_std)\n    evaluate_predictions(comb, f'CF + {beta} * organic_pop')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_252902",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Pure CF scores -> NDCG@10: 0.041682\nCF + 0.01 * organic_pop -> NDCG@10: 0.041632\nCF + 0.02 * organic_pop -> NDCG@10: 0.041607\nCF + 0.05 * organic_pop -> NDCG@10: 0.041483\nCF + 0.1 * organic_pop -> NDCG@10: 0.04117\nCF + 0.2 * organic_pop -> NDCG@10: 0.041172\nProcess Group PGID: 2621\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:00:11.429Z",
            "resultDisplay": "Pure CF scores -> NDCG@10: 0.041682\nCF + 0.01 * organic_pop -> NDCG@10: 0.041632\nCF + 0.02 * organic_pop -> NDCG@10: 0.041607\nCF + 0.05 * organic_pop -> NDCG@10: 0.041483\nCF + 0.1 * organic_pop -> NDCG@10: 0.04117\nCF + 0.2 * organic_pop -> NDCG@10: 0.041172",
            "description": "Evaluate popularity prior blended with CF scores",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:00:11.430Z",
            "content": "<untrusted_context>\nOutput: Pure CF scores -> NDCG@10: 0.041682\nCF + 0.01 * organic_pop -> NDCG@10: 0.041632\nCF + 0.02 * organic_pop -> NDCG@10: 0.041607\nCF + 0.05 * organic_pop -> NDCG@10: 0.041483\nCF + 0.1 * organic_pop -> NDCG@10: 0.04117\nCF + 0.2 * organic_pop -> NDCG@10: 0.041172\nProcess Group PGID: 2621\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "689d1bac-8187-40a8-8c10-667ae7544fbb"
        }
      ]
    },
    {
      "id": 49,
      "role": "agent",
      "timestamp": "2026-09-29T04:00:31.748Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:00:31.748Z",
          "text": "Refining Recommendations\n\nMy focus has shifted to exploring ensembling and multi-scale representations. I've confirmed that pure CF effectively captures personalization and discovery, but I'm seeing how pure popularity can negatively impact discovery metrics."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:00:31.748Z",
          "callId": "run_shell_command__call_212339",
          "name": "run_shell_command",
          "input": {
            "description": "Test multi-timescale model blending",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_212339",
            "name": "run_shell_command",
            "args": {
              "description": "Test multi-timescale model blending",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_212339",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: <string>:102: RuntimeWarning: invalid value encountered in divide\n<string>:103: RuntimeWarning: invalid value encountered in divide\n/usr/bin/bash: line 114:  2657 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\"\nExit Code: 137\nProcess Group PGID: 2655\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:00:56.378Z",
            "resultDisplay": "<string>:102: RuntimeWarning: invalid value encountered in divide\n<string>:103: RuntimeWarning: invalid value encountered in divide\n/usr/bin/bash: line 114:  2657 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\"",
            "description": "Test multi-timescale model blending",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:00:56.395Z",
            "content": "<untrusted_context>\nOutput: <string>:102: RuntimeWarning: invalid value encountered in divide\n<string>:103: RuntimeWarning: invalid value encountered in divide\n/usr/bin/bash: line 114:  2657 Killed                  python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n    decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n    decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    return U @ V\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n    for w_l in [0.0, 0.1, 0.2, 0.3]:\n        blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n        evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\"\nExit Code: 137\nProcess Group PGID: 2655\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "2850ff91-42ca-423a-ac80-d87514ce3ad7"
        }
      ]
    },
    {
      "id": 50,
      "role": "agent",
      "timestamp": "2026-09-29T04:01:11.788Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:01:11.788Z",
          "text": "Exploring Memory Bottlenecks\n\nI'm currently investigating memory usage patterns with large arrays, specifically observing issues with mean calculations on substantial datasets. My focus is on optimizing these operations to prevent exhaustion, especially when dealing with arrays that have a standard deviation of zero for certain users, which is triggering runtime warnings."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:01:11.788Z",
          "callId": "run_shell_command__call_322790",
          "name": "run_shell_command",
          "input": {
            "description": "Test multi-timescale decay kernel with gamma values",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test multi-timescale decay kernel: 2^(-age/7) + gamma * 2^(-age/30)\nfor gamma in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n    kernel_l = (np.power(0.5, age_days_l / 7.0) + gamma * np.power(0.5, age_days_l / 30.0)).astype(np.float32)\n    kernel_lk = (np.power(0.5, age_days_lk / 7.0) + gamma * np.power(0.5, age_days_lk / 30.0)).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * kernel_l, 5.0 * kernel_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores = U @ V\n    evaluate_predictions(scores, f'gamma={gamma}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_322790",
            "name": "run_shell_command",
            "args": {
              "description": "Test multi-timescale decay kernel with gamma values",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test multi-timescale decay kernel: 2^(-age/7) + gamma * 2^(-age/30)\nfor gamma in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n    kernel_l = (np.power(0.5, age_days_l / 7.0) + gamma * np.power(0.5, age_days_l / 30.0)).astype(np.float32)\n    kernel_lk = (np.power(0.5, age_days_lk / 7.0) + gamma * np.power(0.5, age_days_lk / 30.0)).astype(np.float32)\n    \n    all_u = np.concatenate([u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([weights_l_base * kernel_l, 5.0 * kernel_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores = U @ V\n    evaluate_predictions(scores, f'gamma={gamma}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_322790",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: gamma=0.0 -> NDCG@10: 0.041682\ngamma=0.05 -> NDCG@10: 0.041469\ngamma=0.1 -> NDCG@10: 0.040599\ngamma=0.15 -> NDCG@10: 0.040384\ngamma=0.2 -> NDCG@10: 0.039918\ngamma=0.3 -> NDCG@10: 0.039588\nProcess Group PGID: 2683\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:01:53.368Z",
            "resultDisplay": "gamma=0.0 -> NDCG@10: 0.041682\ngamma=0.05 -> NDCG@10: 0.041469\ngamma=0.1 -> NDCG@10: 0.040599\ngamma=0.15 -> NDCG@10: 0.040384\ngamma=0.2 -> NDCG@10: 0.039918\ngamma=0.3 -> NDCG@10: 0.039588",
            "description": "Test multi-timescale decay kernel with gamma values",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:01:53.369Z",
            "content": "<untrusted_context>\nOutput: gamma=0.0 -> NDCG@10: 0.041682\ngamma=0.05 -> NDCG@10: 0.041469\ngamma=0.1 -> NDCG@10: 0.040599\ngamma=0.15 -> NDCG@10: 0.040384\ngamma=0.2 -> NDCG@10: 0.039918\ngamma=0.3 -> NDCG@10: 0.039588\nProcess Group PGID: 2683\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "103360e2-bea9-4e02-ba84-10cf6b0624d8"
        }
      ]
    },
    {
      "id": 51,
      "role": "agent",
      "timestamp": "2026-09-29T04:02:14.980Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:02:14.980Z",
          "text": "Confirming Gamma Value\n\nI've confirmed that setting Gamma to 0.0, which prioritizes recent decay patterns, yields the optimal performance. This suggests users are most effectively exploring new music based on their immediate listening phase or mood."
        },
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:02:14.980Z",
          "text": "Adjusting Skip Weighting\n\nI'm re-evaluating our event weights, specifically how short, incomplete listens (skips) are handled. It appears tracks played for less than 30% are currently assigned a weight of 1.0, which seems too high given their nature as skips. I'm investigating this further."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:02:14.980Z",
          "callId": "run_shell_command__call_164052",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test different completion thresholds:\n# What if minimum played ratio is 0%, 30%, 50%, 60%, 80%?\nfor min_pct in [0, 20, 30, 40, 50]:\n    mask = listens.played_ratio_pct >= min_pct\n    cur_u_l = u_l[mask]\n    cur_it_l = it_l[mask]\n    cur_decay = decay_l[mask]\n    \n    strong = (listens.played_ratio_pct[mask] >= 80).to_numpy()\n    organic = (listens.is_organic[mask] == 1).to_numpy()\n    w_base = np.ones(len(cur_u_l), dtype=np.float32)\n    w_base[~strong & ~organic] = 1.0\n    w_base[strong & ~organic] = 1.5\n    w_base[~strong & organic] = 2.0\n    w_base[strong & organic] = 3.0\n    \n    all_u = np.concatenate([cur_u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([cur_it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([w_base * cur_decay, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'min_played_ratio >= {min_pct}%')\n\"",
            "description": "Evaluate minimum played ratio threshold"
          },
          "recorded": {
            "id": "run_shell_command__call_164052",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\n# 1. Load targets, catalog, and eligibility\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Test different completion thresholds:\n# What if minimum played ratio is 0%, 30%, 50%, 60%, 80%?\nfor min_pct in [0, 20, 30, 40, 50]:\n    mask = listens.played_ratio_pct >= min_pct\n    cur_u_l = u_l[mask]\n    cur_it_l = it_l[mask]\n    cur_decay = decay_l[mask]\n    \n    strong = (listens.played_ratio_pct[mask] >= 80).to_numpy()\n    organic = (listens.is_organic[mask] == 1).to_numpy()\n    w_base = np.ones(len(cur_u_l), dtype=np.float32)\n    w_base[~strong & ~organic] = 1.0\n    w_base[strong & ~organic] = 1.5\n    w_base[~strong & organic] = 2.0\n    w_base[strong & organic] = 3.0\n    \n    all_u = np.concatenate([cur_u_l, u_lk, u_pref_l])\n    all_it = np.concatenate([cur_it_l, it_lk, it_pref_l])\n    all_w = np.concatenate([w_base * cur_decay, 5.0 * decay_lk, w_pref_l])\n    \n    R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n    R.data = np.log1p(R.data)\n    \n    N_users = len(target_uids)\n    doc_freq = np.diff(R.tocsc().indptr)\n    idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n    R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n    \n    svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'min_played_ratio >= {min_pct}%')\n\"",
              "description": "Evaluate minimum played ratio threshold"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_164052",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: min_played_ratio >= 0% -> NDCG@10: 0.041682\nmin_played_ratio >= 20% -> NDCG@10: 0.042088\nmin_played_ratio >= 30% -> NDCG@10: 0.042272\nmin_played_ratio >= 40% -> NDCG@10: 0.04133\nmin_played_ratio >= 50% -> NDCG@10: 0.041569\nProcess Group PGID: 2717\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:02:46.950Z",
            "resultDisplay": "min_played_ratio >= 0% -> NDCG@10: 0.041682\nmin_played_ratio >= 20% -> NDCG@10: 0.042088\nmin_played_ratio >= 30% -> NDCG@10: 0.042272\nmin_played_ratio >= 40% -> NDCG@10: 0.04133\nmin_played_ratio >= 50% -> NDCG@10: 0.041569",
            "description": "Evaluate minimum played ratio threshold",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:02:46.953Z",
            "content": "<untrusted_context>\nOutput: min_played_ratio >= 0% -> NDCG@10: 0.041682\nmin_played_ratio >= 20% -> NDCG@10: 0.042088\nmin_played_ratio >= 30% -> NDCG@10: 0.042272\nmin_played_ratio >= 40% -> NDCG@10: 0.04133\nmin_played_ratio >= 50% -> NDCG@10: 0.041569\nProcess Group PGID: 2717\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "7b56b3fc-2d62-43e5-9477-994bcbb22b06"
        }
      ]
    },
    {
      "id": 52,
      "role": "agent",
      "timestamp": "2026-09-29T04:02:57.954Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:02:57.954Z",
          "text": "Analyzing Performance Boosts\n\nMy analysis shows a slight but positive increase in the `min_played_ratio >= 30%` metric, suggesting that refining playback thresholds is indeed reducing noise and improving NDCG. I'm now exploring the impact of `n_components` around 160 in conjunction with this improved filtering."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:02:57.954Z",
          "callId": "run_shell_command__call_255700",
          "name": "run_shell_command",
          "input": {
            "description": "Scan k around 150 for min_played_ratio >= 30%",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [100, 120, 140, 150, 160, 170, 180, 200]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'k={k}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_255700",
            "name": "run_shell_command",
            "args": {
              "description": "Scan k around 150 for min_played_ratio >= 30%",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [100, 120, 140, 150, 160, 170, 180, 200]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'k={k}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_255700",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: k=100 -> NDCG@10: 0.041147\nk=120 -> NDCG@10: 0.041725\nk=140 -> NDCG@10: 0.041758\nk=150 -> NDCG@10: 0.04131\nk=160 -> NDCG@10: 0.042272\nk=170 -> NDCG@10: 0.040027\nk=180 -> NDCG@10: 0.040822\nk=200 -> NDCG@10: 0.04042\nProcess Group PGID: 2751\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:03:36.778Z",
            "resultDisplay": "k=100 -> NDCG@10: 0.041147\nk=120 -> NDCG@10: 0.041725\nk=140 -> NDCG@10: 0.041758\nk=150 -> NDCG@10: 0.04131\nk=160 -> NDCG@10: 0.042272\nk=170 -> NDCG@10: 0.040027\nk=180 -> NDCG@10: 0.040822\nk=200 -> NDCG@10: 0.04042",
            "description": "Scan k around 150 for min_played_ratio >= 30%",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:03:36.780Z",
            "content": "<untrusted_context>\nOutput: k=100 -> NDCG@10: 0.041147\nk=120 -> NDCG@10: 0.041725\nk=140 -> NDCG@10: 0.041758\nk=150 -> NDCG@10: 0.04131\nk=160 -> NDCG@10: 0.042272\nk=170 -> NDCG@10: 0.040027\nk=180 -> NDCG@10: 0.040822\nk=200 -> NDCG@10: 0.04042\nProcess Group PGID: 2751\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "48c4261a-3941-4d2b-9a6a-cf349f420982"
        }
      ]
    },
    {
      "id": 53,
      "role": "agent",
      "timestamp": "2026-09-29T04:03:47.264Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:03:47.264Z",
          "text": "Exploring Randomized SVD\n\nI'm currently investigating the impact of the random seed on Randomized SVD, particularly its relationship with the `n_iter` parameter. My recent tests with `k=160` yielded a promising result of 0.042272, and I'm now keen to explore variations in `n_iter` to optimize performance."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:03:47.264Z",
          "callId": "run_shell_command__call_145442",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor n_iter in [3, 5, 7, 10]:\n    svd = TruncatedSVD(n_components=160, algorithm='randomized', n_iter=n_iter, random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'n_iter={n_iter}')\n\"",
            "description": "Test n_iter power iterations in TruncatedSVD"
          },
          "recorded": {
            "id": "run_shell_command__call_145442",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor n_iter in [3, 5, 7, 10]:\n    svd = TruncatedSVD(n_components=160, algorithm='randomized', n_iter=n_iter, random_state=42)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    evaluate_predictions(U @ V, f'n_iter={n_iter}')\n\"",
              "description": "Test n_iter power iterations in TruncatedSVD"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_145442",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: n_iter=3 -> NDCG@10: 0.042084\nn_iter=5 -> NDCG@10: 0.042272\nn_iter=7 -> NDCG@10: 0.041766\nn_iter=10 -> NDCG@10: 0.041552\nProcess Group PGID: 2785\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:04:11.715Z",
            "resultDisplay": "n_iter=3 -> NDCG@10: 0.042084\nn_iter=5 -> NDCG@10: 0.042272\nn_iter=7 -> NDCG@10: 0.041766\nn_iter=10 -> NDCG@10: 0.041552",
            "description": "Test n_iter power iterations in TruncatedSVD",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:04:11.717Z",
            "content": "<untrusted_context>\nOutput: n_iter=3 -> NDCG@10: 0.042084\nn_iter=5 -> NDCG@10: 0.042272\nn_iter=7 -> NDCG@10: 0.041766\nn_iter=10 -> NDCG@10: 0.041552\nProcess Group PGID: 2785\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "a2dc3718-7d8b-4c39-8813-5409e0865244"
        }
      ]
    },
    {
      "id": 54,
      "role": "agent",
      "timestamp": "2026-09-29T04:04:23.146Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:04:23.146Z",
          "text": "Testing ensemble gains\n\nI'm exploring ensemble methods to boost performance. My current focus is on averaging predictions from different random seeds and varying the `k` parameter. This should reveal if combining these slight variations yields a higher NDCG@10."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:04:23.146Z",
          "callId": "run_shell_command__call_164084",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Ensemble 3 SVDs with different seeds / k\nsum_scores = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nconfigs = [(160, 42), (160, 101), (140, 202), (160, 303)]\nfor k, seed in configs:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    score_k = U @ V\n    evaluate_predictions(score_k, f'Single model k={k}, seed={seed}')\n    sum_scores += score_k\n\nevaluate_predictions(sum_scores, 'Ensemble average of 4 SVD models')\n\"",
            "description": "Test multi-seed/multi-k ensemble average"
          },
          "recorded": {
            "id": "run_shell_command__call_164084",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Ensemble 3 SVDs with different seeds / k\nsum_scores = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nconfigs = [(160, 42), (160, 101), (140, 202), (160, 303)]\nfor k, seed in configs:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    score_k = U @ V\n    evaluate_predictions(score_k, f'Single model k={k}, seed={seed}')\n    sum_scores += score_k\n\nevaluate_predictions(sum_scores, 'Ensemble average of 4 SVD models')\n\"",
              "description": "Test multi-seed/multi-k ensemble average"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_164084",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Single model k=160, seed=42 -> NDCG@10: 0.042272\nSingle model k=160, seed=101 -> NDCG@10: 0.041304\nSingle model k=140, seed=202 -> NDCG@10: 0.04302\nSingle model k=160, seed=303 -> NDCG@10: 0.042018\nEnsemble average of 4 SVD models -> NDCG@10: 0.042823\nProcess Group PGID: 2813\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:04:47.154Z",
            "resultDisplay": "Single model k=160, seed=42 -> NDCG@10: 0.042272\nSingle model k=160, seed=101 -> NDCG@10: 0.041304\nSingle model k=140, seed=202 -> NDCG@10: 0.04302\nSingle model k=160, seed=303 -> NDCG@10: 0.042018\nEnsemble average of 4 SVD models -> NDCG@10: 0.042823",
            "description": "Test multi-seed/multi-k ensemble average",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:04:47.155Z",
            "content": "<untrusted_context>\nOutput: Single model k=160, seed=42 -> NDCG@10: 0.042272\nSingle model k=160, seed=101 -> NDCG@10: 0.041304\nSingle model k=140, seed=202 -> NDCG@10: 0.04302\nSingle model k=160, seed=303 -> NDCG@10: 0.042018\nEnsemble average of 4 SVD models -> NDCG@10: 0.042823\nProcess Group PGID: 2813\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "e96269ec-9fc4-4d12-966d-616dc9ad4412"
        }
      ]
    },
    {
      "id": 55,
      "role": "agent",
      "timestamp": "2026-09-29T04:05:12.494Z",
      "blocks": [
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:05:12.494Z",
          "callId": "run_shell_command__call_197275",
          "name": "run_shell_command",
          "input": {
            "description": "Evaluate 3-seed ensemble for k around 140",
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [130, 135, 140, 145]:\n    scores_k = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\n    for seed in [42, 101, 202]:\n        svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        scores_k += (U @ V) / 3.0\n    evaluate_predictions(scores_k, f'Ensemble of 3 seeds for k={k}')\n\""
          },
          "recorded": {
            "id": "run_shell_command__call_197275",
            "name": "run_shell_command",
            "args": {
              "description": "Evaluate 3-seed ensemble for k around 140",
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [130, 135, 140, 145]:\n    scores_k = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\n    for seed in [42, 101, 202]:\n        svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n        U = svd.fit_transform(R_p)\n        V = svd.components_\n        scores_k += (U @ V) / 3.0\n    evaluate_predictions(scores_k, f'Ensemble of 3 seeds for k={k}')\n\""
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_197275",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Ensemble of 3 seeds for k=130 -> NDCG@10: 0.041599\nEnsemble of 3 seeds for k=135 -> NDCG@10: 0.041803\nEnsemble of 3 seeds for k=140 -> NDCG@10: 0.043033\nEnsemble of 3 seeds for k=145 -> NDCG@10: 0.042618\nProcess Group PGID: 2853\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:06:01.050Z",
            "resultDisplay": "Ensemble of 3 seeds for k=130 -> NDCG@10: 0.041599\nEnsemble of 3 seeds for k=135 -> NDCG@10: 0.041803\nEnsemble of 3 seeds for k=140 -> NDCG@10: 0.043033\nEnsemble of 3 seeds for k=145 -> NDCG@10: 0.042618",
            "description": "Evaluate 3-seed ensemble for k around 140",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:06:01.052Z",
            "content": "<untrusted_context>\nOutput: Ensemble of 3 seeds for k=130 -> NDCG@10: 0.041599\nEnsemble of 3 seeds for k=135 -> NDCG@10: 0.041803\nEnsemble of 3 seeds for k=140 -> NDCG@10: 0.043033\nEnsemble of 3 seeds for k=145 -> NDCG@10: 0.042618\nProcess Group PGID: 2853\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "bd6b5c28-990b-4d83-afd6-de9db6b4be89"
        }
      ]
    },
    {
      "id": 56,
      "role": "agent",
      "timestamp": "2026-09-29T04:06:21.475Z",
      "blocks": [
        {
          "kind": "reasoning",
          "representation": "summary",
          "timestamp": "2026-09-29T04:06:21.475Z",
          "text": "Achieving Enhanced NDCG\n\nI've successfully developed an ensemble of three seeds for k=140, achieving an NDCG@10 score of 0.043033. This represents a significant improvement, more than quadrupling the baseline of 0.01071, and this impressive gain was realized in a mere 10 seconds of computation. My next step is to investigate variations in k, specifically exploring k=14."
        },
        {
          "kind": "tool",
          "timestamp": "2026-09-29T04:06:21.475Z",
          "callId": "run_shell_command__call_271606",
          "name": "run_shell_command",
          "input": {
            "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Multi-rank ensemble: k in [120, 140, 160] with different seeds\nscores_multi = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nfor k, seed in [(120, 42), (140, 101), (140, 202), (160, 303), (140, 404)]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores_multi += (U @ V) / 5.0\n\nevaluate_predictions(scores_multi, 'Multi-rank ensemble (5 components)')\n\"",
            "description": "Evaluate multi-rank 5-component ensemble"
          },
          "recorded": {
            "id": "run_shell_command__call_271606",
            "name": "run_shell_command",
            "args": {
              "command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\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\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\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n    filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n    ranked = {}\n    for i, uid in enumerate(target_uids):\n        user_scores = scores[i].copy()\n        bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n        user_scores[bits == 0] = -np.inf\n        top100_idx = np.argpartition(user_scores, -100)[-100:]\n        top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[int(uid)] = list(item_ids[top100_sorted])\n    res = score_rankings(ranked, graded)\n    print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n    return res[\\\"quality_ndcg@10\\\"]\n\n# Multi-rank ensemble: k in [120, 140, 160] with different seeds\nscores_multi = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nfor k, seed in [(120, 42), (140, 101), (140, 202), (160, 303), (140, 404)]:\n    svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n    U = svd.fit_transform(R_p)\n    V = svd.components_\n    scores_multi += (U @ V) / 5.0\n\nevaluate_predictions(scores_multi, 'Multi-rank ensemble (5 components)')\n\"",
              "description": "Evaluate multi-rank 5-component ensemble"
            },
            "result": [
              {
                "functionResponse": {
                  "id": "run_shell_command__call_271606",
                  "name": "run_shell_command",
                  "response": {
                    "output": "<untrusted_context>\nOutput: Multi-rank ensemble (5 components) -> NDCG@10: 0.042633\nProcess Group PGID: 2887\n</untrusted_context>"
                  }
                }
              }
            ],
            "status": "success",
            "timestamp": "2026-09-29T04:06:44.027Z",
            "resultDisplay": "Multi-rank ensemble (5 components) -> NDCG@10: 0.042633",
            "description": "Evaluate multi-rank 5-component ensemble",
            "displayName": "Shell",
            "renderOutputAsMarkdown": false
          },
          "result": {
            "timestamp": "2026-09-29T04:06:44.028Z",
            "content": "<untrusted_context>\nOutput: Multi-rank ensemble (5 components) -> NDCG@10: 0.042633\nProcess Group PGID: 2887\n</untrusted_context>",
            "isError": false
          },
          "responseMessageId": "2a415bf9-bc54-4422-8da2-3f25f205a07d"
        }
      ]
    }
  ],
  "source": {
    "format": "gemini-cli-session",
    "sha256": "b12751d5b7f016d5450f1ad25a53648f1b21b412448b495b5b626d2cc4ec2992",
    "records": 274,
    "sessionMetadata": {
      "sessionId": "60e34f21-2eba-4fe1-822f-83a1c914ba13",
      "projectHash": "f53b52ad6d21cceb72dfa78fb67614fe14f110c58e68412b01508d6a485501c3",
      "startTime": "2026-09-29T03:36:53.179Z",
      "lastUpdated": "2026-09-29T03:36:53.179Z",
      "kind": "main"
    },
    "sessionUpdates": 110,
    "usageRecords": [
      {
        "messageId": "1f0ca458-e63e-4fd8-9273-579af4192f1e",
        "timestamp": "2026-09-29T03:36:59.314Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 9890,
          "output": 266,
          "cached": 0,
          "thoughts": 301,
          "tool": 0,
          "total": 10457
        }
      },
      {
        "messageId": "271013fe-e1b4-4ef1-8e7a-30bef1b4a9fd",
        "timestamp": "2026-09-29T03:37:03.579Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 14319,
          "output": 123,
          "cached": 7744,
          "thoughts": 35,
          "tool": 0,
          "total": 14477
        }
      },
      {
        "messageId": "e8a646d9-6d05-4792-bb05-3724eaf31cf4",
        "timestamp": "2026-09-29T03:37:21.172Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 16113,
          "output": 95,
          "cached": 11640,
          "thoughts": 139,
          "tool": 0,
          "total": 16347
        }
      },
      {
        "messageId": "9141221a-a4f4-4c67-8949-5adec5957afa",
        "timestamp": "2026-09-29T03:37:26.436Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 16396,
          "output": 291,
          "cached": 0,
          "thoughts": 50,
          "tool": 0,
          "total": 16737
        }
      },
      {
        "messageId": "de9baac5-c025-4d22-bce4-63270dbe9aed",
        "timestamp": "2026-09-29T03:37:36.712Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 16932,
          "output": 317,
          "cached": 11635,
          "thoughts": 176,
          "tool": 0,
          "total": 17425
        }
      },
      {
        "messageId": "3fb86889-6601-4f71-823a-6c2a91b333ec",
        "timestamp": "2026-09-29T03:37:45.360Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 17518,
          "output": 130,
          "cached": 15520,
          "thoughts": 92,
          "tool": 0,
          "total": 17740
        }
      },
      {
        "messageId": "331df173-9f84-48d5-b36a-d35f1d148d23",
        "timestamp": "2026-09-29T03:37:50.544Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 17778,
          "output": 309,
          "cached": 15510,
          "thoughts": 157,
          "tool": 0,
          "total": 18244
        }
      },
      {
        "messageId": "081e2493-6612-4ec3-ac1a-c3d4512c8346",
        "timestamp": "2026-09-29T03:37:54.487Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 18310,
          "output": 298,
          "cached": 15513,
          "thoughts": 29,
          "tool": 0,
          "total": 18637
        }
      },
      {
        "messageId": "6f8ab0b5-b62d-4326-b8fe-c043bf5bfe35",
        "timestamp": "2026-09-29T03:38:01.704Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 18696,
          "output": 411,
          "cached": 15510,
          "thoughts": 218,
          "tool": 0,
          "total": 19325
        }
      },
      {
        "messageId": "54317ab8-b8aa-4613-a786-dc8ac6fb91f3",
        "timestamp": "2026-09-29T03:38:08.838Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 19378,
          "output": 284,
          "cached": 15519,
          "thoughts": 48,
          "tool": 0,
          "total": 19710
        }
      },
      {
        "messageId": "120bd193-db0f-4d36-a3ee-63ea98b83d40",
        "timestamp": "2026-09-29T03:38:16.627Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 19796,
          "output": 131,
          "cached": 15517,
          "thoughts": 46,
          "tool": 0,
          "total": 19973
        }
      },
      {
        "messageId": "c745cc9f-2b7f-4a2e-8043-976b8b783167",
        "timestamp": "2026-09-29T03:38:24.240Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 20083,
          "output": 244,
          "cached": 15507,
          "thoughts": 303,
          "tool": 0,
          "total": 20630
        }
      },
      {
        "messageId": "f10560af-68c8-4983-9923-d1297621714a",
        "timestamp": "2026-09-29T03:38:38.176Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 21160,
          "output": 235,
          "cached": 15531,
          "thoughts": 663,
          "tool": 0,
          "total": 22058
        }
      },
      {
        "messageId": "d0c14da7-7d03-44b2-9c2b-69f6d07aabc5",
        "timestamp": "2026-09-29T03:38:43.258Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 22124,
          "output": 237,
          "cached": 19437,
          "thoughts": 31,
          "tool": 0,
          "total": 22392
        }
      },
      {
        "messageId": "db650203-b85b-4158-9395-856c00f546f8",
        "timestamp": "2026-09-29T03:38:51.925Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 22481,
          "output": 420,
          "cached": 19431,
          "thoughts": 630,
          "tool": 0,
          "total": 23531
        }
      },
      {
        "messageId": "03b169ac-4fa1-476d-bfae-e42a8fd8391c",
        "timestamp": "2026-09-29T03:39:03.722Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 23654,
          "output": 164,
          "cached": 19460,
          "thoughts": 863,
          "tool": 0,
          "total": 24681
        }
      },
      {
        "messageId": "93de3b63-ff54-481f-98d2-5056a122be24",
        "timestamp": "2026-09-29T03:39:14.355Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 25307,
          "output": 428,
          "cached": 19505,
          "thoughts": 182,
          "tool": 0,
          "total": 25917
        }
      },
      {
        "messageId": "c517cd56-5db6-4f76-9bd7-a4f3ec2495ce",
        "timestamp": "2026-09-29T03:39:22.810Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 25966,
          "output": 267,
          "cached": 23411,
          "thoughts": 63,
          "tool": 0,
          "total": 26296
        }
      },
      {
        "messageId": "ee158623-a5ec-4fd4-bb03-0a4032d76e54",
        "timestamp": "2026-09-29T03:39:33.819Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 26364,
          "output": 338,
          "cached": 23406,
          "thoughts": 718,
          "tool": 0,
          "total": 27420
        }
      },
      {
        "messageId": "fc52d889-9acf-4f6a-b4e2-10d49a530371",
        "timestamp": "2026-09-29T03:39:46.202Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 27507,
          "output": 250,
          "cached": 23431,
          "thoughts": 93,
          "tool": 0,
          "total": 27850
        }
      },
      {
        "messageId": "8fe30a29-fc2b-4511-b844-c6b63d2e0d06",
        "timestamp": "2026-09-29T03:40:10.420Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 27911,
          "output": 169,
          "cached": 23426,
          "thoughts": 1436,
          "tool": 0,
          "total": 29516
        }
      },
      {
        "messageId": "20552c17-af08-4a6f-8df4-55fd3f534cc7",
        "timestamp": "2026-09-29T03:40:35.318Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 29563,
          "output": 230,
          "cached": 27380,
          "thoughts": 78,
          "tool": 0,
          "total": 29871
        }
      },
      {
        "messageId": "ff82677a-c4bd-42ba-81f5-b5127e7d8ac7",
        "timestamp": "2026-09-29T03:41:09.300Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 30029,
          "output": 201,
          "cached": 27376,
          "thoughts": 136,
          "tool": 0,
          "total": 30366
        }
      },
      {
        "messageId": "431ce8b9-4039-4d7c-b11e-80d99681435f",
        "timestamp": "2026-09-29T03:41:15.827Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 30439,
          "output": 209,
          "cached": 27370,
          "thoughts": 9,
          "tool": 0,
          "total": 30657
        }
      },
      {
        "messageId": "d19b255c-9797-41e3-b029-f2e0c75d6648",
        "timestamp": "2026-09-29T03:44:09.636Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 30737,
          "output": 148,
          "cached": 0,
          "thoughts": 137,
          "tool": 0,
          "total": 31022
        }
      },
      {
        "messageId": "9e4ba686-c3c0-4720-82dc-453c6cedd786",
        "timestamp": "2026-09-29T03:44:14.842Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 31076,
          "output": 275,
          "cached": 27352,
          "thoughts": 106,
          "tool": 0,
          "total": 31457
        }
      },
      {
        "messageId": "3989b043-875d-4887-bd65-f7add6bbff7f",
        "timestamp": "2026-09-29T03:44:39.380Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 31517,
          "output": 137,
          "cached": 27348,
          "thoughts": 557,
          "tool": 0,
          "total": 32211
        }
      },
      {
        "messageId": "4e298771-6128-48b7-8c16-c2aab99fed2f",
        "timestamp": "2026-09-29T03:44:54.312Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 32261,
          "output": 969,
          "cached": 27356,
          "thoughts": 767,
          "tool": 0,
          "total": 33997
        }
      },
      {
        "messageId": "271d12b1-0260-43a0-b122-d9395ba7007d",
        "timestamp": "2026-09-29T03:45:40.145Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 34227,
          "output": 148,
          "cached": 31324,
          "thoughts": 137,
          "tool": 0,
          "total": 34512
        }
      },
      {
        "messageId": "df3be5ef-0c38-463d-963b-4be2924dc651",
        "timestamp": "2026-09-29T03:45:55.984Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 34639,
          "output": 802,
          "cached": 31315,
          "thoughts": 575,
          "tool": 0,
          "total": 36016
        }
      },
      {
        "messageId": "9040fcd9-51cd-4f87-9b22-71fce3ff5078",
        "timestamp": "2026-09-29T03:47:08.168Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 36181,
          "output": 261,
          "cached": 31352,
          "thoughts": 197,
          "tool": 0,
          "total": 36639
        }
      },
      {
        "messageId": "f2c1539f-f765-424a-9df8-7d5dae224fe7",
        "timestamp": "2026-09-29T03:47:28.152Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 36668,
          "output": 519,
          "cached": 31349,
          "thoughts": 319,
          "tool": 0,
          "total": 37506
        }
      },
      {
        "messageId": "a45c26f4-8d4d-4f92-b5de-b20ed608cf81",
        "timestamp": "2026-09-29T03:48:30.590Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 37561,
          "output": 167,
          "cached": 35281,
          "thoughts": 497,
          "tool": 0,
          "total": 38225
        }
      },
      {
        "messageId": "2f52fae8-aac1-44d8-9752-a4dfbb587365",
        "timestamp": "2026-09-29T03:49:14.486Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 38282,
          "output": 1453,
          "cached": 35286,
          "thoughts": 924,
          "tool": 0,
          "total": 40659
        }
      },
      {
        "messageId": "8a935e5f-d11e-41b3-b495-7866e2688417",
        "timestamp": "2026-09-29T03:49:53.854Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 40774,
          "output": 1174,
          "cached": 35357,
          "thoughts": 220,
          "tool": 0,
          "total": 42168
        }
      },
      {
        "messageId": "fb99212a-94fb-4914-98c0-12c24dc899fd",
        "timestamp": "2026-09-29T03:51:29.868Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 42370,
          "output": 1168,
          "cached": 39323,
          "thoughts": 74,
          "tool": 0,
          "total": 43612
        }
      },
      {
        "messageId": "e05266c4-d760-4a46-8bc1-60a468128c22",
        "timestamp": "2026-09-29T03:53:08.228Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 43809,
          "output": 1461,
          "cached": 39352,
          "thoughts": 273,
          "tool": 0,
          "total": 45543
        }
      },
      {
        "messageId": "b2f20f21-91b7-40c1-8b40-c788bb1a01de",
        "timestamp": "2026-09-29T03:53:52.246Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 47025,
          "output": 221,
          "cached": 43381,
          "thoughts": 383,
          "tool": 0,
          "total": 47629
        }
      },
      {
        "messageId": "bf1cc3ff-70f5-4065-9ed8-d8da3ad32292",
        "timestamp": "2026-09-29T03:53:59.271Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 47684,
          "output": 181,
          "cached": 43381,
          "thoughts": 277,
          "tool": 0,
          "total": 48142
        }
      },
      {
        "messageId": "9c5cd262-7b7f-4fd3-a4b2-b0e485c1c25f",
        "timestamp": "2026-09-29T03:54:17.427Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 48210,
          "output": 1817,
          "cached": 43376,
          "thoughts": 556,
          "tool": 0,
          "total": 50583
        }
      },
      {
        "messageId": "8e22ca23-e112-40eb-a686-095a7eda83ee",
        "timestamp": "2026-09-29T03:54:43.560Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 50751,
          "output": 1656,
          "cached": 47384,
          "thoughts": 81,
          "tool": 0,
          "total": 52488
        }
      },
      {
        "messageId": "38e434cc-b81c-4d7d-8726-6cabd8583b8c",
        "timestamp": "2026-09-29T03:55:18.488Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 52575,
          "output": 1607,
          "cached": 47421,
          "thoughts": 612,
          "tool": 0,
          "total": 54794
        }
      },
      {
        "messageId": "47e527f1-5bc7-4aa7-ac7e-1fa9e39c35cf",
        "timestamp": "2026-09-29T03:55:56.395Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 54987,
          "output": 1668,
          "cached": 51428,
          "thoughts": 145,
          "tool": 0,
          "total": 56800
        }
      },
      {
        "messageId": "76e9031c-c161-44e6-b1d3-b4945679f03a",
        "timestamp": "2026-09-29T03:56:58.590Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 57042,
          "output": 1518,
          "cached": 51469,
          "thoughts": 200,
          "tool": 0,
          "total": 58760
        }
      },
      {
        "messageId": "d17eb62f-81dd-49c7-948d-76220d081561",
        "timestamp": "2026-09-29T03:59:12.174Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 59345,
          "output": 1737,
          "cached": 0,
          "thoughts": 148,
          "tool": 0,
          "total": 61230
        }
      },
      {
        "messageId": "1502faf1-597f-497d-bd99-f7c95683166f",
        "timestamp": "2026-09-29T03:59:53.247Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 61351,
          "output": 1737,
          "cached": 55513,
          "thoughts": 295,
          "tool": 0,
          "total": 63383
        }
      },
      {
        "messageId": "bf7c832c-59c4-4af6-87eb-c36f5956ad1f",
        "timestamp": "2026-09-29T04:00:31.748Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 63565,
          "output": 1885,
          "cached": 59521,
          "thoughts": 146,
          "tool": 0,
          "total": 65596
        }
      },
      {
        "messageId": "7faf8f81-2a00-4623-bcdb-27d87e1cc591",
        "timestamp": "2026-09-29T04:01:11.788Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 67538,
          "output": 1612,
          "cached": 63582,
          "thoughts": 318,
          "tool": 0,
          "total": 69468
        }
      },
      {
        "messageId": "5ddf2833-2785-4164-ba02-d67c060d485d",
        "timestamp": "2026-09-29T04:02:14.980Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 69627,
          "output": 1605,
          "cached": 63616,
          "thoughts": 429,
          "tool": 0,
          "total": 71661
        }
      },
      {
        "messageId": "50b5af0d-923c-485b-9aba-7995abdea513",
        "timestamp": "2026-09-29T04:02:57.954Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 71819,
          "output": 1500,
          "cached": 67629,
          "thoughts": 133,
          "tool": 0,
          "total": 73452
        }
      },
      {
        "messageId": "211c346b-40f1-4461-ab4a-43bd55924fe5",
        "timestamp": "2026-09-29T04:03:47.264Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 73651,
          "output": 1483,
          "cached": 71633,
          "thoughts": 141,
          "tool": 0,
          "total": 75275
        }
      },
      {
        "messageId": "66eae859-1735-4a02-be47-e083465fab94",
        "timestamp": "2026-09-29T04:04:23.146Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 75389,
          "output": 1584,
          "cached": 71656,
          "thoughts": 80,
          "tool": 0,
          "total": 77053
        }
      },
      {
        "messageId": "5c840c46-ec9c-428f-b1a1-7d0965a1aa7c",
        "timestamp": "2026-09-29T04:05:12.494Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 77222,
          "output": 1544,
          "cached": 71681,
          "thoughts": 94,
          "tool": 0,
          "total": 78860
        }
      },
      {
        "messageId": "97fd2335-d16d-4d8d-8b6d-0fecf2d47755",
        "timestamp": "2026-09-29T04:06:21.475Z",
        "model": "gemini-3.8-flash",
        "tokens": {
          "input": 79000,
          "output": 1573,
          "cached": 75686,
          "thoughts": 156,
          "tool": 0,
          "total": 80729
        }
      }
    ]
  }
}