Agent trace

Two-tower retriever gemini-3-8-flash-high-01

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

This recording includes 180 readable reasoning summaries, shown in full below.

Browse attempts on this task

Agent: gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation: 59:05 · 87 steps
Private reward: 0.009092 · Failed
Attempt cost: $2.01

Thinking
Model text
Tool call
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User context

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User context

<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app

Workspace Directories:
/app
/task
Directory Structure:

Showing up to 200 items (files + folders).

/app/ └───solution/ Showing up to 200 items (files + folders).

/task/ ├───.keep ├───evaluate_public.py ├───ranking_evaluation.py ├───requirements.txt └───data/ ├───candidate_catalog.parquet ├───target_users.parquet ├───test_eligible_candidates.npz ├───test_preference_state.parquet ├───train_dislikes.parquet ├───train_likes.parquet ├───train_listens.parquet ├───train_undislikes.parquet ├───train_unlikes.parquet ├───val_dislikes.parquet ├───val_likes.parquet ├───val_listens.parquet ├───val_undislikes.parquet ├───val_unlikes.parquet ├───validation_eligible_candidates.npz └───validation_preference_state.parquet

</session_context>

Task instructions

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Task instructions

Build a latency-efficient PyTorch two-tower neural-network ranker

Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search.

The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

For each event family there is a train_*.parquet and val_*.parquet file:

Family	Meaning	Columns
`listens`	Playback events	`uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds`
`likes`	Explicit positive actions	`uid`, `timestamp`, `item_id`, `is_organic`
`dislikes`	Explicit negative actions	`uid`, `timestamp`, `item_id`, `is_organic`
`unlikes`	Reversal of a prior like	`uid`, `timestamp`, `item_id`, `is_organic`
`undislikes`	Reversal of a prior dislike	`uid`, `timestamp`, `item_id`, `is_organic`

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

grade 3: the user organically likes the track;
grade 1: the user organically listens to at least 80% of the track;
grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Public tools

/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that defines, trains, and applies both towers, along with a concise /app/solution/README.md, in /app/solution/. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

Write deliverables and intermediate artifacts only under /app/solution/.
Treat /task/ and the installed Python environment as read-only.
Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
Do not install packages or use network access except for the model provider connection managed by Harbor.
The environment includes CPU PyTorch 2.8.0. No accelerator is available.
You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.

update_topic command

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Recorded reasoning summary 38 words

Reviewing Task Parameters

I'm currently scrutinizing the prompt, diligently assessing all requirements, constraints, and the established directory structure. My immediate next step is to execute the update_topic function, as dictated by the Core Mandates for the initial turn.

Tool callupdate_topicTool call Returned

Tool call · Input +00:04

{
  "summary": "Starting the project by inspecting the provided task evaluation script, helper modules, candidate catalog, target users, and interaction data schemas. This initial research phase will establish baseline data statistics, evaluate the feasibility of candidate modeling approaches, and set up our development and validation pipeline.",
  "title": "Research and Pipeline Setup",
  "strategic_intent": "Inspect the task environment, data files, evaluation scripts, and requirements to understand the problem setup."
}

Tool response +00:04

Current topic: "Research and Pipeline Setup"
Topic summary: Starting the project by inspecting the provided task evaluation script, helper modules, candidate catalog, target users, and interaction data schemas. This initial research phase will establish baseline data statistics, evaluate the feasibility of candidate modeling approaches, and set up our development and validation pipeline.

Strategic Intent: Inspect the task environment, data files, evaluation scripts, and requirements to understand the problem setup.

Tool callread_file/task/requirements.txt Returned

Tool call · Input +00:04

{
  "file_path": "/task/requirements.txt"
}

Tool response +00:04

# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1

Tool callread_file/task/evaluate_public.py Returned

Tool call · Input +00:04

{
  "file_path": "/task/evaluate_public.py"
}

Tool response +00:04

#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()

Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input +00:04

{
  "file_path": "/task/ranking_evaluation.py"
}

Tool response +00:04

"""Shared public primitives for Yambda discovery-ranking evaluation.

Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.

The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd


__all__ = [
    "CandidateSet",
    "QUALITY_K",
    "RankingValidationError",
    "REQUIRED_COLUMNS",
    "TOP_N",
    "ndcg_at_k",
    "ranked_items",
    "read_ranking",
    "score_rankings",
    "validate_ranking",
]

TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")


class RankingValidationError(ValueError):
    """Report a ranking-schema or structural-contract violation."""


class CandidateSet:
    """Provide membership lookups over one packed candidate-set archive.

    The archive contains aligned ``uids``, ``item_ids``, and
    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
    copied into memory so they remain valid after the archive is closed.
    """

    def __init__(self, path: Path):
        """Load an eligibility archive and build ID-to-position indexes.

        Args:
            path: Path to a ``.npz`` archive with the candidate-set schema.
        """
        with np.load(path, allow_pickle=False) as artifact:
            self.uids = artifact["uids"].astype(np.int64, copy=True)
            self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
            self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
        self.item_column = {
            int(item): column for column, item in enumerate(self.item_ids)
        }

    def is_eligible(self, uid: int, item_id: int) -> bool:
        """Return whether ``item_id`` is eligible for ``uid``.

        IDs absent from the target-user or candidate-item arrays are treated as
        ineligible rather than raising an exception.

        Args:
            uid: Target user ID to query.
            item_id: Candidate item ID to query.

        Returns:
            ``True`` only when the corresponding packed eligibility bit is set.
        """
        row = self.uid_row.get(int(uid))
        column = self.item_column.get(int(item_id))
        if row is None or column is None:
            return False
        byte, bit = divmod(column, 8)
        return bool((int(self.packed[row, byte]) >> bit) & 1)


def read_ranking(path: Path) -> pd.DataFrame:
    """Read a parquet or CSV ranking into a pandas frame.

    Args:
        path: Input path. A ``.parquet`` suffix selects parquet; every other
            suffix is interpreted as CSV.

    Returns:
        The ranking frame as loaded by pandas.
    """
    return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)


def validate_ranking(
    frame: pd.DataFrame,
    targets: set[int],
) -> pd.DataFrame:
    """Normalize and validate the stage-independent ranking contract.

    Required values must be finite integers. The user set must exactly match
    ``targets``; every user must have 100 rows, distinct items, and each rank
    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
    intentionally left to the calling evaluator.

    Args:
        frame: Raw ranking frame. Extra columns are discarded from an internal
            copy; the caller's frame is not modified.
        targets: Exact set of target user IDs the ranking must cover.

    Returns:
        A normalized frame containing only the required ``int64`` columns.

    Raises:
        RankingValidationError: If the ranking violates the shared contract.
    """
    required = list(REQUIRED_COLUMNS)
    if not all(column in frame for column in required):
        raise RankingValidationError(f"submission requires columns {required}")

    normalized = frame[required].copy()
    for column in required:
        numeric = pd.to_numeric(normalized[column], errors="coerce")
        values = numeric.to_numpy(dtype=float)
        if numeric.isna().any() or not np.isfinite(values).all():
            raise RankingValidationError(
                f"submission {column} must be finite numeric values"
            )
        if not np.equal(numeric, np.floor(numeric)).all():
            raise RankingValidationError(
                f"submission {column} must contain integers"
            )
        normalized[column] = numeric.astype(np.int64)

    submitted_users = set(map(int, normalized.uid.unique()))
    if submitted_users != targets:
        raise RankingValidationError(
            f"submission user set mismatch: missing={len(targets - submitted_users)} "
            f"extra={len(submitted_users - targets)}"
        )
    counts = normalized.groupby("uid").size()
    if not (counts == TOP_N).all():
        raise RankingValidationError(
            f"submission must contain exactly {TOP_N} rows for every target user"
        )
    if normalized.duplicated(["uid", "item_id"]).any():
        raise RankingValidationError(
            "submission contains duplicate (uid, item_id) pairs"
        )
    valid_ranks = set(range(1, TOP_N + 1))
    ranks_are_valid = normalized.groupby("uid")["rank"].apply(
        lambda values: set(map(int, values)) == valid_ranks
    )
    if not ranks_are_valid.all():
        raise RankingValidationError(
            f"each user must have every rank from 1 through {TOP_N} exactly once"
        )
    return normalized


def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
    """Convert a validated ranking frame to ordered items keyed by user.

    Args:
        frame: Frame that has passed :func:`validate_ranking`.

    Returns:
        A ``uid -> item IDs`` mapping ordered by ascending rank.
    """
    ordered = frame.sort_values(["uid", "rank"])
    return {
        int(uid): list(map(int, values))
        for uid, values in ordered.groupby("uid").item_id
    }


def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
    """Compute graded normalized discounted cumulative gain at ``k``.

    Relevance gain is ``2**grade - 1`` and the discount at one-based rank
    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.

    Args:
        ranked: Item IDs in predicted rank order.
        gold: Mapping from relevant item IDs to integer relevance grades.
        k: Maximum number of predicted positions to score.

    Returns:
        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
        ``0.0`` when the ideal gain is zero.
    """
    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
    return dcg / idcg if idcg else 0.0


def score_rankings(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Relevant item grades keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }

Inspect data directory sizes and table schemas

+00:18

Recorded reasoning summary 39 words

Analyzing Model Constraints

I'm currently dissecting the specifications for building a two-tower neural network. My focus is on understanding the nuances of CPU PyTorch implementation and the implications of a smaller benchmark dataset for the "Made for You" shelf.

Recorded reasoning summary 53 words

Developing Two-Tower Structure

I'm refining the architecture, ensuring it avoids global user-item matrices for scalable production. The key is an offline candidate generation via nearest-neighbor search and an online dot product of independent user and item embeddings, both computed by separate PyTorch towers. My focus is on user tower derivation from interaction history.

Recorded reasoning summary 49 words

Defining Data Splits

I'm now clarifying the exact data boundaries, noting that training concludes at timestamp 24786800. A 30-minute buffer separates this from the seven-day public validation window, followed by another 30-minute gap and the subsequent seven-day hidden test period. I've also documented the available event and catalog tables.

Recorded reasoning summary 54 words

Refining Prediction Strategy

I'm now considering how to utilize explicit user states like 'liked' or 'disliked' for the final hidden-test ranking. This involves ensuring candidate sets for validation and testing are cutoff-correct, and planning a two-stage approach: first tuning on validation data, then refitting on combined training and validation data for the hidden test.

Tool callrun_shell_commandInspect data directory sizes and table schemas Returned

Tool call · Input +00:18

{
  "description": "Inspect data directory sizes and table schemas",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nprint('=== Candidate Catalog ===')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nprint('Catalog shape:', cat.shape)\nprint('Catalog columns:', cat.columns.tolist())\nprint(cat.head(3))\n\nprint('\\n=== Target Users ===')\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('Users shape:', users.shape)\nprint('Users columns:', users.columns.tolist())\nprint(users.head(3))\n\nprint('\\n=== Files in data dir ===')\nfor f in sorted(data_dir.iterdir()):\n    if f.is_file():\n        size_mb = f.stat().st_size / (1024 * 1024)\n        print(f'{f.name:35s}: {size_mb:8.2f} MB')\n\""
}

Tool response +00:20

<untrusted_context>
Output: === Candidate Catalog ===
Catalog shape: (30000, 4)
Catalog columns: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3

=== Target Users ===
Users shape: (8456, 3)
Users columns: ['uid', 'recent_positive_listens', 'history_positive_listens']
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359

=== Files in data dir ===
candidate_catalog.parquet          :     0.33 MB
target_users.parquet               :     0.08 MB
test_eligible_candidates.npz       :     7.38 MB
test_preference_state.parquet      :     1.04 MB
train_dislikes.parquet             :     0.82 MB
train_likes.parquet                :     5.13 MB
train_listens.parquet              :   304.40 MB
train_undislikes.parquet           :     0.15 MB
train_unlikes.parquet              :     1.77 MB
val_dislikes.parquet               :     0.03 MB
val_likes.parquet                  :     0.28 MB
val_listens.parquet                :     9.49 MB
val_undislikes.parquet             :     0.01 MB
val_unlikes.parquet                :     0.07 MB
validation_eligible_candidates.npz :     7.22 MB
validation_preference_state.parquet:     1.01 MB
Process Group PGID: 1612
</untrusted_context>

Inspect row counts and timestamp ranges of interaction tables

+00:44

Recorded reasoning summary 55 words

Analyzing Data Sizes

My current focus is on understanding the scale of the datasets. I've noted that target_users.parquet contains 8,456 users and candidate_catalog.parquet holds 30,000 items. The training and validation listen data are substantial, with train_listens.parquet at 304 MB and val_listens.parquet at 9.5 MB. The eligible candidates files are smaller, at 7.22 MB for validation.

Tool callrun_shell_commandInspect row counts and timestamp ranges of interaction tables Returned

Tool call · Input +00:44

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\n\nfor name in ['train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes', 'val_likes', 'val_dislikes', 'val_listens', 'validation_preference_state', 'test_preference_state']:\n    df = pd.read_parquet(data_dir / f'{name}.parquet')\n    print(f'{name:30s}: shape={df.shape}, cols={df.columns.tolist()}')\n\n# Check train_listens length and columns\ntl_head = pd.read_parquet(data_dir / 'train_listens.parquet')\nprint(f'train_listens: shape={tl_head.shape}, cols={tl_head.columns.tolist()}')\nprint('train_listens min/max timestamp:', tl_head['timestamp'].min(), tl_head['timestamp'].max())\nprint('val_listens min/max timestamp:', pd.read_parquet(data_dir / 'val_listens.parquet')['timestamp'].min(), pd.read_parquet(data_dir / 'val_listens.parquet')['timestamp'].max())\n\"",
  "description": "Inspect row counts and timestamp ranges of interaction tables"
}

Tool response +00:46

<untrusted_context>
Output: train_likes                   : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_dislikes                : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes                 : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_undislikes              : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_likes                     : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes                  : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_listens                   : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
validation_preference_state   : shape=(469124, 3), cols=['uid', 'item_id', 'state']
test_preference_state         : shape=(484776, 3), cols=['uid', 'item_id', 'state']
train_listens: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_listens min/max timestamp: 0 24786795
val_listens min/max timestamp: 24788600 25393395
Process Group PGID: 1635
</untrusted_context>

Inspect target user presence in train and val datasets

+00:50

Recorded reasoning summary 31 words

Analyzing User Data

I'm currently examining the user data, confirming that all 8,456 target users are indeed present within the vast train_listens dataset. I'm also cross-referencing these with the val_listens set.

Tool callrun_shell_commandInspect target user presence in train and val datasets Returned

Tool call · Input +00:50

{
  "description": "Inspect target user presence in train and val datasets",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ndata_dir = '/task/data'\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\nprint('Number of target users:', len(targets))\nprint('Target user IDs min/max:', targets.uid.min(), targets.uid.max())\n\nval_targets = set(targets.uid)\nval_listens = pd.read_parquet(f'{data_dir}/val_listens.parquet')\nprint('val_listens unique uids:', val_listens.uid.nunique())\nprint('val_listens uids in target_users:', val_listens.uid.isin(val_targets).sum(), 'rows out of', len(val_listens))\nprint('val_listens target users present:', val_listens[val_listens.uid.isin(val_targets)].uid.nunique())\n\n# Check how many uids in train_listens\n# We can read just uid column from train_listens\ntrain_uids = pd.read_parquet(f'{data_dir}/train_listens.parquet', columns=['uid'])\nprint('train_listens total rows:', len(train_uids))\nprint('train_listens unique uids:', train_uids.uid.nunique())\nprint('train_listens target users present:', train_uids[train_uids.uid.isin(val_targets)].uid.nunique())\ntarget_train_rows = train_uids[train_uids.uid.isin(val_targets)].shape[0]\nprint('train_listens rows belonging to target_users:', target_train_rows)\n\""
}

Tool response +00:59

<untrusted_context>
Output: Number of target users: 8456
Target user IDs min/max: 100 1000000
val_listens unique uids: 8196
val_listens uids in target_users: 1482553 rows out of 1501697
val_listens target users present: 7729
train_listens total rows: 43484159
train_listens unique uids: 9238
train_listens target users present: 8456
train_listens rows belonging to target_users: 43222297
Process Group PGID: 1655
</untrusted_context>

Check available CPU and memory resources

+01:01

Tool callrun_shell_commandCheck available CPU and memory resources Returned

Tool call · Input +01:01

{
  "command": "lscpu | grep -E \"Model name|CPU\\(s\\):|Thread|Core|Socket\"; free -h",
  "description": "Check available CPU and memory resources"
}

Tool response +01:01

<untrusted_context>
Output: CPU(s):                                  8
Model name:                              AMD EPYC 7R13 Processor
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
NUMA node0 CPU(s):                       0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1676
</untrusted_context>

Check system memory in /proc/meminfo

+01:03

Tool callrun_shell_commandCheck system memory in /proc/meminfo Returned

Tool call · Input +01:03

{
  "command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\"",
  "description": "Check system memory in /proc/meminfo"
}

Tool response +01:03

<untrusted_context>
Output: MemTotal:       15988964 kB
MemAvailable:   14655176 kB
Process Group PGID: 1681
</untrusted_context>

Inspect preference state contents and distribution

+01:13

Recorded reasoning summary 43 words

Analyzing System Constraints

My current focus is on understanding the available CPU threads and RAM. I have 8 CPU threads and approximately 14.6 GB of RAM at my disposal, which I'll need to keep in mind for the model's computational and memory demands.

Recorded reasoning summary 57 words

Designing Scalable Towers

I'm now concentrating on the architectural design for the user and item towers. Given the production system's scale, I'll avoid global matrices. The core idea is a dot product between independently computed user and item embeddings, necessitating distinct, trainable PyTorch neural networks for each tower. My focus is on ensuring this approach scales efficiently.

Recorded reasoning summary 43 words

Refining Item Tower Inputs

I'm digging into the item tower's potential inputs, considering how to best represent items. Beyond just item IDs, I'm exploring incorporating features like track length and popularity to enrich the item vector. This aims for a more nuanced precomputation.

Recorded reasoning summary 51 words

Evaluating Item Tower Inputs

I'm considering how the item tower should process inputs. It appears it can accept item IDs, and I'm exploring if this embedding can be projected via a linear or MLP layer. I've noted the available item features like popularity_rank and track_length_seconds that could potentially enrich this process.

Recorded reasoning summary 42 words

Implementing User Tower Logic

I'm now focusing on the user tower's implementation, ensuring it derives representations from interaction history, not just user IDs. For the item tower, I'm confirming it can utilize embeddings and potentially project them via linear layers or MLPs.

Tool callrun_shell_commandInspect preference state contents and distribution Returned

Tool call · Input +01:13

{
  "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\n\nprint('val_pref states:', val_pref['state'].value_counts())\nprint(val_pref.head(5))\n\nprint('test_pref states:', test_pref['state'].value_counts())\nprint(test_pref.head(5))\n\"",
  "description": "Inspect preference state contents and distribution"
}

Tool response +01:15

<untrusted_context>
Output: val_pref states: state
liked       418435
disliked     50689
Name: count, dtype: int64
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
test_pref states: state
liked       432659
disliked     52117
Name: count, dtype: int64
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
Process Group PGID: 1685
</untrusted_context>

Inspect candidate sets and eligible counts per user

+01:19

Recorded reasoning summary 47 words

Analyzing User Preferences

My current focus is on quantifying user engagement with tracks, specifically examining the disparity between liked and disliked entries within both validation and testing datasets. The sheer volume of data, over 400,000 liked and 50,000 disliked tracks per dataset, suggests robust patterns to explore.

Tool callrun_shell_commandInspect candidate sets and eligible counts per user Returned

Tool call · Input +01:19

{
  "command": "python3 -c \"\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nwith np.load(data_dir / 'validation_eligible_candidates.npz') as val_npz:\n    uids = val_npz['uids']\n    items = val_npz['item_ids']\n    packed = val_npz['packed_eligible']\n    print('Validation candidates:')\n    print('uids shape:', uids.shape)\n    print('item_ids shape:', items.shape)\n    print('packed shape:', packed.shape)\n    \n    # Check count of eligible items for first few users and overall stats\n    # Sample 100 users to get stats\n    counts = []\n    for r in range(min(500, len(uids))):\n        mask = np.unpackbits(packed[r], count=len(items), bitorder='little')\n        counts.append(mask.sum())\n    print('Sample 500 users: min eligible:', min(counts), 'max eligible:', max(counts), 'mean:', np.mean(counts))\n\nwith np.load(data_dir / 'test_eligible_candidates.npz') as test_npz:\n    print('Test candidates:')\n    print('uids shape:', test_npz['uids'].shape)\n    print('item_ids shape:', test_npz['item_ids'].shape)\n\"",
  "description": "Inspect candidate sets and eligible counts per user"
}

Tool response +01:20

<untrusted_context>
Output: Validation candidates:
uids shape: (8456,)
item_ids shape: (30000,)
packed shape: (8456, 3750)
Sample 500 users: min eligible: 25547 max eligible: 29994 mean: 29057.652
Test candidates:
uids shape: (8456,)
item_ids shape: (30000,)
Process Group PGID: 1702
</untrusted_context>

Check alignment between catalog, targets, and candidate npz files

+01:23

Recorded reasoning summary 29 words

Confirming Data Consistency

I've confirmed that the item IDs across candidate_catalog.parquet, validation_eligible_candidates.npz, and test_eligible_candidates.npz are perfectly aligned, all containing the same 30,000 items. This suggests a robust data pipeline.

Tool callrun_shell_commandCheck alignment between catalog, targets, and candidate npz files Returned

Tool call · Input +01:23

{
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nwith np.load(data_dir / 'validation_eligible_candidates.npz') as val_npz:\n    val_items = val_npz['item_ids']\n    val_uids = val_npz['uids']\nwith np.load(data_dir / 'test_eligible_candidates.npz') as test_npz:\n    test_items = test_npz['item_ids']\n    test_uids = test_npz['uids']\n\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\n\nprint('Catalog item_ids match val item_ids:', np.array_equal(cat.item_id.to_numpy(), val_items))\nprint('Val item_ids match test item_ids:', np.array_equal(val_items, test_items))\nprint('Targets uids match val uids:', np.array_equal(targets.uid.to_numpy(), val_uids))\nprint('Val uids match test uids:', np.array_equal(val_uids, test_uids))\n\"",
  "description": "Check alignment between catalog, targets, and candidate npz files"
}

Tool response +01:25

<untrusted_context>
Output: Catalog item_ids match val item_ids: True
Val item_ids match test item_ids: True
Targets uids match val uids: True
Val uids match test uids: True
Process Group PGID: 1712
</untrusted_context>

Compute baseline score of popularity ranking

+01:30

Recorded reasoning summary 30 words

Confirming Data Alignment

It seems the user lists across target_users.parquet, validation_eligible_candidates.npz, and test_eligible_candidates.npz are identical, with precisely 8,456 users in the same sequence. This consistency extends to candidate_catalog.parquet as well.

Tool callrun_shell_commandCompute baseline score of popularity ranking Returned

Tool call · Input +01:30

{
  "description": "Compute baseline score of popularity ranking",
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport json\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\n\n# For each target user, select top 100 eligible items based on candidate catalog order\ncatalog_items = cat.item_id.to_numpy()\nrecords = []\nfor r, uid in enumerate(targets.uid):\n    mask = np.unpackbits(eligibility.packed[r], count=len(catalog_items), bitorder='little')\n    eligible_items = catalog_items[mask == 1]\n    top_100 = eligible_items[:100]\n    for rank, item in enumerate(top_100, 1):\n        records.append((uid, item, rank))\n\npop_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(pop_df, target_set)\nranked = ranked_items(validated)\ngraded = validation_truth(eligibility, target_set)\nprint('Popularity baseline score:', score(ranked, graded))\n\""
}

Tool response +01:32

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 6, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1729
</untrusted_context>

Compute baseline score of popularity ranking with PYTHONPATH=/task

+01:36

Tool callrun_shell_commandCompute baseline score of popularity ranking with PYTHONPATH=/task Returned

Tool call · Input +01:36

{
  "description": "Compute baseline score of popularity ranking with PYTHONPATH=/task",
  "command": "PYTHONPATH=/task python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport json\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\n\ncatalog_items = cat.item_id.to_numpy()\nrecords = []\nfor r, uid in enumerate(targets.uid):\n    mask = np.unpackbits(eligibility.packed[r], count=len(catalog_items), bitorder='little')\n    eligible_items = catalog_items[mask == 1]\n    top_100 = eligible_items[:100]\n    for rank, item in enumerate(top_100, 1):\n        records.append((uid, item, rank))\n\npop_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(pop_df, target_set)\nranked = ranked_items(validated)\ngraded = validation_truth(eligibility, target_set)\nprint('Popularity baseline score:', score(ranked, graded))\n\""
}

Tool response +01:39

<untrusted_context>
Output: Popularity baseline score: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1746
</untrusted_context>

list_directory command

+01:44

Recorded reasoning summary 38 words

Understanding the Requirements

I'm currently focusing on deeply understanding the problem and its requirements, particularly in relation to building a two-tower neural network specifically for CPU implementation. My initial benchmark with popularity baseline showed an NDCG@10 of 0.010710.

Recorded reasoning summary 69 words

Developing the Architecture

I'm now shifting focus to designing the core architecture for the "Made for You" shelf, specifically a two-tower model in PyTorch. The key challenge is ensuring scalability for millions of users and items, avoiding global interaction matrices. The architecture must support offline item embedding computation and real-time user embedding generation, with the relevance score being their dot product. User representations will be built from interaction history.

Tool calllist_directoryTool call Returned

Tool call · Input +01:44

{
  "dir_path": "/app/solution"
}

Tool response +01:44

Directory /app/solution is empty.

Create initial valid fallback submission

+01:49

Recorded reasoning summary 26 words

Generating Initial Valid Submission

I'm focusing on creating the first functional submission artifact now, aiming to have a valid file in /app/solution/submission.parquet as the initial benchmark.

Tool callrun_shell_commandCreate initial valid fallback submission Returned

Tool call · Input +01:49

{
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\ncatalog_items = cat.item_id.to_numpy()\nrecords = []\nfor r, uid in enumerate(targets.uid):\n    mask = np.unpackbits(eligibility.packed[r], count=len(catalog_items), bitorder='little')\n    eligible_items = catalog_items[mask == 1]\n    top_100 = eligible_items[:100]\n    for rank, item in enumerate(top_100, 1):\n        records.append((uid, item, rank))\n\npop_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(pop_df, target_set)\n# Check all are eligible\nfor uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None):\n    if not eligibility.is_eligible(uid, item):\n        raise ValueError(f'Ineligible item {item} for user {uid}')\n\nPath('/app/solution').mkdir(parents=True, exist_ok=True)\nvalidated.to_parquet('/app/solution/submission.parquet')\nprint('Baseline submission written successfully! Row count:', len(validated))\n\"",
  "description": "Create initial valid fallback submission"
}

Tool response +01:53

<untrusted_context>
Output: Baseline submission written successfully! Row count: 845600
Process Group PGID: 1766
</untrusted_context>

Analyze interactions and catalog coverage of target users

+02:02

Recorded reasoning summary 46 words

Exploring Submission Status

I've confirmed the fallback submission is safely in place at /app/solution/submission.parquet and meets all necessary schema and eligibility requirements. My focus now shifts to a thorough examination of the problem's core requirements, the intended architecture, the available data, and the overall design principles.

Recorded reasoning summary 37 words

Refining User Tower Input

I'm re-evaluating how the user tower should process interaction history. The key is to capture user preferences effectively without needing a global interaction matrix, aligning with the production system's constraints and scaling requirements.

Recorded reasoning summary 43 words

Analyzing Data Structures

I'm digging into the provided data structures now. Specifically, I'm looking at the different event types and their timestamps, aiming to understand the training and validation windows better. My goal is to efficiently process these parquet files for model development.

Recorded reasoning summary 47 words

Validating Ranking Metrics

I'm clarifying the distinction between validation and hidden-test event usage. For validation, I need to ensure val_* events aren't used as features when measuring ranking quality, while still allowing them for final ranker refitting. My focus is on correctly implementing the graded NDCG@10 metric.

Tool callrun_shell_commandAnalyze interactions and catalog coverage of target users Returned

Tool call · Input +02:02

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\nprint('Catalog items count:', len(catalog_items))\n\nval_likes = pd.read_parquet(data_dir / 'val_likes.parquet')\nval_listens = pd.read_parquet(data_dir / 'val_listens.parquet')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\n\nval_org_likes = val_likes[(val_likes.uid.isin(target_set)) & (val_likes.is_organic == 1)]\nval_org_listens_80 = val_listens[(val_listens.uid.isin(target_set)) & (val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]\n\nprint('Val organic likes:', len(val_org_likes), 'unique items:', val_org_likes.item_id.nunique())\nprint('Val organic likes in catalog:', val_org_likes.item_id.isin(catalog_items).mean())\nprint('Val organic listens >=80%:', len(val_org_listens_80), 'unique items:', val_org_listens_80.item_id.nunique())\nprint('Val organic listens in catalog:', val_org_listens_80.item_id.isin(catalog_items).mean())\n\n# Check how many interactions target users have in train_likes and train_listens\ntrain_likes = pd.read_parquet(data_dir / 'train_likes.parquet')\ntarget_likes = train_likes[train_likes.uid.isin(target_set)]\nprint('Target users with at least 1 like in train_likes:', target_likes.uid.nunique())\n\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('Validation preference state target users:', val_pref.uid.nunique())\nprint('Validation preference state in catalog:', val_pref.item_id.isin(catalog_items).mean())\n\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\nprint('Test preference state target users:', test_pref.uid.nunique())\nprint('Test preference state in catalog:', test_pref.item_id.isin(catalog_items).mean())\n\"",
  "description": "Analyze interactions and catalog coverage of target users"
}

Tool response +02:04

<untrusted_context>
Output: Catalog items count: 30000
Val organic likes: 20702 unique items: 13921
Val organic likes in catalog: 0.5026567481402763
Val organic listens >=80%: 421615 unique items: 86668
Val organic listens in catalog: 0.6850515280528444
Target users with at least 1 like in train_likes: 7071
Validation preference state target users: 7038
Validation preference state in catalog: 1.0
Test preference state target users: 7092
Test preference state in catalog: 1.0
Process Group PGID: 1783
</untrusted_context>

Distribution of liked and disliked items in validation preference state

+02:11

Recorded reasoning summary 39 words

Confirming Data Alignment

It seems all items within the validation preference set are accounted for in the candidate catalog, and this holds true for the test set as well. Next, I'll examine the contents of this validation preference state.

Tool callrun_shell_commandDistribution of liked and disliked items in validation preference state Returned

Tool call · Input +02:11

{
  "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('Per user count stats in val_pref:')\ncounts = val_pref.groupby('uid').size()\nprint(counts.describe())\n\nliked = val_pref[val_pref.state == 'liked'].groupby('uid').size()\nprint('\\nLiked count stats:')\nprint(liked.describe())\n\ndisliked = val_pref[val_pref.state == 'disliked'].groupby('uid').size()\nprint('\\nDisliked count stats:')\nprint(disliked.describe())\n\"",
  "description": "Distribution of liked and disliked items in validation preference state"
}

Tool response +02:13

<untrusted_context>
Output: Per user count stats in val_pref:
count    7038.000000
mean       66.655868
std       108.686351
min         1.000000
25%         7.000000
50%        30.000000
75%        81.000000
max      1847.000000
dtype: float64

Liked count stats:
count    6769.000000
mean       61.816369
std        97.987644
min         1.000000
25%         7.000000
50%        28.000000
75%        75.000000
max      1327.000000
dtype: float64

Disliked count stats:
count    4277.000000
mean       11.851531
std        54.838496
min         1.000000
25%         1.000000
50%         3.000000
75%         7.000000
max      1703.000000
dtype: float64
Process Group PGID: 1803
</untrusted_context>

Check eligibility of liked and disliked tracks in validation preference state

+02:19

Recorded reasoning summary 43 words

Checking Train Listens

I'm now focusing on the train_listens dataset. Specifically, I'm investigating the number of listens attributed to each target user within this data. This follows my prior observation that a significant majority of the dataset's rows correspond to these target users.

Recorded reasoning summary 48 words

Analyzing Listen Distributions

I'm now analyzing the timestamps of listens, particularly focusing on the distribution relative to the cutoff. I'm comparing recent listens to historical ones, and I'm interested in how many of these involve repeat items versus new ones. This is crucial for understanding candidate eligibility's impact.

Tool callrun_shell_commandCheck eligibility of liked and disliked tracks in validation preference state Returned

Tool call · Input +02:19

{
  "description": "Check eligibility of liked and disliked tracks in validation preference state",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\n# Check if liked items in val_pref are eligible in validation_eligible_candidates\nprint('Checking if liked/disliked items in val_pref are eligible:')\nliked_sample = val_pref[val_pref.state == 'liked'].head(1000)\ndisliked_sample = val_pref[val_pref.state == 'disliked'].head(1000)\n\nliked_eligible = [eligibility.is_eligible(uid, item) for uid, item in liked_sample[['uid', 'item_id']].itertuples(index=False, name=None)]\ndisliked_eligible = [eligibility.is_eligible(uid, item) for uid, item in disliked_sample[['uid', 'item_id']].itertuples(index=False, name=None)]\n\nprint('Liked sample eligible %:', np.mean(liked_eligible))\nprint('Disliked sample eligible %:', np.mean(disliked_eligible))\n\n# Check in general what items are INELIGIBLE\n# Let's count how many total items are ineligible per user\n\""
}

Tool response +02:21

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 5, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1820
</untrusted_context>

Check eligibility of liked and disliked tracks with PYTHONPATH=/task

+02:32

Tool callrun_shell_commandCheck eligibility of liked and disliked tracks with PYTHONPATH=/task Returned

Tool call · Input +02:32

{
  "description": "Check eligibility of liked and disliked tracks with PYTHONPATH=/task",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\nliked_sample = val_pref[val_pref.state == 'liked'].head(1000)\ndisliked_sample = val_pref[val_pref.state == 'disliked'].head(1000)\n\nliked_eligible = [eligibility.is_eligible(uid, item) for uid, item in liked_sample[['uid', 'item_id']].itertuples(index=False, name=None)]\ndisliked_eligible = [eligibility.is_eligible(uid, item) for uid, item in disliked_sample[['uid', 'item_id']].itertuples(index=False, name=None)]\n\nprint('Liked sample eligible %:', np.mean(liked_eligible))\nprint('Disliked sample eligible %:', np.mean(disliked_eligible))\n\""
}

Tool response +02:34

<untrusted_context>
Output: Liked sample eligible %: 0.0
Disliked sample eligible %: 0.0
Process Group PGID: 1831
</untrusted_context>

Analyze ineligible items composition for user 100

+02:41

Recorded reasoning summary 35 words

Exploring Candidate Eligibility

My analysis shows that candidate eligibility has already successfully filtered out liked and disliked tracks. I'm now investigating whether this mechanism also excludes items the user has recently or previously listened to.

Tool callrun_shell_commandAnalyze ineligible items composition for user 100 Returned

Tool call · Input +02:41

{
  "description": "Analyze ineligible items composition for user 100",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\n\n# Let's inspect user 100\nuid = 100\nrow = eligibility.uid_row[uid]\nmask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\nineligible_items = set(eligibility.item_ids[mask == 0])\nprint(f'User {uid} has {len(ineligible_items)} ineligible items out of {len(eligibility.item_ids)}')\n\n# Check user 100 preference state\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\npref_items = set(val_pref[val_pref.uid == uid].item_id)\nprint(f'User {uid} has {len(pref_items)} items in pref_state. Are all in ineligible? {pref_items.issubset(ineligible_items)}')\n\n# What else is in ineligible_items?\n# Let's check user 100 listens\nlistens = pd.read_parquet(data_dir / 'train_listens.parquet', filters=[('uid', '==', uid)])\nlisten_items = set(listens.item_id)\nprint(f'User {uid} listened to {len(listen_items)} unique items. Intersection with catalog: {len(listen_items.intersection(set(eligibility.item_ids)))}')\nprint(f'User {uid} listen items in ineligible_items: {len(listen_items.intersection(ineligible_items))}')\n\n# Check recent listens vs older listens\nlistens_last_30d = listens[listens.timestamp > (24786800 - 30 * 86400)]\nitems_30d = set(listens_last_30d.item_id)\nprint(f'User {uid} listened in last 30d: {len(items_30d)} items. In ineligible: {len(items_30d.intersection(ineligible_items))}')\n\n# Check what the remaining ineligible items are\nother_ineligible = ineligible_items - pref_items\nprint(f'User {uid} other ineligible items: {len(other_ineligible)}')\nprint(f'How many of other ineligible were in listens? {len(other_ineligible.intersection(listen_items))}')\n\""
}

Tool response +02:42

<untrusted_context>
Output: User 100 has 682 ineligible items out of 30000
User 100 has 15 items in pref_state. Are all in ineligible? True
User 100 listened to 830 unique items. Intersection with catalog: 682
User 100 listen items in ineligible_items: 682
User 100 listened in last 30d: 325 items. In ineligible: 284
User 100 other ineligible items: 667
How many of other ineligible were in listens? 667
Process Group PGID: 1849
</untrusted_context>

Verify eligibility filtering across sample users

+02:52

Recorded reasoning summary 38 words

Confirming user engagement patterns

I'm seeing a strong correlation: all ineligible items were previously interacted with by the user. This suggests a discovery mechanism at play, and I'm proceeding to validate this hypothesis across a broader user base.

Tool callrun_shell_commandVerify eligibility filtering across sample users Returned

Tool call · Input +02:52

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\n\ntest_uids = targets.uid.iloc[:20].tolist()\nall_ineligible_are_prior = []\n\nfor uid in test_uids:\n    row = eligibility.uid_row[uid]\n    mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder='little')\n    ineligible = set(eligibility.item_ids[mask == 0])\n    listens = pd.read_parquet(data_dir / 'train_listens.parquet', filters=[('uid', '==', uid)], columns=['item_id'])\n    prior_items = set(listens.item_id)\n    diff = ineligible - prior_items\n    all_ineligible_are_prior.append(len(diff) == 0)\n\nprint('All ineligible items were prior interactions for 20 users:', all(all_ineligible_are_prior))\n\"",
  "description": "Verify eligibility filtering across sample users"
}

Tool response +02:54

<untrusted_context>
Output: All ineligible items were prior interactions for 20 users: False
Process Group PGID: 1875
</untrusted_context>

Benchmark read time of recent train_listens

+03:17

Recorded reasoning summary 41 words

Focusing on Candidate Eligibility

I've confirmed that candidate eligibility is already determined and readily available in the validation_eligible_candidates.npz and test_eligible_candidates.npz files, as per the prompt's guidance. My current focus is on efficiently retrieving these precomputed masks for model training and evaluation.

Recorded reasoning summary 65 words

Refining Scoring Logic

I'm now focusing on how to precisely implement the ranking and scoring mechanisms. Specifically, I need to ensure that when I rank candidates, I only consider those where the mask is equal to 1. Furthermore, when selecting the top_100 for scoring, these must strictly come from the eligible items. My attention is also directed towards understanding the two-tower model's requirements in detail.

Recorded reasoning summary 47 words

Integrating Two-Tower Dynamics

I'm now working to integrate the specifics of the two-tower model. I've clarified that the item tower generates precomputable item vectors, while the user tower derives user vectors from interaction history. I'm also considering vector normalization and the precise inputs for the user tower.

Recorded reasoning summary 69 words

Constructing User Representations

I'm now deeply engaged in feature engineering for the user tower. My primary objective is to construct robust user representations by leveraging their entire interaction history. I'm analyzing the available data, including liked, disliked, and listened items, and considering various strategies like strong organic listens, positive listens, and recency to build these representations. My focus is on how to effectively pool item embeddings from this history.

Recorded reasoning summary 50 words

Aggregating Item Embeddings

I'm now focused on how to best aggregate item embeddings from a user's interaction history. I'm exploring various pooling strategies like weighted averages or attentive pooling, considering positive signals like likes and listens, and potentially incorporating negative signals. My aim is to construct a robust user representation.

Recorded reasoning summary 49 words

Refining Item Tower Architecture

I'm now scrutinizing the item tower's implementation, specifically evaluating whether a simple nn.Embedding lookup suffices or if adding an MLP after it provides a more nuanced representation. The prompt specifies separate trainable towers, and I'm weighing the complexity against potential performance gains for item embeddings.

Recorded reasoning summary 51 words

Refining Training Strategy

I'm now critically re-evaluating the training approach to ensure it scales effectively for a large production system. My focus is on strictly adhering to the constraint of not constructing or decomposing a global user-item interaction matrix, avoiding methods like SVD or ALS that wouldn't be feasible at scale.

Recorded reasoning summary 46 words

Evaluating Training Methodology

I am confirming that the training methodology must be scalable, explicitly avoiding the construction or decomposition of a global user-item interaction matrix. My focus is on implementing standard industry practices for two-tower models, such as mini-batch training with in-batch negatives or contrastive losses.

Recorded reasoning summary 41 words

Confirming Data Handling

I'm confirming that the validation_truth function, as presented, correctly filters strong organic listens and organic likes based on user targets and eligibility. My current task is to ensure this logic is accurately integrated into the overall evaluation pipeline.

Recorded reasoning summary 46 words

Analyzing Evaluation Metrics

I am now analyzing the NDCG@10 calculation, understanding how grades for organic likes (3) and strong organic listens (1) translate to relevance gains. My focus is on the discount applied at each rank and the user averaging for users with eligible relevant items.

Recorded reasoning summary 58 words

Adjusting Time-Based Splitting

I'm now adjusting the data splitting strategy based on timestamps. I've confirmed the end of the training set and the start of the validation set with a 30-minute gap. My current focus is on understanding how validation events should be used as features versus outcomes for ranking and ensuring this aligns with the hidden-test evaluation.

Tool callrun_shell_commandBenchmark read time of recent train_listens Returned

Tool call · Input +03:17

{
  "command": "python3 -c \"\nimport time\nimport pandas as pd\n\nt0 = time.time()\n# Check read time of train_likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint(f'Read train_likes: {len(likes)} rows in {time.time() - t0:.2f}s')\n\n# Check read time of train_listens with columns / filter\nt0 = time.time()\n# Timestamp for 60 days before cutoff: 24786800 - 60*86400 = 19602800\ncutoff_60d = 24786800 - 60 * 86400\nrecent_listens = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_60d)]\n)\nprint(f'Read train_listens last 60d: {len(recent_listens)} rows in {time.time() - t0:.2f}s')\n\"",
  "description": "Benchmark read time of recent train_listens"
}

Tool response +03:19

<untrusted_context>
Output: Read train_likes: 814831 rows in 0.07s
Read train_listens last 60d: 11718758 rows in 0.35s
Process Group PGID: 1896
</untrusted_context>

Analyze distribution of positive pairs in validation ground truth

+03:27

Recorded reasoning summary 52 words

Analyzing Data Structures

I'm impressed by PyArrow's speed, processing 11.7 million rows in just 0.35 seconds. My current focus is on understanding positive events in the training data that resemble the target task. I'm also looking into how relevance is defined within the validation window, specifically for Grade 3, considering organic similarities.

Tool callrun_shell_commandAnalyze distribution of positive pairs in validation ground truth Returned

Tool call · Input +03:27

{
  "command": "PYTHONPATH=/task python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, target_set)\n\ntotal_pairs = sum(len(items) for items in graded.values())\ngrade_counts = {}\nfor items in graded.values():\n    for g in items.values():\n        grade_counts[g] = grade_counts.get(g, 0) + 1\n\nprint(f'Scored users: {len(graded)} / {len(target_set)}')\nprint(f'Total positive pairs: {total_pairs}')\nprint(f'Grade counts: {grade_counts}')\n\"",
  "description": "Analyze distribution of positive pairs in validation ground truth"
}

Tool response +03:29

<untrusted_context>
Output: Scored users: 4263 / 8456
Total positive pairs: 37769
Grade counts: {1: 33352, 3: 4417}
Process Group PGID: 1917
</untrusted_context>

Check catalog overlap for items in user history

+03:52

Recorded reasoning summary 57 words

Exploring User Music Selection

I'm currently analyzing user listening habits. My focus is on understanding what drives organic track selection, with 37,769 positive pairs identified across 4,263 users in the last week. This suggests an average of 8.8 positively engaged tracks per user, prompting a deeper dive into the core question of track relevance and organic discovery.

Recorded reasoning summary 47 words

Understanding Track Discovery

My focus is on how to best recommend NEW tracks. I'm exploring if users will engage with a song they've never encountered, relying on collaborative filtering and similarity to their existing preferences. This involves understanding the two-tower model architecture to retrieve relevant candidates efficiently.

Recorded reasoning summary 47 words

Generating Item Vectors

I am now focusing on generating item vectors, independent of user interactions. The item tower, implemented in PyTorch, will compute these $v_i$ vectors for each item, enabling offline precomputation of the entire catalog's item representations. This will allow for efficient retrieval of candidate items.

Recorded reasoning summary 48 words

Refining User Representation

I'm now focusing on the user tower, specifically how to represent a user's interaction history. This involves considering liked, listened, and disliked items, and aggregating their representations through methods like weighted sums or attention before passing through an MLP to derive the final user vector.

Recorded reasoning summary 47 words

Sharing Item Embeddings

I'm now considering whether to share item embeddings between the user and item towers. This seems feasible and is a common practice in similar architectures. My current focus is on confirming if this sharing maintains the independent computability of the item tower's output vectors.

Tool callrun_shell_commandCheck catalog overlap for items in user history Returned

Tool call · Input +03:52

{
  "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\n\ntrain_likes = pd.read_parquet(data_dir / 'train_likes.parquet')\nprint('Train likes unique items:', train_likes.item_id.nunique(), 'in catalog:', train_likes.item_id.isin(catalog_items).mean())\n\n# Check recent listens (last 60 days)\ncutoff_60d = 24786800 - 60 * 86400\nrecent_listens = pd.read_parquet(\n    data_dir / 'train_listens.parquet',\n    columns=['item_id'],\n    filters=[('timestamp', '>=', cutoff_60d)]\n)\nprint('Recent listens unique items:', recent_listens.item_id.nunique(), 'in catalog:', recent_listens.item_id.isin(catalog_items).mean())\n\"",
  "description": "Check catalog overlap for items in user history"
}

Tool response +03:54

<untrusted_context>
Output: Train likes unique items: 172303 in catalog: 0.6809068383505291
Recent listens unique items: 474085 in catalog: 0.7182835416517689
Process Group PGID: 1937
</untrusted_context>

Check target user interactions in candidate catalog

+04:02

Recorded reasoning summary 55 words

Focusing on Candidate Catalog

I'm noticing a significant pattern: roughly 70% of user interactions occur within the initial 30,000 candidates. This leads me to consider a potential strategy. Perhaps assigning an Out-Of-Vocabulary (OOV) index, say index 30,000, or a dedicated embedding table could effectively handle both the catalog items (0-29,999) and any new, OOV items.

Tool callrun_shell_commandCheck target user interactions in candidate catalog Returned

Tool call · Input +04:02

{
  "description": "Check target user interactions in candidate catalog",
  "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\n\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Check user preference state\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('Val pref items in catalog:', val_pref.item_id.isin(catalog_items).all())\n# Val pref has 418,435 liked and 50,689 disliked, and 100% of them are in candidate catalog!\n\n# Check target users in train_likes\ntrain_likes = pd.read_parquet(data_dir / 'train_likes.parquet')\ntarget_likes = train_likes[train_likes.uid.isin(target_set)]\nprint('Target likes total:', len(target_likes), 'in catalog:', target_likes.item_id.isin(catalog_items).mean())\n\n# Check recent listens for target users\ncutoff_90d = 24786800 - 90 * 86400\nrecent_listens = pd.read_parquet(\n    data_dir / 'train_listens.parquet',\n    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_90d)]\n)\ntarget_recent = recent_listens[recent_listens.uid.isin(target_set)]\nprint('Target recent listens total:', len(target_recent), 'in catalog:', target_recent.item_id.isin(catalog_items).mean())\n\""
}

Tool response +04:06

<untrusted_context>
Output: Val pref items in catalog: True
Target likes total: 690968 in catalog: 0.6843862523300644
Target recent listens total: 16743307 in catalog: 0.722659328888851
Process Group PGID: 1964
</untrusted_context>

Analyze validation positive item characteristics

+04:24

Recorded reasoning summary 44 words

Analyzing User Engagement

My focus has shifted to quantifying user engagement within the candidate catalog. I've observed that our target users collectively generated approximately 12 million catalog listens in the past 90 days, stemming from about 8,456 individuals, averaging roughly 1,400 listens per user.

Recorded reasoning summary 45 words

Quantifying User Interaction

I'm now delving into the specifics of user interactions, noting around 472,000 likes within the catalog from our target users, averaging about 56 likes each. Additionally, validation preferences show a significant number of liked and disliked tracks, all within the candidate catalog.

Recorded reasoning summary 44 words

Training Model Inputs

I'm now considering the specific data I'll use for training. The two-tower architecture requires user history as input, alongside positive examples such as organically liked or significantly completed items. This refined understanding will guide my data preparation for stage 2 refitting.

Recorded reasoning summary 47 words

Refining Loss Functions

I'm now evaluating specific loss functions for the two-tower model. The in-batch negative cross-entropy loss appears promising, using both positive interactions and other items in the batch as negatives. I'm also considering augmenting this with randomly or popularity-sampled negative items to mitigate potential bias.

Recorded reasoning summary 57 words

Weighting Training Data

I'm considering how to best incorporate varying levels of user preference, like organic likes versus prolonged listens, into the loss function. Given that organic likes are significantly more valuable in our evaluation metric, I'm exploring weighting their contribution to the training loss much higher. This will ensure the model prioritizes learning from stronger signals.

Recorded reasoning summary 56 words

Constructing Training Pairs

I'm now focusing on how to form our training pairs $(H_u, i^+)$. The user history ($H_u$) will encompass interactions prior to a specific timestamp, while positive items ($i^+$) will be derived from user likes or completed listens within a defined recent window, like the last 7 to 28 days of the training set.

Recorded reasoning summary 50 words

Defining User History

I am re-evaluating the definition of user history for training pairs. Instead of fixed time windows, I am considering using the complete history up to a point, or focusing on explicit preferences and recent items, with the goal of generating simpler user-item interaction pairs for the model.

Tool callrun_shell_commandAnalyze validation positive item characteristics Returned

Tool call · Input +04:24

{
  "description": "Analyze validation positive item characteristics",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Let's inspect what happens in the validation set\nval_likes = pd.read_parquet(data_dir / 'val_likes.parquet')\nval_listens = pd.read_parquet(data_dir / 'val_listens.parquet')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\n\n# Validation positive items\nval_pos_listens = val_listens[(val_listens.uid.isin(target_set)) & (val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]\nval_pos_likes = val_likes[(val_likes.uid.isin(target_set)) & (val_likes.is_organic == 1)]\n\nprint('Unique items organically liked in val:', val_pos_likes.item_id.nunique())\nprint('Unique items organically listened in val:', val_pos_listens.item_id.nunique())\n\n# Check how many unique items in candidate catalog appear in validation positives\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncat_items = set(cat.item_id)\nval_pos_items = (set(val_pos_likes.item_id) | set(val_pos_listens.item_id)) & cat_items\nprint('Validation positive catalog items:', len(val_pos_items), 'out of 30,000')\n\n# Check distribution of popularity rank of these positive items\ncat_pos = cat[cat.item_id.isin(val_pos_items)]\nprint('Popularity rank stats of val positive catalog items:')\nprint(cat_pos['popularity_rank'].describe())\n\""
}

Tool response +04:26

<untrusted_context>
Output: Unique items organically liked in val: 13921
Unique items organically listened in val: 86668
Validation positive catalog items: 25624 out of 30,000
Popularity rank stats of val positive catalog items:
count    25624.000000
mean     13972.732204
std       8546.496269
min          1.000000
25%       6531.750000
50%      13485.500000
75%      21101.250000
max      30000.000000
Name: popularity_rank, dtype: float64
Process Group PGID: 1985
</untrusted_context>

Analyze interaction counts across recent time windows

+04:45

Recorded reasoning summary 65 words

Analyzing Validation Results

My current focus is on understanding the validation set performance. It appears a significant majority of catalog items, over 25,000, are flagged as positive. Even with a baseline popularity recommender, the NDCG@10 is 0.01071, indicating room for improvement. The wide distribution of these positive items across the catalog, with a mean popularity rank of nearly 14,000, is a key factor I'm considering.

Recorded reasoning summary 56 words

Refining Recommendation Strategy

I'm integrating the core idea that users engage with diverse content tailored to their preferences. This emphasizes the need for personalized recommendations. My current thoughts are leaning towards ensuring my model can effectively handle the vast scale of users and items without explicit interaction matrix construction, keeping latency requirements in mind for production.

Recorded reasoning summary 63 words

Designing User and Item Towers

My current thoughts are focused on the architectural requirements. I need to design separate PyTorch neural network towers for users and items. Crucially, the user tower will generate its representation from interaction history, not a simple ID lookup. The final relevance score will be the dot product of these independently precomputable vectors, and candidate eligibility is a given.

Recorded reasoning summary 55 words

Building Neural Network Towers

I'm now focusing on the specific implementation details for my PyTorch towers. The UserTower will process interaction history to create a user vector, while the ItemTower will generate item vectors that can be precomputed offline and stored for efficient retrieval. This architecture is designed to avoid a large, explicit interaction matrix.

Recorded reasoning summary 50 words

Processing User History

My current thoughts are on how the user tower will process interaction history. I'm analyzing what constitutes a user's history—likes, dislikes, and listens, with consideration for listen quality (completion, recency, frequency). The next step is to compare standard pooling architectures like Mean/Weighted Pooling combined with an MLP.

Recorded reasoning summary 51 words

Developing User Representation

I'm exploring how to represent users from their interaction history. Currently, I'm considering pooling embeddings of positive items, potentially weighted by recency or interaction type. I'm also evaluating whether multi-head attention or attentive pooling could offer a more nuanced representation than standard pooling before feeding into an MLP.

Recorded reasoning summary 52 words

Optimizing User Embedding

I'm prioritizing efficiency for user representation. Self-attention over long histories ($O(L^2)$) is too slow; mean/weighted pooling ($O(L \cdot D)$) is much faster and heavily optimized in PyTorch's EmbeddingBag. I plan to pool bags of liked, listened, and disliked items, concatenate these embeddings, and then pass them through an MLP.

Recorded reasoning summary 45 words

Sharing Item Embeddings

I'm considering whether the UserTower and ItemTower can share the same base item embedding table. This seems efficient, as the user tower will embed historical items, and the item tower will embed candidate items, potentially allowing for weight sharing to avoid redundancy.

Recorded reasoning summary 46 words

Sharing Item Embeddings Strategy

I'm weighing the benefits of sharing item embeddings between the user and item towers. While sharing can propagate learning signals efficiently, I'm also considering separate embedding tables with distinct tower MLPs to maintain architectural integrity as specified by separate trainable PyTorch modules.

Tool callrun_shell_commandAnalyze interaction counts across recent time windows Returned

Tool call · Input +04:45

{
  "description": "Analyze interaction counts across recent time windows",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's inspect the timestamps in train_listens and train_likes\ntrain_end = 24786800\nday_sec = 86400\nweek_sec = 7 * day_sec\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes timestamp range:', likes.timestamp.min(), likes.timestamp.max())\nprint('Likes in last 7 days:', (likes.timestamp >= train_end - week_sec).sum())\nprint('Likes in last 14 days:', (likes.timestamp >= train_end - 2*week_sec).sum())\nprint('Likes in last 28 days:', (likes.timestamp >= train_end - 4*week_sec).sum())\nprint('Likes in last 60 days:', (likes.timestamp >= train_end - 60*day_sec).sum())\n\n# Sample listens\nlistens_last_14d = pd.read_parquet(\n    '/task/data/train_listens.parquet',\n    columns=['timestamp', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', train_end - 14*day_sec)]\n)\nprint('\\nListens in last 14 days:', len(listens_last_14d))\nprint('Organic listens >= 80% in last 14d:', ((listens_last_14d.is_organic == 1) & (listens_last_14d.played_ratio_pct >= 80)).sum())\nprint('Organic listens >= 80% in last 7d:', ((listens_last_14d.timestamp >= train_end - week_sec) & (listens_last_14d.is_organic == 1) & (listens_last_14d.played_ratio_pct >= 80)).sum())\n\""
}

Tool response +04:47

<untrusted_context>
Output: Likes timestamp range: 80 24786790
Likes in last 7 days: 30156
Likes in last 14 days: 57467
Likes in last 28 days: 113399
Likes in last 60 days: 231488

Listens in last 14 days: 2920227
Organic listens >= 80% in last 14d: 834725
Organic listens >= 80% in last 7d: 428612
Process Group PGID: 2005
</untrusted_context>

update_topic command

+05:06

Recorded reasoning summary 49 words

Analyzing Engagement Metrics

My current focus is on interpreting recent training data, noting significant increases in both likes and organic listens exceeding 80% in the past week and fortnight. I'm now considering the potential impact of creating a training dataset composed of user-positive pairs to further refine these outcomes.

Recorded reasoning summary 50 words

Refining Training Strategies

I'm considering how to best leverage my understanding of two-tower models. Specifically, I'm exploring the implications of using recent organic listens and likes as positive training pairs, while carefully navigating the potential for model shortcuts by ensuring candidate items at inference are genuinely novel to the user.

Recorded reasoning summary 49 words

Preventing Overfitting Traps

I've realized a critical insight: if my user tower simply memorizes past interactions, it will fail because those items are already filtered out at inference. I'm now exploring training strategies like leaving-one-out or masked item prediction to ensure my model learns genuine preferences, not just recall.

Recorded reasoning summary 50 words

Understanding Collaborative Filtering

I've confirmed that my user tower's core function is to learn collaborative filtering principles. It predicts new items based on existing preferences, necessitating genuine learning of relationships rather than simple memorization. This ensures it identifies truly undiscovered tracks aligned with user taste, mirroring established two-tower recommendation architectures.

Recorded reasoning summary 41 words

Validating Temporal Splits

I've confirmed that our current validation and training data splits align with standard temporal considerations. Critically, the evaluation script provided can directly assess any validation ranking, allowing us to proceed with testing model performance against the ground truth.

Tool callupdate_topicTool call Returned

Tool call · Input +05:06

{
  "summary": "We confirmed the candidate catalog size (30,000 items), target users (8,456 users), and established an initial valid fallback submission that achieves NDCG@10 = 0.01071 with popularity. We verified that candidate eligibility masks out prior interactions, making this a true discovery task. We are now formulating the two-tower neural network architecture (UserTower and ItemTower) and designing the training and evaluation pipeline using PyTorch on CPU.",
  "strategic_intent": "Analyze user interaction patterns, formulate the two-tower model architecture, and design the validation experiment pipeline.",
  "title": "Model Architecture & Training Design"
}

Tool response +05:06

Current topic: "Model Architecture & Training Design"
Topic summary: We confirmed the candidate catalog size (30,000 items), target users (8,456 users), and established an initial valid fallback submission that achieves NDCG@10 = 0.01071 with popularity. We verified that candidate eligibility masks out prior interactions, making this a true discovery task. We are now formulating the two-tower neural network architecture (UserTower and ItemTower) and designing the training and evaluation pipeline using PyTorch on CPU.

Strategic Intent: Analyze user interaction patterns, formulate the two-tower model architecture, and design the validation experiment pipeline.

Test weighted pooling implementation in PyTorch

+05:38

Recorded reasoning summary 38 words

Exploring Two-Tower Design

I'm currently investigating the core requirements for effective two-tower models within this dataset. My focus is on understanding how to best engineer independent user and item vectors that, through their dot product, accurately capture relevance.

Recorded reasoning summary 445 words

Designing Two-Tower Networks

I'm focusing on the implementation details for our two-tower architecture. Specifically, I'm setting up PyTorch neural networks for both user and item towers, ensuring user representations are built from interaction history, not just IDs. The plan is to avoid a full interaction matrix for scalability, opting for offline candidate embeddings and efficient in-memory nearest-neighbor indexing.

2. User Tower Design

The user tower needs to generate a fixed-size embedding for each user based on their interaction history. This means processing sequences of item interactions, which may include item features.

To start, I'll construct a minimal user tower. This will likely involve:

An embedding layer for item IDs within the interaction history.
Potentially, an embedding layer for item features if we decide to incorporate them.
A recurrent neural network (RNN) like an LSTM or GRU, or a Transformer encoder, to aggregate the sequence of item embeddings into a user representation.
A final dense layer to project the aggregated representation into the desired embedding dimension.

Crucially, I must consider how to handle variable-length interaction histories. Padding and masking will be essential for batching.

3. Item Tower Design

The item tower needs to generate a fixed-size embedding for each item. This should be relatively straightforward if we have item features.

I'll start with a simple item tower:

An embedding layer for item IDs.
If item features are available (e.g., category, text description, image embeddings), I'll include embedding layers or other appropriate processing for them.
A dense layer to project the combined features into the desired item embedding dimension.

If items are represented solely by their IDs, then the item tower could simply be an embedding layer. However, it's generally more powerful to leverage item features if available.

4. Training Strategy Considerations

The training must be scalable. This implies that constructing the full user-item interaction matrix is not an option. This points towards using negative sampling or in-batch negatives during training.

5. Candidate Generation and Retrieval

Candidate item embeddings will be pre-computed and stored. A nearest-neighbor index (e.g., FAISS, Annoy) will be used for efficient retrieval. At inference time, a user embedding will be computed, and then the nearest neighbors in the item index will be found using maximum inner-product search.

Existing Summary (empty if none exists yet):

Designing Two-Tower Networks

Recorded reasoning summary 48 words

Processing User History

I'm refining how user interaction histories are handled. It appears item IDs align directly with our catalog, simplifying that mapping. I'm evaluating how to best represent non-catalog items and focusing on incorporating explicit likes and dislikes, alongside high-completion listens, as positive signals for user representations.

Recorded reasoning summary 43 words

Refining User Representation

I'm now detailing the user tower's input, focusing on how to translate interaction signals into embeddings. I'm exploring the inclusion of item IDs, item features, explicit likes/dislikes, and weighted signals like play frequency and recency to build robust user representations.

Recorded reasoning summary 38 words

Incorporating User Features

I'm exploring how to integrate additional user statistics and refine the input to the user tower beyond just pooled embeddings. The goal is to create richer representations, considering scenarios like users with no explicit dislikes.

Tool callrun_shell_commandTest weighted pooling implementation in PyTorch Returned

Tool call · Input +05:38

{
  "description": "Test weighted pooling implementation in PyTorch",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nembed_dim = 64\nemb = nn.Embedding(30001, embed_dim, padding_idx=30000)\n\n# Test batch of 256 users, each with up to 100 items\nitem_ids = torch.randint(0, 30000, (256, 100))\nweights = torch.rand(256, 100)\n\n# Mask out some items\nmask = torch.rand(256, 100) > 0.5\nitem_ids[~mask] = 30000\nweights[~mask] = 0.0\n\n# Weighted sum\nitems_emb = emb(item_ids) # (256, 100, 64)\nweighted = items_emb * weights.unsqueeze(-1)\nsummed = weighted.sum(dim=1) # (256, 64)\nnorm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\npooled = summed / norm\n\nprint('Pooled shape:', pooled.shape)\nprint('Compute took milliseconds!')\n\""
}

Tool response +05:41

<untrusted_context>
Output: Pooled shape: torch.Size([256, 64])
Compute took milliseconds!
Process Group PGID: 2034
</untrusted_context>

Inspect target user interaction distribution statistics

+05:45

Recorded reasoning summary 37 words

Refining interaction tracking

I'm focusing on optimizing how I capture your recent engagement. I'm considering keeping the last 100-200 interactions, or perhaps weighting them. This approach should efficiently retain your latest tastes while remaining performant for processing.

Tool callrun_shell_commandInspect target user interaction distribution statistics Returned

Tool call · Input +05:45

{
  "description": "Inspect target user interaction distribution statistics",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's inspect target users' number of likes and listens\ndata_dir = '/task/data'\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\nprint(targets.describe())\n\""
}

Tool response +05:47

<untrusted_context>
Output: uid  recent_positive_listens  history_positive_listens
count     8456.000000              8456.000000               8456.000000
mean    502289.108325              1251.076750               3236.440752
std     287953.395912              1414.340904               3697.575286
min        100.000000                20.000000                 20.000000
25%     254375.000000               281.000000                630.000000
50%     503850.000000               784.500000               1920.500000
75%     751725.000000              1706.250000               4494.250000
max    1000000.000000             19592.000000              26597.000000
Process Group PGID: 2047
</untrusted_context>

Count organic likes and listens across historical 7-day windows

+05:58

Recorded reasoning summary 60 words

Analyzing User Engagement Metrics

My recent focus has been on evaluating user engagement, specifically scrutinizing recent positive listen counts. It's encouraging to see every target user surpassing 20 positive listens within the last 90 days, with a median of 784. My next step involves deeply considering the optimal formulation of training samples and defining the composition of our training dataset.

Recorded reasoning summary 45 words

Formulating Training Samples

I'm currently exploring training strategies, focusing on a temporal backtest with a sliding window approach. My objective is to define distinct historical windows to generate training instances. Each window will utilize user history preceding the outcome period to predict future positive interactions.

Tool callrun_shell_commandCount organic likes and listens across historical 7-day windows Returned

Tool call · Input +05:58

{
  "description": "Count organic likes and listens across historical 7-day windows",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_end = 24786800\nday = 86400\nweek = 7 * day\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Let's inspect organic listens and likes in the 4 weeks before train_end\nfor w in range(1, 5):\n    t_end = train_end - (w - 1) * week\n    t_start = train_end - w * week\n    \n    likes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_start), ('timestamp', '<', t_end)])\n    likes = likes[(likes.uid.isin(target_set)) & (likes.is_organic == 1) & (likes.item_id.isin(catalog_items))]\n    \n    listens = pd.read_parquet('/task/data/train_listens.parquet', \n                              columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n                              filters=[('timestamp', '>=', t_start), ('timestamp', '<', t_end)])\n    listens = listens[(listens.uid.isin(target_set)) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.item_id.isin(catalog_items))]\n    \n    print(f'Week {w} [{t_start}..{t_end}]: org_likes={len(likes)} (users={likes.uid.nunique()}), org_listens={len(listens)} (users={listens.uid.nunique()})')\n\""
}

Tool response +06:03

<untrusted_context>
Output: Week 1 [24182000..24786800]: org_likes=7774 (users=2185), org_listens=304885 (users=5842)
Week 2 [23577200..24182000]: org_likes=7129 (users=2119), org_listens=289060 (users=5660)
Week 3 [22972400..23577200]: org_likes=7451 (users=2026), org_listens=287015 (users=5593)
Week 4 [22367600..22972400]: org_likes=7631 (users=2018), org_listens=289176 (users=5513)
Process Group PGID: 2063
</untrusted_context>

Count novel organic interactions in Week 1

+06:17

Recorded reasoning summary 47 words

Analyzing User Engagement Metrics

I'm currently focused on analyzing the latest engagement data, specifically looking at weekly organic likes and listens across catalog items, as well as the number of eligible users within the validation window. This will help me understand current user interaction patterns more deeply.

Tool callrun_shell_commandCount novel organic interactions in Week 1 Returned

Tool call · Input +06:17

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_end = 24786800\nweek = 7 * 86400\nt_w1_start = train_end - week\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Week 1 events\nw1_likes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_w1_start)])\nw1_likes = w1_likes[(w1_likes.uid.isin(target_set)) & (w1_likes.is_organic == 1) & (w1_likes.item_id.isin(catalog_items))]\n\nw1_listens = pd.read_parquet('/task/data/train_listens.parquet', \n                             columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n                             filters=[('timestamp', '>=', t_w1_start)])\nw1_listens = w1_listens[(w1_listens.uid.isin(target_set)) & (w1_listens.is_organic == 1) & (w1_listens.played_ratio_pct >= 80) & (w1_listens.item_id.isin(catalog_items))]\n\n# Check how many (uid, item) pairs are unique in w1\nw1_likes_pairs = set(zip(w1_likes.uid, w1_likes.item_id))\nw1_listens_pairs = set(zip(w1_listens.uid, w1_listens.item_id))\nprint('Week 1 unique (uid, item) organic likes:', len(w1_likes_pairs))\nprint('Week 1 unique (uid, item) organic listens >=80%:', len(w1_listens_pairs))\n\n# Now check prior history (say 60 days before w1_start)\nt_hist_start = t_w1_start - 60 * 86400\nprior_listens = pd.read_parquet('/task/data/train_listens.parquet',\n                                columns=['uid', 'item_id'],\n                                filters=[('timestamp', '>=', t_hist_start), ('timestamp', '<', t_w1_start)])\nprior_listens = prior_listens[prior_listens.uid.isin(target_set)]\nprior_pairs = set(zip(prior_listens.uid, prior_listens.item_id))\n\nnew_likes = w1_likes_pairs - prior_pairs\nnew_listens = w1_listens_pairs - prior_pairs\nprint('Week 1 NEW organic likes (not in prior 60d):', len(new_likes))\nprint('Week 1 NEW organic listens (not in prior 60d):', len(new_listens))\n\"",
  "description": "Count novel organic interactions in Week 1"
}

Tool response +06:26

<untrusted_context>
Output: Week 1 unique (uid, item) organic likes: 7645
Week 1 unique (uid, item) organic listens >=80%: 172936
Week 1 NEW organic likes (not in prior 60d): 5813
Week 1 NEW organic listens (not in prior 60d): 53345
Process Group PGID: 2090
</untrusted_context>

Benchmark PyTorch training throughput on 8 CPU threads

+06:40

Recorded reasoning summary 39 words

Observing Initial Data

The early results are striking; I'm seeing approximately 5,800 new organic likes and over 53,000 listens exceeding 80% in just the first week. This aligns remarkably with the scale and distribution observed during the validation phase.

Recorded reasoning summary 57 words

Formulating Training Data

I'm now focusing on constructing genuine discovery-prediction training instances from the initial weeks of data. The goal is to leverage user interaction histories, specifically their prior likes and listens, as features to predict novel organic likes and listens in subsequent weeks. I'm also initiating timing experiments to gauge PyTorch model training performance on CPU.

Tool callrun_shell_commandBenchmark PyTorch training throughput on 8 CPU threads Returned

Tool call · Input +06:40

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nclass UserTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids, weights):\n        # item_ids: (B, L), weights: (B, L)\n        emb = self.item_emb(item_ids) # (B, L, D)\n        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled = weighted / norm\n        out = self.fc(pooled)\n        return F.normalize(out, p=2, dim=-1)\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items, embed_dim)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids):\n        emb = self.item_emb(item_ids)\n        out = self.fc(emb)\n        return F.normalize(out, p=2, dim=-1)\n\nnum_items = 30000\nembed_dim = 64\noutput_dim = 64\nbatch_size = 512\nseq_len = 64\n\nuser_tower = UserTower(num_items, embed_dim, output_dim)\nitem_tower = ItemTower(num_items, embed_dim, output_dim)\noptimizer = torch.optim.Adam(list(user_tower.parameters()) + list(item_tower.parameters()), lr=1e-3)\n\ntorch.set_num_threads(8)\n\n# Benchmark 50 steps\nt0 = time.time()\nnum_steps = 50\nfor step in range(num_steps):\n    optimizer.zero_grad()\n    u_items = torch.randint(0, num_items, (batch_size, seq_len))\n    u_weights = torch.rand(batch_size, seq_len)\n    pos_items = torch.randint(0, num_items, (batch_size,))\n    \n    u_vecs = user_tower(u_items, u_weights) # (B, D)\n    i_vecs = item_tower(pos_items) # (B, D)\n    \n    logits = torch.matmul(u_vecs, i_vecs.T) / 0.07 # (B, B)\n    labels = torch.arange(batch_size)\n    loss = F.cross_entropy(logits, labels)\n    loss.backward()\n    optimizer.step()\n\nelapsed = time.time() - t0\nsamples = num_steps * batch_size\nprint(f'Processed {samples} samples in {elapsed:.2f}s -> {samples / elapsed:.1f} samples/sec')\n\"",
  "description": "Benchmark PyTorch training throughput on 8 CPU threads"
}

Tool response +06:46

<untrusted_context>
Output: Processed 25600 samples in 0.68s -> 37459.8 samples/sec
Process Group PGID: 2111
</untrusted_context>

Benchmark full inference (matmul, masking, top-k) for 8456 users

+06:53

Recorded reasoning summary 45 words

Achieving High Throughput

I'm excited by the current throughput figures, easily clearing 37,000 samples per second. This translates to lightning-fast processing for large datasets, with 500,000 samples taking under 15 seconds. My next step is to benchmark inference time to ensure efficiency across the board.

Tool callrun_shell_commandBenchmark full inference (matmul, masking, top-k) for 8456 users Returned

Tool call · Input +06:53

{
  "description": "Benchmark full inference (matmul, masking, top-k) for 8456 users",
  "command": "python3 -c \"\nimport torch\nimport numpy as np\nimport time\n\ntorch.set_num_threads(8)\nt0 = time.time()\nU = torch.randn(8456, 64)\nV = torch.randn(30000, 64)\n\n# Matrix multiply\nscores = torch.matmul(U, V.T) # (8456, 30000)\nprint(f'Scores matmul took: {time.time() - t0:.4f}s')\n\n# Masking and top-100\nt1 = time.time()\n# Simulate packed mask\npacked = np.random.bytes(8456 * 3750)\npacked_arr = np.frombuffer(packed, dtype=np.uint8).reshape(8456, 3750)\n\n# Unpack bits for all rows\n# np.unpackbits along axis 1\nunpacked = np.unpackbits(packed_arr, axis=1)[:, :30000] # (8456, 30000)\nmask_tensor = torch.from_numpy(unpacked.astype(bool))\nscores[~mask_tensor] = -1e9\n\ntop100_scores, top100_indices = torch.topk(scores, 100, dim=1)\nprint(f'Unpacking + masking + topk took: {time.time() - t1:.4f}s')\nprint(f'Total inference took: {time.time() - t0:.4f}s')\n\""
}

Tool response +06:57

<untrusted_context>
Output: Scores matmul took: 0.1929s
Unpacking + masking + topk took: 0.4212s
Total inference took: 0.6142s
Process Group PGID: 2135
</untrusted_context>

Inspect explicit action logs vs validation preference state

+07:13

Recorded reasoning summary 45 words

Achieving Sub-Second Inference

I've successfully optimized the inference process; the entire computation for all 8,456 users across 30,000 items now completes in a mere 0.61 seconds. This aligns perfectly with the requirement of computing one user embedding and performing maximum inner-product search at request time.

Recorded reasoning summary 44 words

Designing Model Architecture

My focus has shifted to dissecting the model's architecture, specifically how to decompose it into independent user and item towers. I'm investigating ways to represent user interactions, including explicit preferences and detailed listening history, to inform the user tower's representation effectively.

Tool callrun_shell_commandInspect explicit action logs vs validation preference state Returned

Tool call · Input +07:13

{
  "description": "Inspect explicit action logs vs validation preference state",
  "command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\ntrain_likes = pd.read_parquet(data_dir / 'train_likes.parquet')\ntrain_unlikes = pd.read_parquet(data_dir / 'train_unlikes.parquet')\ntrain_dislikes = pd.read_parquet(data_dir / 'train_dislikes.parquet')\ntrain_undislikes = pd.read_parquet(data_dir / 'train_undislikes.parquet')\n\nprint('Val pref rows:', len(val_pref))\nprint('Train likes:', len(train_likes))\nprint('Train unlikes:', len(train_unlikes))\nprint('Train dislikes:', len(train_dislikes))\nprint('Train undislikes:', len(train_undislikes))\n\""
}

Tool response +07:15

<untrusted_context>
Output: Val pref rows: 469124
Train likes: 814831
Train unlikes: 292665
Train dislikes: 101075
Train undislikes: 19960
Process Group PGID: 2165
</untrusted_context>

Analyze user history coverage combining likes and listens

+07:26

Recorded reasoning summary 41 words

Clarifying Preference State

I'm refining my understanding of the preference state post-signal aggregation. I'm currently verifying if users can exhibit positive explicit likes while simultaneously having both positive explicit and implicit dislikes. This analysis is crucial for accurately modeling user sentiment.

Recorded reasoning summary 50 words

Incorporating All Signals

I'm now focusing on how to integrate all available positive signals, including implicit ones like high organic listen percentages, into user profiles. This is to ensure every user has a rich positive interaction history, even if explicit likes are absent. I'll then pair this with explicit dislikes.

Tool callrun_shell_commandAnalyze user history coverage combining likes and listens Returned

Tool call · Input +07:26

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\n\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked'].groupby('uid')['item_id'].apply(set).to_dict()\npref_dislikes = val_pref[val_pref.state == 'disliked'].groupby('uid')['item_id'].apply(set).to_dict()\n\n# Recent listens (last 60 days)\ncutoff_60d = 24786800 - 60 * 86400\nrecent_listens = pd.read_parquet(\n    data_dir / 'train_listens.parquet',\n    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_60d)]\n)\nrecent_pos = recent_listens[(recent_listens.uid.isin(target_set)) & (recent_listens.played_ratio_pct >= 80)]\nrecent_pos_items = recent_pos.groupby('uid')['item_id'].apply(set).to_dict()\n\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\npos_counts = []\nneg_counts = []\nfor uid in targets.uid:\n    u_likes = pref_likes.get(uid, set())\n    u_listens = recent_pos_items.get(uid, set())\n    u_all_pos = (u_likes | u_listens) & cat_items\n    u_dislikes = pref_dislikes.get(uid, set()) & cat_items\n    pos_counts.append(len(u_all_pos))\n    neg_counts.append(len(u_dislikes))\n\nprint('Catalog positive items per user: min=', min(pos_counts), 'mean=', np.mean(pos_counts), 'median=', np.median(pos_counts), 'max=', max(pos_counts))\nprint('Users with at least 5 positive catalog items:', sum(c >= 5 for c in pos_counts), '/', len(pos_counts))\nprint('Users with at least 10 positive catalog items:', sum(c >= 10 for c in pos_counts), '/', len(pos_counts))\nprint('Disliked catalog items per user: mean=', np.mean(neg_counts), 'median=', np.median(neg_counts), 'users with >0 dislikes:', sum(c > 0 for c in neg_counts))\n\"",
  "description": "Analyze user history coverage combining likes and listens"
}

Tool response +07:31

<untrusted_context>
Output: Catalog positive items per user: min= 0 mean= 286.9950331125828 median= 206.0 max= 2627
Users with at least 5 positive catalog items: 8316 / 8456
Users with at least 10 positive catalog items: 8199 / 8456
Disliked catalog items per user: mean= 5.994441816461684 median= 1.0 users with >0 dislikes: 4277
Process Group PGID: 2183
</untrusted_context>

Compare user listen volume in recent vs older windows

+07:48

Recorded reasoning summary 58 words

Analyzing User Engagement Metrics

My analysis reveals a strong user engagement with our catalog. The average user has positively interacted with 287 items, with a median of 206. A significant majority, over 8,300 users, demonstrate consistent interaction by having at least five positive catalog items. This figure further solidifies our understanding of robust user activity within the platform.

Recorded reasoning summary 67 words

Refining Training Sample Generation

I'm now focusing on the specifics of generating training samples. My thought process is delving into how we can ensure all target users have at least 20 positive listens, even falling back to older interactions if recent ones are sparse. Following this, I'll be designing the training samples and objective, considering a contrastive learning approach with a sampled softmax for the two-tower model.

Recorded reasoning summary 56 words

Exploring Training Sample Generation

I'm currently exploring training sample generation, specifically how to construct positive and negative pairs. The idea is to use a user's history as input to the user tower and a target item for the item tower. This approach offers efficiency by leveraging in-batch negatives and a large number of potential training samples.

Recorded reasoning summary 44 words

Optimizing Training Sample Creation

I'm refining the generation of training samples by considering item weighting based on interaction type, recency, and frequency. My thoughts are also now including the incorporation of explicit dislikes as negative examples, enhancing the robustness of the model's learning process.

Recorded reasoning summary 93 words

Incorporating Dislikes

I'm now considering how to incorporate user dislikes into the user tower's embedding. The thought is to embed disliked tracks and then either subtract or concatenate these embeddings from the positive ones, allowing the model to learn to steer the user vector away from undesirable content.

Addressing Sampling Bias

I'm contemplating the challenges of sampling bias in our in-batch negatives. The idea is to explore methods to mitigate this bias, potentially by incorporating popularity-debiased negatives or hard negatives to ensure a more robust and fair learning process for the two-tower model.

Recorded reasoning summary 98 words

Testing Log-Q Correction

I'm now evaluating the impact of log-Q correction on model performance. The current thought is to test both with and without this correction on the public validation set, leveraging /task/evaluate_public.py for direct evaluation. My focus is on whether this correction recovers the true unbiased multinomial distribution.

Incorporating Temporal Dynamics

I'm considering the temporal aspect of user preferences. My thought is to empirically verify how much more predictive recent listening history is compared to older interactions. I plan to write a Python test to measure similarity between validation window interactions and recent versus older listening data.

Tool callrun_shell_commandCompare user listen volume in recent vs older windows Returned

Tool call · Input +07:48

{
  "description": "Compare user listen volume in recent vs older windows",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check how recent the user's history is\ntrain_end = 24786800\nday = 86400\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nval_likes = val_likes[(val_likes.uid.isin(target_set)) & (val_likes.is_organic == 1)]\n\n# Look at train listens for 50 sample users who had val likes\nsample_uids = list(val_likes.uid.unique()[:50])\n\n# Compare item co-occurrence or play frequency in recent 14d vs 90d\nlistens_recent = pd.read_parquet('/task/data/train_listens.parquet',\n                                filters=[('timestamp', '>=', train_end - 14*day)],\n                                columns=['uid', 'item_id'])\nlistens_older = pd.read_parquet('/task/data/train_listens.parquet',\n                               filters=[('timestamp', '<', train_end - 60*day), ('timestamp', '>=', train_end - 90*day)],\n                               columns=['uid', 'item_id'])\n\nprint('Listens last 14d target users:', listens_recent.uid.isin(target_set).sum())\nprint('Listens 60-90d target users:', listens_older.uid.isin(target_set).sum())\n\""
}

Tool response +07:51

<untrusted_context>
Output: Listens last 14d target users: 2911931
Listens 60-90d target users: 5044288
Process Group PGID: 2204
</untrusted_context>

Inspect catalog tracks listen count statistics

+08:05

Recorded reasoning summary 51 words

Prototyping Two-Tower

I'm thinking about quickly building a two-tower model prototype to run through the public evaluator. My goal is to get an initial validation score and then iterate to improve it. I'm also starting to outline the script for our first full two-tower training run, ensuring all components are well-defined.

Recorded reasoning summary 50 words

Refining Data Sources

I'm currently focusing on defining the precise data to be used for training and validation. My immediate thought is to leverage recent interactions from the train_* dataset, considering organic likes and high-completion listens as primary targets, while also factoring in user history with recency and frequency weights.

Recorded reasoning summary 39 words

Considering Negatives

I'm evaluating the effectiveness of negative sampling strategies. Initially, in-batch negatives seem robust with a large quantity per positive. I'm now weighing the benefit of augmenting this with uniformly sampled catalog negatives to broaden the negative distribution.

Recorded reasoning summary 60 words

Testing Mixed Negatives

I'm now focused on validating a mixed negative sampling strategy. By combining in-batch and uniformly sampled catalog negatives, I aim to ensure both niche and popular items are appropriately handled, preventing over-penalization of the latter. I'm also initiating a check on the UserTower and ItemTower architectures, evaluating embedding dimensions and their impact on parameter size and latency.

Recorded reasoning summary 45 words

Enhancing Item Representation

I'm exploring ways to enrich the ItemTower by integrating item features. I'm considering concatenating item embeddings with normalized popularity metrics like positive_listens and organic_positive_listens, then passing this combined representation through the MLP. This could allow the tower to learn item priors directly.

Tool callrun_shell_commandInspect catalog tracks listen count statistics Returned

Tool call · Input +08:05

{
  "description": "Inspect catalog tracks listen count statistics",
  "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.describe())\n\""
}

Tool response +08:07

<untrusted_context>
Output: item_id  ...  popularity_rank
count  3.000000e+04  ...     30000.000000
mean   4.698278e+06  ...     15000.500000
std    2.700274e+06  ...      8660.398374
min    2.060000e+02  ...         1.000000
25%    2.382368e+06  ...      7500.750000
50%    4.687992e+06  ...     15000.500000
75%    7.040711e+06  ...     22500.250000
max    9.390294e+06  ...     30000.000000

[8 rows x 4 columns]
Process Group PGID: 2231
</untrusted_context>

Inspect positive and organic positive listens in catalog

+08:14

Tool callrun_shell_commandInspect positive and organic positive listens in catalog Returned

Tool call · Input +08:14

{
  "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat[['positive_listens', 'organic_positive_listens']].describe())\n\"",
  "description": "Inspect positive and organic positive listens in catalog"
}

Tool response +08:16

<untrusted_context>
Output: positive_listens  organic_positive_listens
count      30000.000000              30000.000000
mean         668.544300                313.926433
std         1053.389553                636.039902
min          163.000000                  0.000000
25%          228.000000                 93.000000
50%          354.000000                162.000000
75%          669.250000                309.000000
max        26615.000000              20620.000000
Process Group PGID: 2248
</untrusted_context>

Count novel discovery events across historical weeks

+08:33

Recorded reasoning summary 43 words

Confirming Catalog Richness

I'm thrilled to report that a thorough sweep of our 30,000-item catalog confirms every single item boasts at least 163 positive listens in the training data. This is a monumental discovery, as it means we've effectively eliminated cold catalog items.

Recorded reasoning summary 50 words

Exploring User Representations

I'm now delving into how user history, specifically within UserTower, might be leveraged. My current focus is on whether item embeddings, weighted by interaction strength and incorporating recency decay, could form the basis of user representations. I'm also considering how to handle disliked items in this calculation.

Recorded reasoning summary 42 words

Investigating Embedding Sharing

I'm currently exploring the implications of sharing the item_emb table between the UserTower and ItemTower. My analysis suggests this could foster collaborative filtering principles by pulling related item embeddings closer. I'll also test whether separate embeddings yield superior performance.

Recorded reasoning summary 45 words

Refining Tower Architectures

I'm refining the architecture of both the user and item towers. Specifically, I'm confirming that separate nn.Module instances for each tower are indeed the correct interpretation, each with its own distinct parameters and embedding layers, as opposed to shared embeddings between them.

Recorded reasoning summary 36 words

Testing Tower Designs

I'm now designing experiments to directly compare separate versus shared item embedding tables across distinct tower architectures. This will involve setting up validation runs to precisely measure NDCG@10 and determine the optimal configuration.

Recorded reasoning summary 49 words

Defining Training Weeks

I'm now defining the specific time windows for training data, segmenting into four distinct weeks. For each week, I'm carefully specifying the target events, focusing on organic likes and high-completion listens, and critically, I'm incorporating a discovery filter to ensure we only train on novel interactions.

Tool callrun_shell_commandCount novel discovery events across historical weeks Returned

Tool call · Input +08:33

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_end = 24786800\nday = 86400\nweek = 7 * day\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Let's inspect 4 weeks\nfor w in [1, 2, 3, 4]:\n    t_end = train_end - (w - 1) * week\n    t_start = train_end - w * week\n    \n    # Target events in this week\n    w_likes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_start), ('timestamp', '<', t_end)])\n    w_likes = w_likes[(w_likes.uid.isin(target_set)) & (w_likes.is_organic == 1) & (w_likes.item_id.isin(catalog_items))]\n    \n    w_listens = pd.read_parquet('/task/data/train_listens.parquet',\n                                columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n                                filters=[('timestamp', '>=', t_start), ('timestamp', '<', t_end)])\n    w_listens = w_listens[(w_listens.uid.isin(target_set)) & (w_listens.is_organic == 1) & (w_listens.played_ratio_pct >= 80) & (w_listens.item_id.isin(catalog_items))]\n    \n    # Check prior interactions (say last 60 days before t_start)\n    prior_start = t_start - 60 * day\n    prior = pd.read_parquet('/task/data/train_listens.parquet',\n                            columns=['uid', 'item_id'],\n                            filters=[('timestamp', '>=', prior_start), ('timestamp', '<', t_start)])\n    prior = prior[prior.uid.isin(target_set)]\n    prior_pairs = set(zip(prior.uid, prior.item_id))\n    \n    novel_likes = set(zip(w_likes.uid, w_likes.item_id)) - prior_pairs\n    novel_listens = set(zip(w_listens.uid, w_listens.item_id)) - prior_pairs\n    \n    print(f'Week {w}: novel_likes={len(novel_likes)}, novel_listens={len(novel_listens)}, total novel={len(novel_likes | novel_listens)}')\n\"",
  "description": "Count novel discovery events across historical weeks"
}

Tool response +09:04

<untrusted_context>
Output: Week 1: novel_likes=5813, novel_listens=53345, total novel=55906
Week 2: novel_likes=5193, novel_listens=51672, total novel=53747
Week 3: novel_likes=5623, novel_listens=50641, total novel=53130
Week 4: novel_likes=5703, novel_listens=48860, total novel=51413
Process Group PGID: 2265
</untrusted_context>

Benchmark extraction of user history before cutoff

+10:07

Recorded reasoning summary 47 words

Discovering Novel Interactions

I'm focusing on the sheer volume of new positive discoveries weekly, noting approximately 53,000 novel positive items per week, which quickly accumulates over a month. My current exploration is delving into how user history, specifically interactions preceding a given week, informs this discovery process.

Tool callrun_shell_commandBenchmark extraction of user history before cutoff Returned

Tool call · Input +10:07

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\ntrain_end = 24786800\nday = 86400\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem2idx = {item: idx for idx, item in enumerate(cat.item_id)}\n\n# Let's test extracting history before cutoff T\nT = train_end\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Likes before T\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '<', T)])\nlikes = likes[likes.uid.isin(target_set)]\nlikes['idx'] = likes.item_id.map(item2idx)\nlikes = likes.dropna(subset=['idx'])\nlikes['idx'] = likes['idx'].astype(int)\n\n# Listens in last 60 days before T\nlistens = pd.read_parquet('/task/data/train_listens.parquet',\n                          columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n                          filters=[('timestamp', '>=', T - 60*day), ('timestamp', '<', T)])\nlistens = listens[(listens.uid.isin(target_set)) & (listens.played_ratio_pct >= 80)]\nlistens['idx'] = listens.item_id.map(item2idx)\nlistens = listens.dropna(subset=['idx'])\nlistens['idx'] = listens['idx'].astype(int)\n\nprint(f'Data filtered in {time.time() - t0:.2f}s: {len(likes)} likes, {len(listens)} listens')\n\"",
  "description": "Benchmark extraction of user history before cutoff"
}

Tool response +10:10

<untrusted_context>
Output: Data filtered in 1.65s: 472889 likes, 5051220 listens
Process Group PGID: 2298
</untrusted_context>

Check target users count

+10:37

Recorded reasoning summary 48 words

Optimizing Interaction Processing

I'm currently analyzing the efficiency of processing user interactions. My focus is on how to best represent each user's history, considering fixed-length or padded lists of item indices and their associated weights. The next step involves determining the optimal set of items for this representation.

Tool callrun_shell_commandCheck target users count Returned

Tool call · Input +10:37

{
  "description": "Check target users count",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's inspect how many unique catalog items users interact with\n# when we combine likes + positive listens in last 60d\ndata_dir = '/task/data'\ntargets = pd.read_parquet(f'{data_dir}/target_users.parquet')\nprint('Number of target users:', len(targets))\n\""
}

Tool response +10:38

<untrusted_context>
Output: Number of target users: 8456
Process Group PGID: 2319
</untrusted_context>

update_topic command

+10:53

Recorded reasoning summary 44 words

Developing Baseline Model

I'm currently focused on building a clean, end-to-end prototype script. The immediate goal is to prepare user history up to the validation cutoff and then generate initial training samples. This will allow for a quick baseline evaluation of a two-tower model.

Recorded reasoning summary 45 words

Refining Candidate Generation

I'm currently detailing the inference pipeline. This involves pre-computing all item embeddings offline and then generating user embeddings. The plan is to perform maximum inner-product search (MIPS) against these item embeddings and apply the eligibility mask to extract top candidates, then evaluate.

Recorded reasoning summary 37 words

Evaluating User Embedding Strategy

I'm considering how best to represent users, specifically whether to pool positive items or incorporate disliked items. I'll start with just positive signals to establish a solid initial score for the user tower.

Recorded reasoning summary 36 words

Exploring Embedding Sharing

I'm considering if the item embedding layers in both the UserTower and ItemTower should share weights. This could impact model efficiency and performance, and I'm weighing the implications of separate versus shared embeddings.

Recorded reasoning summary 51 words

Addressing Popularity Bias

I'm revisiting the candidate generation phase to account for potential popularity bias in our current dot-product similarity. While MIPS is excellent for cosine similarity, I'm exploring how to effectively integrate an item bias term ($b_i$) into the scoring mechanism, as the current approach might favor popular items unfairly.

Recorded reasoning summary 45 words

Incorporating Item Bias

I'm focusing on how to effectively incorporate an item bias term into the scoring mechanism. It appears that by augmenting user and item embeddings, I can represent this bias as an inner product, allowing me to test this approach in code soon.

Tool callupdate_topicTool call Returned

Tool call · Input +10:53

{
  "strategic_intent": "Implement and evaluate initial PyTorch two-tower neural network on validation data.",
  "title": "Initial Two-Tower Implementation & Validation",
  "summary": "Designing and implementing our initial two-tower model in /app/solution/train_val.py. The model uses separate UserTower and ItemTower modules in PyTorch, extracting historical interactions into weighted user profile bags and training via in-batch negative cross-entropy on discovery events. We will evaluate the resulting validation ranking using /task/evaluate_public.py."
}

Tool response +10:53

Current topic: "Initial Two-Tower Implementation & Validation"
Topic summary: Designing and implementing our initial two-tower model in /app/solution/train_val.py. The model uses separate UserTower and ItemTower modules in PyTorch, extracting historical interactions into weighted user profile bags and training via in-batch negative cross-entropy on discovery events. We will evaluate the resulting validation ranking using /task/evaluate_public.py.

Strategic Intent: Implement and evaluate initial PyTorch two-tower neural network on validation data.

write_file train_val.py

+11:24

Recorded reasoning summary 38 words

Drafting code structure

I'm now focusing on writing the /app/solution/train_val.py script. My immediate thought is to meticulously structure the code for preparing training pairs and user history, specifically considering the 8,456 target users and the 30,000 catalog items.

Recorded reasoning summary 41 words

Defining training targets

I'm now considering how to define the training targets, specifically looking at the weeks preceding the validation cutoff. My current thought is to segment this history into weekly chunks and identify items for each user within those periods.

Recorded reasoning summary 46 words

Refining interaction sampling

I'm now refining how to sample interactions for training, leaning towards a leave-one-out masking approach. This allows every organic like and significant listen to become a training example, ensuring the user tower predicts novel items based on their actual history, preventing any "cheating."

Recorded reasoning summary 52 words

Selecting positive examples

I'm now exploring a random sampling strategy for positive examples during training. The thought is to dynamically select one positive item $i^+$ from a user's history and mask it out, feeding the remaining items into the User Tower for prediction. This appears elegant and efficient for generating training data.

Recorded reasoning summary 37 words

Validating user/item decomposition

I'm now verifying the decomposition into user and item vectors aligns with the problem statement. My current thought is to ensure the architecture precisely supports independent precomputation of item embeddings, which this strategy facilitates.

Tool callwrite_file/app/solution/train_val.py Returned

Tool call · Input +11:24

{
  "content": "import time\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA_DIR = Path('/task/data')\nTRAIN_END = 24786800\nDAY_SEC = 86400\nNUM_CATALOG = 30000\nDEVICE = torch.device('cpu')\n\ntorch.set_num_threads(8)\n\nprint(\"Loading metadata...\")\ncat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\nidx2item = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet(DATA_DIR / 'target_users.parquet')\ntarget_uids = targets.uid.astype(int).to_numpy()\ntarget_set = set(target_uids)\nuid2idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f\"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}\")\n\n# 1. Load active explicit preference state at validation cutoff\nprint(\"Loading validation preference state...\")\nval_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked']\npref_dislikes = val_pref[val_pref.state == 'disliked']\n\nuser_pref_likes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_likes.uid, pref_likes.item_id):\n    if uid in user_pref_likes and item in item2idx:\n        user_pref_likes[uid].add(item2idx[item])\n\nuser_pref_dislikes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):\n    if uid in user_pref_dislikes and item in item2idx:\n        user_pref_dislikes[uid].add(item2idx[item])\n\n# 2. Load recent positive listens (last 90 days before cutoff)\nprint(\"Loading recent listens...\")\ncutoff_90d = TRAIN_END - 90 * DAY_SEC\nlistens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]\n)\nlistens = listens[listens.uid.isin(target_set) & (listens.played_ratio_pct >= 50)]\nlistens['item_idx'] = listens.item_id.map(item2idx)\nlistens = listens.dropna(subset=['item_idx'])\nlistens['item_idx'] = listens['item_idx'].astype(int)\n\n# Group listens per user\nprint(\"Aggregating user histories...\")\n# Count listens and check organic >= 80%\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nlistens['recency_weight'] = np.exp((listens.timestamp - TRAIN_END) / (30 * DAY_SEC)) # decay over 30 days\n\n# Pre-aggregate per user\nuser_histories = {}\nMAX_HIST_LEN = 128\n\nfor uid, group in listens.groupby('uid'):\n    # Score each item: organic strong gets higher weight\n    item_weights = {}\n    item_is_like = {}\n    \n    # First add preference likes\n    for idx in user_pref_likes.get(uid, ()):\n        item_weights[idx] = 4.0\n        item_is_like[idx] = True\n        \n    for row in group.itertuples(index=False):\n        idx = row.item_idx\n        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight\n        item_weights[idx] = item_weights.get(idx, 0.0) + w\n        \n    # Sort by weight descending, keep top MAX_HIST_LEN\n    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]\n    items = [x[0] for x in sorted_items]\n    weights = [x[1] for x in sorted_items]\n    is_like = [item_is_like.get(x[0], False) for x in sorted_items]\n    \n    user_histories[uid] = {\n        'items': np.array(items, dtype=np.int64),\n        'weights': np.array(weights, dtype=np.float32),\n        'is_like': np.array(is_like, dtype=bool)\n    }\n\n# Fallback for users with no recent listens\nfor uid in target_uids:\n    if uid not in user_histories:\n        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]\n        if p_likes:\n            user_histories[uid] = {\n                'items': np.array(p_likes, dtype=np.int64),\n                'weights': np.ones(len(p_likes), dtype=np.float32) * 4.0,\n                'is_like': np.ones(len(p_likes), dtype=bool)\n            }\n        else:\n            user_histories[uid] = {\n                'items': np.array([0], dtype=np.int64),\n                'weights': np.array([0.001], dtype=np.float32),\n                'is_like': np.array([False], dtype=bool)\n            }\n\nprint(f\"History built for {len(user_histories)} users.\")\n\n# Model definitions\nclass UserTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        \n    def forward(self, item_ids, weights):\n        # item_ids: (B, L), weights: (B, L)\n        emb = self.item_emb(item_ids) # (B, L, D)\n        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1)\n        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled = weighted / norm\n        out = self.fc(pooled)\n        return F.normalize(out, p=2, dim=-1)\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items, embed_dim)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        \n    def forward(self, item_ids):\n        emb = self.item_emb(item_ids)\n        out = self.fc(emb)\n        return F.normalize(out, p=2, dim=-1)\n\n# Dataset for training with dynamic leave-one-out masking\nclass TwoTowerDataset(Dataset):\n    def __init__(self, uids, user_histories, samples_per_epoch=200000, max_len=128):\n        self.uids = [u for u in uids if len(user_histories[u]['items']) >= 2]\n        self.user_histories = user_histories\n        self.samples_per_epoch = samples_per_epoch\n        self.max_len = max_len\n        \n    def __len__(self):\n        return self.samples_per_epoch\n        \n    def __getitem__(self, idx):\n        # Pick random user\n        uid = self.uids[np.random.randint(0, len(self.uids))]\n        hist = self.user_histories[uid]\n        items = hist['items']\n        weights = hist['weights']\n        n = len(items)\n        \n        # Pick target item: sample proportional to weights\n        probs = weights / weights.sum()\n        target_pos = np.random.choice(n, p=probs)\n        target_item = items[target_pos]\n        \n        # User input: items without target_pos\n        u_items = np.full(self.max_len, NUM_CATALOG, dtype=np.int64)\n        u_weights = np.zeros(self.max_len, dtype=np.float32)\n        \n        cur_items = np.delete(items, target_pos)[:self.max_len]\n        cur_weights = np.delete(weights, target_pos)[:self.max_len]\n        \n        u_items[:len(cur_items)] = cur_items\n        u_weights[:len(cur_weights)] = cur_weights\n        \n        return {\n            'u_items': torch.from_numpy(u_items),\n            'u_weights': torch.from_numpy(u_weights),\n            'target_item': torch.tensor(target_item, dtype=torch.long)\n        }\n\nEMBED_DIM = 64\nOUTPUT_DIM = 64\nBATCH_SIZE = 512\nLR = 1e-3\nTEMPERATURE = 0.07\nEPOCHS = 3\n\nprint(\"Initializing models...\")\nuser_tower = UserTower(NUM_CATALOG, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\noptimizer = torch.optim.Adam(\n    list(user_tower.parameters()) + list(item_tower.parameters()),\n    lr=LR,\n    weight_decay=1e-5\n)\n\ndataset = TwoTowerDataset(target_uids, user_histories, samples_per_epoch=150000, max_len=MAX_HIST_LEN)\ndataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(\"Starting training...\")\nuser_tower.train()\nitem_tower.train()\n\nfor epoch in range(1, EPOCHS + 1):\n    total_loss = 0.0\n    t0 = time.time()\n    for batch_idx, batch in enumerate(dataloader):\n        optimizer.zero_grad()\n        u_items = batch['u_items'].to(DEVICE)\n        u_weights = batch['u_weights'].to(DEVICE)\n        target_items = batch['target_item'].to(DEVICE)\n        \n        u_vecs = user_tower(u_items, u_weights) # (B, D)\n        i_vecs = item_tower(target_items)       # (B, D)\n        \n        # Logits with temperature\n        logits = torch.matmul(u_vecs, i_vecs.T) / TEMPERATURE # (B, B)\n        labels = torch.arange(BATCH_SIZE, device=DEVICE)\n        \n        loss = F.cross_entropy(logits, labels)\n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 100 == 0:\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(dataloader)}] Loss: {total_loss/(batch_idx+1):.4f}\")\n            \n    print(f\"Epoch {epoch} completed in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(dataloader):.4f}\")\n\n# Evaluation on Public Validation\nprint(\"Evaluating on public validation set...\")\nuser_tower.eval()\nitem_tower.eval()\n\nwith torch.no_grad():\n    # 1. Precompute candidate item embeddings\n    print(\"Precomputing item embeddings...\")\n    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n    item_vecs = item_tower(all_item_ids) # (30000, D)\n    \n    # 2. Compute user embeddings for all target users\n    print(\"Computing user embeddings...\")\n    u_items_all = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)\n    u_weights_all = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)\n    \n    for i, uid in enumerate(target_uids):\n        hist = user_histories[uid]\n        cur_items = hist['items'][:MAX_HIST_LEN]\n        cur_weights = hist['weights'][:MAX_HIST_LEN]\n        u_items_all[i, :len(cur_items)] = cur_items\n        u_weights_all[i, :len(cur_weights)] = cur_weights\n        \n    u_items_tensor = torch.from_numpy(u_items_all).to(DEVICE)\n    u_weights_tensor = torch.from_numpy(u_weights_all).to(DEVICE)\n    user_vecs = user_tower(u_items_tensor, u_weights_tensor) # (8456, D)\n    \n    # 3. MIPS retrieval: User x Item dot product\n    print(\"Computing inner products...\")\n    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n    \n    # 4. Mask ineligible candidates\n    print(\"Applying validation candidate eligibility mask...\")\n    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\n    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\n    \n    scores[~mask_tensor] = -1e9\n    \n    # 5. Top 100 per user\n    print(\"Retrieving top 100 candidates per user...\")\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.cpu().numpy()\n\n# 6. Build validation ranking dataframe\nprint(\"Formatting validation ranking...\")\nrecords = []\nfor r, uid in enumerate(target_uids):\n    ranked_catalog_indices = top100_indices[r]\n    ranked_item_ids = idx2item[ranked_catalog_indices]\n    for rank, item_id in enumerate(ranked_item_ids, 1):\n        records.append((int(uid), int(item_id), rank))\n\nval_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nval_ranking_path = Path('/app/solution/validation.parquet')\nval_ranking_df.to_parquet(val_ranking_path)\nprint(f\"Validation ranking written to {val_ranking_path}\")\n\n# Run evaluate_public\ngraded = validation_truth(eligibility, target_set)\nvalidated = validate_ranking(val_ranking_df, target_set)\nranked = ranked_items(validated)\nscore_report = score(ranked, graded)\nprint(f\"Validation score report: {json.dumps(score_report, indent=2)}\")\n",
  "file_path": "/app/solution/train_val.py"
}

Tool response +11:24

Successfully created and wrote to new file: /app/solution/train_val.py. Here is the updated code:
import time
import json
from pathlib import Path
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
import sys
sys.path.append('/task')
from evaluate_public import validation_truth, score
from ranking_evaluation import CandidateSet, validate_ranking, ranked_items

DATA_DIR = Path('/task/data')
TRAIN_END = 24786800
DAY_SEC = 86400
NUM_CATALOG = 30000
DEVICE = torch.device('cpu')

torch.set_num_threads(8)

print("Loading metadata...")
cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')
item2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}
idx2item = cat.item_id.to_numpy()

targets = pd.read_parquet(DATA_DIR / 'target_users.parquet')
target_uids = targets.uid.astype(int).to_numpy()
target_set = set(target_uids)
uid2idx = {uid: idx for idx, uid in enumerate(target_uids)}

print(f"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}")

# 1. Load active explicit preference state at validation cutoff
print("Loading validation preference state...")
val_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')
pref_likes = val_pref[val_pref.state == 'liked']
pref_dislikes = val_pref[val_pref.state == 'disliked']

user_pref_likes = {uid: set() for uid in target_uids}
for uid, item in zip(pref_likes.uid, pref_likes.item_id):
    if uid in user_pref_likes and item in item2idx:
        user_pref_likes[uid].add(item2idx[item])

user_pref_dislikes = {uid: set() for uid in target_uids}
for uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):
    if uid in user_pref_dislikes and item in item2idx:
        user_pref_dislikes[uid].add(item2idx[item])

# 2. Load recent positive listens (last 90 days before cutoff)
print("Loading recent listens...")
cutoff_90d = TRAIN_END - 90 * DAY_SEC
listens = pd.read_parquet(
    DATA_DIR / 'train_listens.parquet',
    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],
    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]
)
listens = listens[listens.uid.isin(target_set) & (listens.played_ratio_pct >= 50)]
listens['item_idx'] = listens.item_id.map(item2idx)
listens = listens.dropna(subset=['item_idx'])
listens['item_idx'] = listens['item_idx'].astype(int)

# Group listens per user
print("Aggregating user histories...")
# Count listens and check organic >= 80%
listens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)
listens['recency_weight'] = np.exp((listens.timestamp - TRAIN_END) / (30 * DAY_SEC)) # decay over 30 days

# Pre-aggregate per user
user_histories = {}
MAX_HIST_LEN = 128

for uid, group in listens.groupby('uid'):
    # Score each item: organic strong gets higher weight
    item_weights = {}
    item_is_like = {}
    
    # First add preference likes
    for idx in user_pref_likes.get(uid, ()):
        item_weights[idx] = 4.0
        item_is_like[idx] = True
        
    for row in group.itertuples(index=False):
        idx = row.item_idx
        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight
        item_weights[idx] = item_weights.get(idx, 0.0) + w
        
    # Sort by weight descending, keep top MAX_HIST_LEN
    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]
    items = [x[0] for x in sorted_items]
    weights = [x[1] for x in sorted_items]
    is_like = [item_is_like.get(x[0], False) for x in sorted_items]
    
    user_histories[uid] = {
        'items': np.array(items, dtype=np.int64),
        'weights': np.array(weights, dtype=np.float32),
        'is_like': np.array(is_like, dtype=bool)
    }

# Fallback for users with no recent listens
for uid in target_uids:
    if uid not in user_histories:
        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]
        if p_likes:
            user_histories[uid] = {
                'items': np.array(p_likes, dtype=np.int64),
                'weights': np.ones(len(p_likes), dtype=np.float32) * 4.0,
                'is_like': np.ones(len(p_likes), dtype=bool)
            }
        else:
            user_histories[uid] = {
                'items': np.array([0], dtype=np.int64),
                'weights': np.array([0.001], dtype=np.float32),
                'is_like': np.array([False], dtype=bool)
            }

print(f"History built for {len(user_histories)} users.")

# Model definitions
class UserTower(nn.Module):
    def __init__(self, num_items, embed_dim, output_dim, dropout=0.1):
        super().__init__()
        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)
        self.fc = nn.Sequential(
            nn.Linear(embed_dim, embed_dim),
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        
    def forward(self, item_ids, weights):
        # item_ids: (B, L), weights: (B, L)
        emb = self.item_emb(item_ids) # (B, L, D)
        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1)
        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        pooled = weighted / norm
        out = self.fc(pooled)
        return F.normalize(out, p=2, dim=-1)

class ItemTower(nn.Module):
    def __init__(self, num_items, embed_dim, output_dim, dropout=0.1):
        super().__init__()
        self.item_emb = nn.Embedding(num_items, embed_dim)
        self.fc = nn.Sequential(
            nn.Linear(embed_dim, embed_dim),
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        
    def forward(self, item_ids):
        emb = self.item_emb(item_ids)
        out = self.fc(emb)
        return F.normalize(out, p=2, dim=-1)

# Dataset for training with dynamic leave-one-out masking
class TwoTowerDataset(Dataset):
    def __init__(self, uids, user_histories, samples_per_epoch=200000, max_len=128):
        self.uids = [u for u in uids if len(user_histories[u]['items']) >= 2]
        self.user_histories = user_histories
        self.samples_per_epoch = samples_per_epoch
        self.max_len = max_len
        
    def __len__(self):
        return self.samples_per_epoch
        
    def __getitem__(self, idx):
        # Pick random user
        uid = self.uids[np.random.randint(0, len(self.uids))]
        hist = self.user_histories[uid]
        items = hist['items']
        weights = hist['weights']
        n = len(items)
        
        # Pick target item: sample proportional to weights
        probs = weights / weights.sum()
        target_pos = np.random.choice(n, p=probs)
        target_item = items[target_pos]
        
        # User input: items without target_pos
        u_items = np.full(self.max_len, NUM_CATALOG, dtype=np.int64)
        u_weights = np.zeros(self.max_len, dtype=np.float32)
        
        cur_items = np.delete(items, target_pos)[:self.max_len]
        cur_weights = np.delete(weights, target_pos)[:self.max_len]
        
        u_items[:len(cur_items)] = cur_items
        u_weights[:len(cur_weights)] = cur_weights
        
        return {
            'u_items': torch.from_numpy(u_items),
            'u_weights': torch.from_numpy(u_weights),
            'target_item': torch.tensor(target_item, dtype=torch.long)
        }

EMBED_DIM = 64
OUTPUT_DIM = 64
BATCH_SIZE = 512
LR = 1e-3
TEMPERATURE = 0.07
EPOCHS = 3

print("Initializing models...")
user_tower = UserTower(NUM_CATALOG, EMBED_DIM, OUTPUT_DIM).to(DEVICE)
item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

optimizer = torch.optim.Adam(
    list(user_tower.parameters()) + list(item_tower.parameters()),
    lr=LR,
    weight_decay=1e-5
)

dataset = TwoTowerDataset(target_uids, user_histories, samples_per_epoch=150000, max_len=MAX_HIST_LEN)
dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

print("Starting training...")
user_tower.train()
item_tower.train()

for epoch in range(1, EPOCHS + 1):
    total_loss = 0.0
    t0 = time.time()
    for batch_idx, batch in enumerate(dataloader):
        optimizer.zero_grad()
        u_items = batch['u_items'].to(DEVICE)
        u_weights = batch['u_weights'].to(DEVICE)
        target_items = batch['target_item'].to(DEVICE)
        
        u_vecs = user_tower(u_items, u_weights) # (B, D)
        i_vecs = item_tower(target_items)       # (B, D)
        
        # Logits with temperature
        logits = torch.matmul(u_vecs, i_vecs.T) / TEMPERATURE # (B, B)
        labels = torch.arange(BATCH_SIZE, device=DEVICE)
        
        loss = F.cross_entropy(logits, labels)
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
        if (batch_idx + 1) % 100 == 0:
            print(f"Epoch {epoch} [{batch_idx+1}/{len(dataloader)}] Loss: {total_loss/(batch_idx+1):.4f}")
            
    print(f"Epoch {epoch} completed in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(dataloader):.4f}")

# Evaluation on Public Validation
print("Evaluating on public validation set...")
user_tower.eval()
item_tower.eval()

with torch.no_grad():
    # 1. Precompute candidate item embeddings
    print("Precomputing item embeddings...")
    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)
    item_vecs = item_tower(all_item_ids) # (30000, D)
    
    # 2. Compute user embeddings for all target users
    print("Computing user embeddings...")
    u_items_all = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)
    u_weights_all = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)
    
    for i, uid in enumerate(target_uids):
        hist = user_histories[uid]
        cur_items = hist['items'][:MAX_HIST_LEN]
        cur_weights = hist['weights'][:MAX_HIST_LEN]
        u_items_all[i, :len(cur_items)] = cur_items
        u_weights_all[i, :len(cur_weights)] = cur_weights
        
    u_items_tensor = torch.from_numpy(u_items_all).to(DEVICE)
    u_weights_tensor = torch.from_numpy(u_weights_all).to(DEVICE)
    user_vecs = user_tower(u_items_tensor, u_weights_tensor) # (8456, D)
    
    # 3. MIPS retrieval: User x Item dot product
    print("Computing inner products...")
    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)
    
    # 4. Mask ineligible candidates
    print("Applying validation candidate eligibility mask...")
    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')
    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]
    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)
    
    scores[~mask_tensor] = -1e9
    
    # 5. Top 100 per user
    print("Retrieving top 100 candidates per user...")
    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)
    top100_indices = top100_indices.cpu().numpy()

# 6. Build validation ranking dataframe
print("Formatting validation ranking...")
records = []
for r, uid in enumerate(target_uids):
    ranked_catalog_indices = top100_indices[r]
    ranked_item_ids = idx2item[ranked_catalog_indices]
    for rank, item_id in enumerate(ranked_item_ids, 1):
        records.append((int(uid), int(item_id), rank))

val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])
val_ranking_path = Path('/app/solution/validation.parquet')
val_ranking_df.to_parquet(val_ranking_path)
print(f"Validation ranking written to {val_ranking_path}")

# Run evaluate_public
graded = validation_truth(eligibility, target_set)
validated = validate_ranking(val_ranking_df, target_set)
ranked = ranked_items(validated)
score_report = score(ranked, graded)
print(f"Validation score report: {json.dumps(score_report, indent=2)}")

Train and evaluate initial two-tower model on validation set

+11:59

Model text

I have created /app/solution/train_val.py which implements:

Extraction and recency/completion-weighted aggregation of user history from train_listens and validation_preference_state.
PyTorch UserTower and ItemTower architectures using PyTorch CPU with layer normalization, GELU activation, and L2 normalization.
Mini-batch contrastive training with dynamic leave-one-out masking and in-batch negative cross-entropy loss.
MIPS candidate retrieval, validation eligibility masking, and formatted parquet ranking generation.

Next steps: Execute the script to train the model, evaluate against /task/evaluate_public.py, and inspect the validation NDCG@10 score.

Tool callrun_shell_commandTrain and evaluate initial two-tower model on validation set Returned

Tool call · Input +11:59

{
  "description": "Train and evaluate initial two-tower model on validation set",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +12:25

<untrusted_context>
Output: /opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp
  result = getattr(ufunc, method)(*inputs, **kwargs)
/app/solution/train_val.py:179: RuntimeWarning: invalid value encountered in divide
  probs = weights / weights.sum()
Traceback (most recent call last):
  File "/app/solution/train_val.py", line 226, in <module>
    for batch_idx, batch in enumerate(dataloader):
                            ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 734, in __next__
    data = self._next_data()
           ^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py", line 790, in _next_data
    data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
Loading metadata...
Target users: 8456, Catalog items: 30000
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
History built for 8456 users.
Initializing models...
Starting training...
    data = [self.dataset[idx] for idx in possibly_batched_index]
            ~~~~~~~~~~~~^^^^^
  File "/app/solution/train_val.py", line 180, in __getitem__
    target_pos = np.random.choice(n, p=probs)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "numpy/random/mtrand.pyx", line 990, in numpy.random.mtrand.RandomState.choice
ValueError: probabilities contain NaN
Exit Code: 1
Process Group PGID: 2347
</untrusted_context>

Check timestamp dtype and subtraction behavior

+12:29

Recorded reasoning summary 35 words

Investigating Exp Overflow

I'm digging into a recent np.exp overflow. It seems the subtraction listens.timestamp - TRAIN_END is producing values larger than anticipated, likely due to the timestamp's magnitude, causing the exponential function to fail.

Tool callrun_shell_commandCheck timestamp dtype and subtraction behavior Returned

Tool call · Input +12:29

{
  "command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['timestamp'], filters=[('timestamp', '>=', 24786800 - 86400)])\nprint('dtype:', df.timestamp.dtype)\ndiff = df.timestamp.astype(float) - 24786800.0\nprint('diff min/max:', diff.min(), diff.max())\n\"",
  "description": "Check timestamp dtype and subtraction behavior"
}

Tool response +12:32

<untrusted_context>
Output: dtype: uint32
diff min/max: -86400.0 -5.0
Process Group PGID: 2382
</untrusted_context>

replace train_val.py

+12:38

Recorded reasoning summary 39 words

Analyzing Timestamp Arithmetic

I've identified a critical issue where subtracting a larger value from listens.timestamp, a uint32, causes it to wrap around to a massive positive number, significantly impacting subsequent calculations like np.exp. This unexpected overflow needs immediate attention.

Tool callreplace/app/solution/train_val.py Returned

Tool call · Input +12:38

{
  "old_string": "# Group listens per user\nprint(\"Aggregating user histories...\")\n# Count listens and check organic >= 80%\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nlistens['recency_weight'] = np.exp((listens.timestamp - TRAIN_END) / (30 * DAY_SEC)) # decay over 30 days",
  "allow_multiple": false,
  "new_string": "# Group listens per user\nprint(\"Aggregating user histories...\")\n# Count listens and check organic >= 80%\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)\nlistens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))",
  "instruction": "Cast timestamp to float to prevent uint32 underflow in recency_weight, and guard probability normalization.",
  "file_path": "/app/solution/train_val.py"
}

Tool response +12:38

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...

# Group listens per user
print("Aggregating user histories...")
# Count listens and check organic >= 80%
listens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)
ts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)
listens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))

# Pre-aggregate per user
user_histories = {}
MAX_HIST_LEN = 128

...

replace train_val.py

+13:12

Model text

I have updated the timestamp subtraction to cast from uint32 to float64 and clip the exponent argument, avoiding unsigned integer underflow.

Next, I will update the probability normalization in TwoTowerDataset to ensure numerical safety.

Tool callreplace/app/solution/train_val.py Returned

Tool call · Input +13:12

{
  "new_string": "        # Pick target item: sample proportional to weights\n        total_w = float(weights.sum())\n        if total_w > 0 and np.isfinite(total_w):\n            probs = weights / total_w\n            probs = np.maximum(probs, 0.0)\n            probs = probs / probs.sum()\n        else:\n            probs = np.ones(n, dtype=np.float64) / n\n        target_pos = np.random.choice(n, p=probs)",
  "file_path": "/app/solution/train_val.py",
  "instruction": "Safeguard probability normalization against division by zero and numerical drift",
  "allow_multiple": false,
  "old_string": "        # Pick target item: sample proportional to weights\n        probs = weights / weights.sum()\n        target_pos = np.random.choice(n, p=probs)"
}

Tool response +13:12

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
        items = hist['items']
        weights = hist['weights']
        n = len(items)
        
        # Pick target item: sample proportional to weights
        total_w = float(weights.sum())
        if total_w > 0 and np.isfinite(total_w):
            probs = weights / total_w
            probs = np.maximum(probs, 0.0)
            probs = probs / probs.sum()
        else:
            probs = np.ones(n, dtype=np.float64) / n
        target_pos = np.random.choice(n, p=probs)
        target_item = items[target_pos]
        
        # User input: items without target_pos
        u_items = np.full(self.max_len, NUM_CATALOG, dtype=np.int64)
...

Train and evaluate initial two-tower model on validation set

+13:18

Model text

I have guarded the probability calculation with non-negative clipping and fallback to uniform sampling if the weight sum is zero.

Next step: Execute train_val.py to train and evaluate the baseline model.

Tool callrun_shell_commandTrain and evaluate initial two-tower model on validation set Returned

Tool call · Input +13:18

{
  "description": "Train and evaluate initial two-tower model on validation set",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +14:37

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
History built for 8456 users.
Initializing models...
Starting training...
Epoch 1 [100/292] Loss: 6.3694
Epoch 1 [200/292] Loss: 6.3064
Epoch 1 completed in 17.28s, Avg Loss: 6.2835
Epoch 2 [100/292] Loss: 6.2277
Epoch 2 [200/292] Loss: 6.2228
Epoch 2 completed in 17.41s, Avg Loss: 6.2179
Epoch 3 [100/292] Loss: 6.1884
Epoch 3 [200/292] Loss: 6.1731
Epoch 3 completed in 17.14s, Avg Loss: 6.1577
Evaluating on public validation set...
Precomputing item embeddings...
Computing user embeddings...
Computing inner products...
Applying validation candidate eligibility mask...
Retrieving top 100 candidates per user...
Formatting validation ranking...
Validation ranking written to /app/solution/validation.parquet
Validation score report: {
  "quality_ndcg@10": 0.00247,
  "quality_users": 4263
}
Process Group PGID: 2409
</untrusted_context>

Inspect popularity distribution of baseline predictions

+20:34

Recorded reasoning summary 46 words

Diagnosing Poor Initial Performance

My current focus is understanding the dismal 0.00247 NDCG@10 from the initial two-tower model. It's particularly perplexing given the pipeline's smooth 50-second run. I'm now digging deep to pinpoint the exact cause of this underperformance, especially when compared to the popularity baseline.

Recorded reasoning summary 46 words

Investigating Learning Plateaus

The loss is barely budging from the random guess level. I've discovered that the UserTower and ItemTower possess entirely separate item embedding tables, meaning their learned representations of items are completely independent. This lack of shared parameters is a critical area for investigation.

Recorded reasoning summary 50 words

Sharing Item Embeddings

I've realized that with separate item embedding tables, each item is seen only a handful of times per epoch. Sharing the base item embedding table between the user and item towers seems like a standard and crucial step to align their semantic spaces and drive meaningful learning.

Recorded reasoning summary 47 words

Testing Shared Embeddings

I'm currently verifying the hypothesis that sharing the item embedding table between the user and item towers will improve performance. This seems like a fundamental fix given the current independence of their learned representations. I'm also considering adjustments to learning rate and optimizer choices.

Tool callrun_shell_commandInspect popularity distribution of baseline predictions Returned

Tool call · Input +20:34

{
  "description": "Inspect popularity distribution of baseline predictions",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_df = pd.read_parquet('/app/solution/validation.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\npredicted_items = val_df['item_id'].map(item_pop)\nprint('Predicted items popularity rank distribution:')\nprint(predicted_items.describe())\n\n# Check how many unique items were recommended across all 8456 users\nprint('Unique items recommended in top 100:', val_df['item_id'].nunique())\n\""
}

Tool response +20:36

<untrusted_context>
Output: Predicted items popularity rank distribution:
count    845600.000000
mean      13483.954186
std        9531.562323
min           1.000000
25%        4004.750000
50%       13555.000000
75%       21926.000000
max       30000.000000
Name: item_id, dtype: float64
Unique items recommended in top 100: 22332
Process Group PGID: 2486
</untrusted_context>

Test shared embedding learning dynamics on clustered data

+20:45

Recorded reasoning summary 49 words

Analyzing Model Performance

I've identified that my model initially recommended items almost randomly, with a high average popularity rank. This happened because the two towers had just started learning, and I suspect the use of separate embedding tables is a significant factor contributing to this lack of focused learning.

Tool callrun_shell_commandTest shared embedding learning dynamics on clustered data Returned

Tool call · Input +20:45

{
  "description": "Test shared embedding learning dynamics on clustered data",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids, weights):\n        emb = self.item_emb(item_ids)\n        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1)\n        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled = weighted / norm\n        out = self.fc(pooled)\n        return F.normalize(out, p=2, dim=-1)\n\nclass ItemTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids):\n        emb = self.item_emb(item_ids)\n        out = self.fc(emb)\n        return F.normalize(out, p=2, dim=-1)\n\nnum_items = 30000\nembed_dim = 64\noutput_dim = 64\nshared_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\nnn.init.normal_(shared_emb.weight, std=0.05)\n\nuser_tower = UserTower(shared_emb, embed_dim, output_dim)\nitem_tower = ItemTower(shared_emb, embed_dim, output_dim)\n\nparams = list(shared_emb.parameters()) + list(user_tower.fc.parameters()) + list(item_tower.fc.parameters())\noptimizer = torch.optim.Adam(params, lr=3e-3)\n\n# Test with synthetic co-occurring data: users like items in cluster C\nbatch_size = 512\nseq_len = 32\n\nfor step in range(50):\n    optimizer.zero_grad()\n    # 10 clusters of items\n    cluster = torch.randint(0, 10, (batch_size,))\n    u_items = cluster.unsqueeze(1) * 3000 + torch.randint(0, 3000, (batch_size, seq_len))\n    u_weights = torch.ones(batch_size, seq_len)\n    pos_items = cluster * 3000 + torch.randint(0, 3000, (batch_size,))\n    \n    u_vecs = user_tower(u_items, u_weights)\n    i_vecs = item_tower(pos_items)\n    \n    logits = torch.matmul(u_vecs, i_vecs.T) / 0.07\n    labels = torch.arange(batch_size)\n    loss = F.cross_entropy(logits, labels)\n    loss.backward()\n    optimizer.step()\n    if step % 10 == 0:\n        print(f'Step {step} loss: {loss.item():.4f}')\n\""
}

Tool response +20:52

<untrusted_context>
Output: Step 0 loss: 6.4045
Step 10 loss: 6.2414
Step 20 loss: 6.2390
Step 30 loss: 6.2388
Step 40 loss: 6.2376
Process Group PGID: 2505
</untrusted_context>

Test sampled negatives learning dynamics

+23:24

Recorded reasoning summary 50 words

Investigating Loss Anomaly

I'm examining the plateau in our loss at 6.238. It appears the synthetic data generation is creating significant intra-cluster bias. With batch sizes of 512 and only 10 clusters, I'm seeing an average of 51 users assigned to the same cluster, which likely explains the stalled progress.

Tool callrun_shell_commandTest sampled negatives learning dynamics Returned

Tool call · Input +23:24

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nnum_items = 30000\nembed_dim = 64\noutput_dim = 64\nshared_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\nnn.init.normal_(shared_emb.weight, std=0.05)\n\nbatch_size = 512\nseq_len = 32\nK_neg = 32 # 32 sampled negatives per positive\n\nfc_u = nn.Linear(embed_dim, output_dim)\nfc_i = nn.Linear(embed_dim, output_dim)\n\nparams = list(shared_emb.parameters()) + list(fc_u.parameters()) + list(fc_i.parameters())\noptimizer = torch.optim.Adam(params, lr=3e-3)\n\nfor step in range(50):\n    optimizer.zero_grad()\n    cluster = torch.randint(0, 10, (batch_size,))\n    u_items = cluster.unsqueeze(1) * 3000 + torch.randint(0, 3000, (batch_size, seq_len))\n    pos_items = cluster * 3000 + torch.randint(0, 3000, (batch_size,))\n    \n    # Negative items sampled from OTHER clusters\n    neg_clusters = (cluster.unsqueeze(1) + torch.randint(1, 10, (batch_size, K_neg))) % 10\n    neg_items = neg_clusters * 3000 + torch.randint(0, 3000, (batch_size, K_neg))\n    \n    # Embeddings\n    u_vecs = F.normalize(fc_u(shared_emb(u_items).mean(dim=1)), dim=-1) # (B, D)\n    i_vecs_pos = F.normalize(fc_i(shared_emb(pos_items)), dim=-1)       # (B, D)\n    i_vecs_neg = F.normalize(fc_i(shared_emb(neg_items)), dim=-1)       # (B, K_neg, D)\n    \n    pos_logits = (u_vecs * i_vecs_pos).sum(dim=-1, keepdim=True) / 0.1 # (B, 1)\n    neg_logits = torch.bmm(i_vecs_neg, u_vecs.unsqueeze(-1)).squeeze(-1) / 0.1 # (B, K_neg)\n    \n    all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_neg)\n    labels = torch.zeros(batch_size, dtype=torch.long)\n    loss = F.cross_entropy(all_logits, labels)\n    loss.backward()\n    optimizer.step()\n    if step % 10 == 0:\n        print(f'Step {step} loss: {loss.item():.4f}')\n\"",
  "description": "Test sampled negatives learning dynamics"
}

Tool response +23:30

<untrusted_context>
Output: Step 0 loss: 3.5961
Step 10 loss: 3.5229
Step 20 loss: 3.5025
Step 30 loss: 3.5121
Step 40 loss: 3.4950
Process Group PGID: 2530
</untrusted_context>

Debug gradient dynamics of two-tower on fixed batch

+23:48

Recorded reasoning summary 37 words

Analyzing Loss Divergence

I'm currently investigating why the loss isn't decreasing as expected. My focus is on examining the gradient of the normalized u_vecs, particularly considering the interplay of normalization, temperature settings, and the final linear layer.

Recorded reasoning summary 33 words

Exploring Initialization Strategies

I'm considering how pre-training or structured initialization of item embeddings, perhaps through co-occurrence data, might impact performance, contrasting this with the current random initialization which appears problematic given the scale.

Recorded reasoning summary 39 words

Investigating User Embeddings

I'm exploring how user embeddings are generated, specifically focusing on deriving them from interaction history rather than a simple ID lookup. This contrasts with a global matrix decomposition, which is explicitly forbidden due to scalability concerns.

Recorded reasoning summary 39 words

Incorporating Popularity Signals

I'm realizing the initial dot-product score lacked any sense of item popularity. I'm now investigating if the ItemTower can directly ingest features like log of positive listens and normalized popularity rank to capture this essential signal.

Recorded reasoning summary 46 words

Integrating Popularity Features

I'm now focusing on how to directly integrate item popularity signals into the ItemTower. The idea is to leverage features like 'positive_listens' and 'popularity_rank' to generate item embeddings that inherently capture popularity, potentially using a dedicated dimension in the embedding for this purpose.

Tool callrun_shell_commandDebug gradient dynamics of two-tower on fixed batch Returned

Tool call · Input +23:48

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass UserTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids, weights):\n        emb = self.item_emb(item_ids)\n        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1)\n        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled = weighted / norm\n        out = self.fc(pooled)\n        return F.normalize(out, p=2, dim=-1)\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items, embed_dim)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n    def forward(self, item_ids):\n        emb = self.item_emb(item_ids)\n        out = self.fc(emb)\n        return F.normalize(out, p=2, dim=-1)\n\ntorch.manual_seed(42)\nu_tower = UserTower(1000, 32, 32)\ni_tower = ItemTower(1000, 32, 32)\n\nopt = torch.optim.Adam(list(u_tower.parameters()) + list(i_tower.parameters()), lr=1e-3)\n\n# Say we have 10 fixed users and 10 fixed positive items\nu_items = torch.randint(0, 1000, (64, 16))\nu_weights = torch.ones(64, 16)\npos_items = torch.randint(0, 1000, (64,))\n\nfor step in range(50):\n    opt.zero_grad()\n    u = u_tower(u_items, u_weights)\n    v = i_tower(pos_items)\n    logits = torch.matmul(u, v.T) / 0.07\n    loss = F.cross_entropy(logits, torch.arange(64))\n    loss.backward()\n    opt.step()\n    if step % 10 == 0:\n        print(f'Step {step} loss: {loss.item():.4f}, pos logit: {logits.diag().mean().item():.3f}, neg logit: {logits[~torch.eye(64, dtype=bool)].mean().item():.3f}')\n\"",
  "description": "Debug gradient dynamics of two-tower on fixed batch"
}

Tool response +23:54

<untrusted_context>
Output: Step 0 loss: 6.0898, pos logit: 0.369, neg logit: 0.191
Step 10 loss: 3.3801, pos logit: 2.282, neg logit: 0.455
Step 20 loss: 2.1030, pos logit: 3.678, neg logit: 0.317
Step 30 loss: 1.1787, pos logit: 5.474, neg logit: 0.304
Step 40 loss: 0.5515, pos logit: 7.173, neg logit: 0.145
Process Group PGID: 2549
</untrusted_context>

Check volume of organic interactions in last 30 days

+24:14

Recorded reasoning summary 38 words

Confirming Model Effectiveness

It's exciting to see the loss plummet on a fixed batch, validating our architecture and loss function's learning capabilities. My current focus is pinpointing why that rapid improvement didn't materialize in our previous script's execution.

Recorded reasoning summary 40 words

Investigating Data Sampling

My focus has shifted to scrutinizing the data sampling strategy. It appears previous iterations might have introduced significant variance by repeatedly sampling diverse target items per user, potentially hindering consistent item exposure and model convergence across epochs.

Recorded reasoning summary 36 words

Refining Training Data

I'm re-examining how training items are selected. The current approach might be diluting the signal by averaging diverse historical items when predicting a specific target, potentially leading to noisy gradients and slower convergence.

Recorded reasoning summary 36 words

Exploring User-Item Interactions

I'm now analyzing how users actually interact with music to better inform the two-tower model. Specifically, I'm investigating if incorporating item features like embeddings, alongside traditional collaborative filtering insights, can improve prediction accuracy.

Recorded reasoning summary 39 words

Integrating User Features

I'm exploring how to enrich the user tower with available features like recent/history listens and organic listen ratios. This could provide a more robust representation, especially when paired with item features, to better capture user preferences.

Recorded reasoning summary 39 words

Testing Data Selection

I'm now testing a hypothesis about how we select training data, specifically focusing on using actual recent organic listens and likes from the past 30 days. This seems much more dynamic than a static item bag.

Tool callrun_shell_commandCheck volume of organic interactions in last 30 days Returned

Tool call · Input +24:14

{
  "description": "Check volume of organic interactions in last 30 days",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_end = 24786800\ncutoff_30d = train_end - 30 * 86400\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(cat.item_id)\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Likes in last 30d\nlikes_30d = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', cutoff_30d)])\nlikes_30d = likes_30d[(likes_30d.uid.isin(target_set)) & (likes_30d.is_organic == 1) & (likes_30d.item_id.isin(catalog_items))]\n\n# Organic listens >= 80% in last 30d\nlistens_30d = pd.read_parquet('/task/data/train_listens.parquet',\n                             columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct'],\n                             filters=[('timestamp', '>=', cutoff_30d)])\nlistens_30d = listens_30d[(listens_30d.uid.isin(target_set)) & (listens_30d.is_organic == 1) & (listens_30d.played_ratio_pct >= 80) & (listens_30d.item_id.isin(catalog_items))]\n\nprint('Target users likes in last 30d:', len(likes_30d))\nprint('Target users organic listens >=80% in last 30d:', len(listens_30d))\nprint('Unique users with likes:', likes_30d.uid.nunique())\nprint('Unique users with organic listens:', listens_30d.uid.nunique())\n\""
}

Tool response +24:16

<untrusted_context>
Output: Target users likes in last 30d: 31940
Target users organic listens >=80% in last 30d: 1250037
Unique users with likes: 4238
Unique users with organic listens: 7301
Process Group PGID: 2562
</untrusted_context>

Count novel organic discovery events in last 60 days

+24:31

Recorded reasoning summary 61 words

Analyzing Novel Engagement

I'm currently dissecting the recent listener data, specifically focusing on novel catalog item engagement. Over the past 30 days, our target users have shown significant interest with 31,940 organic likes and a massive 1,250,037 organic listens at 80% completion. My next step is to isolate the listens that were to previously un-heard catalog items within this substantial dataset.

Recorded reasoning summary 58 words

Refining Predictive Strategy

I've identified a critical flaw in my previous approach. My model was over-indexed on familiar tracks due to the training data. This led to poor performance as those familiar tracks were masked during evaluation, forcing selection of irrelevant items. I'm now adjusting the training data to focus solely on novel item engagement to rectify this.

Recorded reasoning summary 43 words

Validating Novelty Focus

I'm confirming a critical hypothesis: my model was evaluated on items it wasn't trained to predict. The current strategy of training exclusively on novel discoveries directly addresses this. I'm now building the training pairs for this new approach right away.

Tool callrun_shell_commandCount novel organic discovery events in last 60 days Returned

Tool call · Input +24:31

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_end = 24786800\nday = 86400\nt_start = train_end - 60 * day\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\n# Prior listens before t_start\nprint('Reading prior listens...')\nprior_listens = pd.read_parquet('/task/data/train_listens.parquet',\n                               columns=['uid', 'item_id'],\n                               filters=[('timestamp', '<', t_start)])\nprior_listens = prior_listens[prior_listens.uid.isin(target_set)]\nprior_pairs = set(zip(prior_listens.uid, prior_listens.item_id))\nprint('Prior user-item pairs:', len(prior_pairs))\n\n# Events in last 60 days\nprint('Reading recent events...')\nlikes_60d = pd.read_parquet('/task/data/train_likes.parquet',\n                            filters=[('timestamp', '>=', t_start)])\nlikes_60d = likes_60d[(likes_60d.uid.isin(target_set)) & (likes_60d.is_organic == 1) & (likes_60d.item_id.isin(cat_items))]\n\nlistens_60d = pd.read_parquet('/task/data/train_listens.parquet',\n                              columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n                              filters=[('timestamp', '>=', t_start)])\nlistens_60d = listens_60d[(listens_60d.uid.isin(target_set)) & (listens_60d.is_organic == 1) & (listens_60d.played_ratio_pct >= 80) & (listens_60d.item_id.isin(cat_items))]\n\n# Check novel events\nnovel_likes = []\nfor u, t, i in zip(likes_60d.uid, likes_60d.timestamp, likes_60d.item_id):\n    if (u, i) not in prior_pairs:\n        novel_likes.append((u, t, i, 3)) # grade 3\n\nnovel_listens = []\n# Drop duplicates per (u, i) in listens_60d keeping first timestamp\nfirst_listens = listens_60d.sort_values('timestamp').drop_duplicates(subset=['uid', 'item_id'])\nfor u, t, i in zip(first_listens.uid, first_listens.timestamp, first_listens.item_id):\n    if (u, i) not in prior_pairs:\n        novel_listens.append((u, t, i, 1)) # grade 1\n\nprint('Novel organic likes in last 60d:', len(novel_likes))\nprint('Novel organic listens >=80% in last 60d:', len(novel_listens))\n\"",
  "description": "Count novel organic discovery events in last 60 days"
}

Tool response +24:52

<untrusted_context>
Output: Reading prior listens...
Prior user-item pairs: 9322874
Reading recent events...
Novel organic likes in last 60d: 52322
Novel organic listens >=80% in last 60d: 353486
Process Group PGID: 2583
</untrusted_context>

Verify normalized popularity score calculation

+25:29

Recorded reasoning summary 51 words

Analyzing Discovery Metrics

I'm currently processing significant user engagement data. Over the past 60 days, I've identified over 52,000 new organic likes and more than 353,000 new organic listens, totaling over 400,000 genuine discovery events. My next step is to analyze the correlation between likes and listens for specific user-item pairings.

Recorded reasoning summary 61 words

Modeling User Tastes

I'm refining the approach by focusing on user history prior to discovery. I'll represent each user by a taste profile derived from their past listens and likes. The model will then predict newly discovered items based on this profile, using both positive and negative examples within batches. I'm also investigating the impact of item popularity bias on performance.

Recorded reasoning summary 59 words

Adjusting Popularity Bias

I'm re-evaluating how item popularity influences my scoring. The current cosine similarity method can favor less popular but contextually relevant items over widespread hits, even when baseline probabilities differ vastly. I'm now considering allowing item vectors to have unconstrained norms or incorporating an explicit popularity dimension to better reflect real-world listen counts and address potential bias.

Recorded reasoning summary 44 words

Incorporating Bias Factors

I've confirmed that user and item biases can be elegantly incorporated by extending the dimensionality of our vectors. This allows the model to precisely capture both latent interactions and individual biases through a unified dot product, compatible with existing MIPS indexing.

Recorded reasoning summary 55 words

Refining Relevance Scoring

I'm now focusing on the final relevance score, which will be the dot product of user and item vectors. These vectors will be generated by separate trainable neural networks. The user representation will come from interaction history, not just IDs. I'm also exploring how item biases, like positive listens, can be integrated.

Tool callrun_shell_commandVerify normalized popularity score calculation Returned

Tool call · Input +25:29

{
  "description": "Verify normalized popularity score calculation",
  "command": "PYTHONPATH=/task python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\ndata_dir = Path('/task/data')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_log_pop = np.log1p(cat.positive_listens.to_numpy())\n# Normalize log pop to [0, 1]\nnorm_pop = (item_log_pop - item_log_pop.min()) / (item_log_pop.max() - item_log_pop.min())\n\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\n\n# Score using pure popularity first\nscores = np.tile(norm_pop, (len(target_uids), 1))\nscores[unpacked_mask == 0] = -1e9\n\ntop100_indices = np.argpartition(-scores, 100, axis=1)[:, :100]\n# Sort top 100\nfor r in range(len(target_uids)):\n    row_scores = scores[r, top100_indices[r]]\n    order = np.argsort(-row_scores)\n    top100_indices[r] = top100_indices[r][order]\n\nrecords = []\ncat_items = cat.item_id.to_numpy()\nfor r, uid in enumerate(target_uids):\n    for rank, idx in enumerate(top100_indices[r], 1):\n        records.append((uid, cat_items[idx], rank))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nval_df = validate_ranking(df, target_set)\ngraded = validation_truth(eligibility, target_set)\nprint('Norm pop score:', score(ranked_items(val_df), graded))\n\""
}

Tool response +25:39

<untrusted_context>
Output: Norm pop score: {'quality_ndcg@10': 0.005441, 'quality_users': 4263}
Process Group PGID: 2610
</untrusted_context>

Inspect head and tail of candidate catalog

+28:03

Tool callrun_shell_commandInspect head and tail of candidate catalog Returned

Tool call · Input +28:03

{
  "command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.head(10))\nprint(cat.tail(5))\n\"",
  "description": "Inspect head and tail of candidate catalog"
}

Tool response +28:04

<untrusted_context>
Output: item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
3  3542184             24075                     17699                4
4  9378983             24036                     18195                5
5  5463340             22746                     17398                6
6  8213481             22505                     17071                7
7   906358             22238                     17183                8
8  2859641             18380                     13704                9
9  3033749             16682                     12267               10
       item_id  positive_listens  organic_positive_listens  popularity_rank
29995  1275692               163                        81            29996
29996  1280011               163                        76            29997
29997  1309246               163                       138            29998
29998  1634958               163                        46            29999
29999  1670524               163                        73            30000
Process Group PGID: 2648
</untrusted_context>

Inspect item user counts in likes

+28:43

Recorded reasoning summary 59 words

Refining Tie-Breaking Logic

I'm currently addressing an issue where my tie-breaking mechanism for the popularity rank was unexpectedly altered by np.argpartition due to float rounding. The initial sorting by positive listens, with organic positive listens as the secondary criterion for ranks 1-30000, is now being re-evaluated to ensure consistent ordering, especially concerning the raw catalog order of items 0-29999.

Tool callrun_shell_commandInspect item user counts in likes Returned

Tool call · Input +28:43

{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's check co-occurrence of likes or organic listens\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid)\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(target_set)]\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat.item_id)\nlikes = likes[likes.item_id.isin(cat_items)]\n\n# Check number of users per item in likes\nitem_user_counts = likes.groupby('item_id')['uid'].nunique()\nprint('Item user count in likes describe:')\nprint(item_user_counts.describe())\n\"",
  "description": "Inspect item user counts in likes"
}

Tool response +28:45

<untrusted_context>
Output: Item user count in likes describe:
count    28606.000000
mean        15.974131
std         26.567869
min          1.000000
25%          4.000000
50%          8.000000
75%         17.000000
max        661.000000
Name: uid, dtype: float64
Process Group PGID: 2671
</untrusted_context>

Test shared embedding two-tower training dynamics

+29:06

Recorded reasoning summary 47 words

Analyzing Data Distributions

It seems most of the items are indeed present within the train_likes dataset for our target users. Interestingly, the train_listens dataset shows significantly higher occurrences for all items. I'm now shifting focus to understanding how a two-tower neural network architecture might fit into this.

Recorded reasoning summary 43 words

Investigating Embeddings

My initial experiment with separate random embeddings for user and item towers yielded near-random performance. Analyzing the input and target item selection within the batch suggests the averaging of historical items led to embeddings too close to the origin, hindering differentiation.

Recorded reasoning summary 57 words

Refining User-Item Interaction

The normalization by 127 renders pooled vectors near noise. With many users, pooled vectors resemble a global mean, leading to random user vector directions after processing. Similarly, separate embedding tables cause item vectors to also be random. Gradient updates only connect towers via the dot product, with items appearing infrequently as targets, limiting learning.

Recorded reasoning summary 55 words

Aligning User-Item Spaces

I've realized the current setup prevents user and item towers from converging because item embeddings aren't linked. My current thinking is that sharing item embeddings between towers, or having the user tower construct user vectors by aggregating item vectors, will directly align their representation spaces. This should allow for natural similarity matching.

Recorded reasoning summary 41 words

Testing Network Components

I'm currently exploring how the user tower can effectively pool item tower outputs. The goal is to ensure the user and item embeddings can be computed independently for efficient retrieval. I'll be testing this approach in Python soon.

Recorded reasoning summary 48 words

Sharing Embeddings to Unify

My current focus is on how the user tower can effectively pool outputs from the item tower. I'm considering a shared item_emb between both towers to directly align their representation spaces, allowing for direct similarity matching. This should enable independent computation and efficient retrieval.

Tool callrun_shell_commandTest shared embedding two-tower training dynamics Returned

Tool call · Input +29:06

{
  "description": "Test shared embedding two-tower training dynamics",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nnum_items = 1000\nembed_dim = 64\noutput_dim = 64\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items, embed_dim)\n        self.proj = nn.Linear(embed_dim, output_dim)\n    def forward(self, item_ids):\n        return F.normalize(self.proj(self.item_emb(item_ids)), dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.net = nn.Sequential(\n            nn.Linear(embed_dim, output_dim),\n            nn.LayerNorm(output_dim),\n            nn.GELU(),\n            nn.Linear(output_dim, output_dim)\n        )\n    def forward(self, item_ids, weights):\n        emb = self.item_emb(item_ids) # (B, L, D)\n        weighted = (emb * weights.unsqueeze(-1)).sum(dim=1)\n        norm = weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled = weighted / norm\n        return F.normalize(self.net(pooled), dim=-1)\n\nitem_tower = ItemTower(num_items, embed_dim, output_dim)\nuser_tower = UserTower(item_tower.item_emb, embed_dim, output_dim)\n\nopt = torch.optim.Adam(list(item_tower.parameters()) + list(user_tower.parameters()), lr=1e-3)\n\n# Say we have 100 users, each likes items from 1 of 5 genres\ntorch.manual_seed(42)\nfor step in range(50):\n    opt.zero_grad()\n    genre = torch.randint(0, 5, (64,))\n    # Users have 10 items from their genre\n    u_items = genre.unsqueeze(1) * 200 + torch.randint(0, 200, (64, 10))\n    u_weights = torch.ones(64, 10)\n    # Target item is also from their genre\n    pos_items = genre * 200 + torch.randint(0, 200, (64,))\n    \n    u = user_tower(u_items, u_weights)\n    v = item_tower(pos_items)\n    \n    logits = torch.matmul(u, v.T) / 0.1\n    loss = F.cross_entropy(logits, torch.arange(64))\n    loss.backward()\n    opt.step()\n    if step % 10 == 0:\n        print(f'Step {step} loss: {loss.item():.4f}')\n\""
}

Tool response +29:12

<untrusted_context>
Output: /opt/task-venv/lib/python3.12/site-packages/torch/_compile.py:53: UserWarning: optimizer contains a parameter group with duplicate parameters; in future, this will cause an error; see github.com/pytorch/pytorch/issues/40967 for more information
  return disable_fn(*args, **kwargs)
Step 0 loss: 4.8293
Step 10 loss: 4.5612
Step 20 loss: 4.5963
Step 30 loss: 4.6001
Step 40 loss: 4.2679
Process Group PGID: 2690
</untrusted_context>

Test convergence with sampled negatives loss

+29:32

Recorded reasoning summary 39 words

Observing Learning Progress

The initial loss shows a promising downward trend. However, a critical warning about duplicate parameters in the optimizer has emerged, likely due to item_tower.item_emb being added twice. This requires immediate attention to ensure correct gradient updates.

Recorded reasoning summary 57 words

Investigating Embedding Initialization

I've identified a crucial issue with duplicate parameters in the optimizer, specifically with item_tower.item_emb appearing twice. I'm now exploring initialization strategies for these item embeddings, considering approaches like Item2Vec, which treats user histories as "sentences" and tracks as "words." This method learns embeddings based on item co-occurrence, potentially capturing genre, artist, and mood similarities.

Recorded reasoning summary 46 words

Exploring Embedding Methods

I've realized the Item2Vec approach offers a way to learn embeddings based on item co-occurrence, which is exciting. However, given the scale of millions of items, I'm now considering how to efficiently compute and store item-item similarity or co-occurrence matrices within memory constraints.

Recorded reasoning summary 43 words

Refining Interaction Handling

I'm prioritizing efficient handling of user-item interactions without constructing a large global matrix, as that's infeasible for production latency. My focus is now on offline computation of candidate item embeddings and their storage in a nearest-neighbor index for swift retrieval.

Recorded reasoning summary 41 words

Analyzing Negative Sampling

I'm re-evaluating the loss function's denominator and considering the impact of in-batch negatives. It appears that if users in a batch share preferences, particularly for popular tracks or genres, the learning could be hindered by redundant negative samples.

Recorded reasoning summary 60 words

Addressing False Negatives

I've identified a significant problem with false negatives in my in-batch negative sampling strategy. When users in a batch share similar tastes, the model mistakenly penalizes items the user actually likes. I'm now exploring catalog sampling for negatives, which offers a much lower chance of including false negatives, and allows for a higher number of sampled negatives.

Tool callrun_shell_commandTest convergence with sampled negatives loss Returned

Tool call · Input +29:32

{
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n# Test sampled softmax loss on real user-positive pairs\n# Let's verify gradient and convergence\nnum_items = 1000\nembed_dim = 32\nbatch_size = 256\nK_neg = 64\n\nemb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\nfc_u = nn.Sequential(nn.Linear(embed_dim, embed_dim), nn.LayerNorm(embed_dim), nn.GELU(), nn.Linear(embed_dim, embed_dim))\nfc_i = nn.Sequential(nn.Linear(embed_dim, embed_dim), nn.LayerNorm(embed_dim), nn.GELU(), nn.Linear(embed_dim, embed_dim))\n\nopt = torch.optim.Adam(list(emb.parameters()) + list(fc_u.parameters()) + list(fc_i.parameters()), lr=3e-3)\n\n# 10 clusters\ncluster = torch.randint(0, 10, (batch_size,))\nu_items = cluster.unsqueeze(1) * 100 + torch.randint(0, 100, (batch_size, 16))\npos_items = cluster * 100 + torch.randint(0, 100, (batch_size,))\n\nfor step in range(30):\n    opt.zero_grad()\n    # Random catalog negatives\n    neg_items = torch.randint(0, num_items, (batch_size, K_neg))\n    \n    # User vectors\n    u_raw = emb(u_items).mean(dim=1)\n    u_vec = F.normalize(fc_u(u_raw), dim=-1) # (B, D)\n    \n    # Pos and neg item vectors\n    pos_vec = F.normalize(fc_i(emb(pos_items)), dim=-1) # (B, D)\n    neg_vec = F.normalize(fc_i(emb(neg_items)), dim=-1) # (B, K_neg, D)\n    \n    # Scores\n    pos_score = (u_vec * pos_vec).sum(dim=-1, keepdim=True) / 0.1 # (B, 1)\n    neg_score = torch.bmm(neg_vec, u_vec.unsqueeze(-1)).squeeze(-1) / 0.1 # (B, K_neg)\n    \n    logits = torch.cat([pos_score, neg_score], dim=1) # (B, 1 + K_neg)\n    labels = torch.zeros(batch_size, dtype=torch.long)\n    \n    loss = F.cross_entropy(logits, labels)\n    loss.backward()\n    opt.step()\n    \n    if step % 5 == 0:\n        acc = (logits.argmax(dim=1) == 0).float().mean().item()\n        print(f'Step {step}: Loss = {loss.item():.4f}, Top-1 Acc = {acc:.3f}, Pos score = {pos_score.mean().item():.2f}, Neg score = {neg_score.mean().item():.2f}')\n\"",
  "description": "Test convergence with sampled negatives loss"
}

Tool response +29:38

<untrusted_context>
Output: Step 0: Loss = 5.3662, Top-1 Acc = 0.000, Pos score = 0.08, Neg score = 0.10
Step 5: Loss = 3.8663, Top-1 Acc = 0.078, Pos score = 1.69, Neg score = 0.81
Step 10: Loss = 3.2373, Top-1 Acc = 0.238, Pos score = 2.39, Neg score = 0.89
Step 15: Loss = 2.7007, Top-1 Acc = 0.348, Pos score = 3.00, Neg score = 0.75
Step 20: Loss = 2.1184, Top-1 Acc = 0.527, Pos score = 3.86, Neg score = 0.54
Step 25: Loss = 1.5314, Top-1 Acc = 0.676, Pos score = 4.81, Neg score = 0.24
Process Group PGID: 2703
</untrusted_context>

update_topic command

+29:50

Tool callupdate_topicTool call Returned

Tool call · Input +29:50

{
  "title": "Sampled Negatives & Feature-Enhanced Two-Tower",
  "strategic_intent": "Implement sampled negative cross-entropy and item catalog features in the two-tower model and evaluate on validation.",
  "summary": "Diagnosed why initial in-batch negative loss struggled: false negative collisions on overlapping user tastes and ungrounded embeddings. Replacing with sampled catalog negative cross-entropy and adding item catalog features (positive listens, organic listens, popularity rank) into the ItemTower. We will train this architecture on organic likes and listens and measure public validation NDCG@10."
}

Tool response +29:52

Current topic: "Sampled Negatives & Feature-Enhanced Two-Tower"
Topic summary: Diagnosed why initial in-batch negative loss struggled: false negative collisions on overlapping user tastes and ungrounded embeddings. Replacing with sampled catalog negative cross-entropy and adding item catalog features (positive listens, organic listens, popularity rank) into the ItemTower. We will train this architecture on organic likes and listens and measure public validation NDCG@10.

Strategic Intent: Implement sampled negative cross-entropy and item catalog features in the two-tower model and evaluate on validation.

Benchmark batch-shared negative sampling throughput

+30:07

Recorded reasoning summary 42 words

Investigating Interaction Data

I'm currently sifting through the dataset, specifically focusing on how to define positive training samples. My immediate task involves analyzing the 'train_likes' and 'train_listens' data, with a particular emphasis on 'organic listens' constituting at least 80% of the total.

Recorded reasoning summary 54 words

Defining Training Data

I've determined that approximately 400,000 positive interactions (likes and organic listens) within the last 60 days will form my training dataset. Each training event will be a tuple $(u, i^+)$, with weights assigned to likes and listens. I'm now defining how to mask historical items and sample negative items for training.

Recorded reasoning summary 51 words

Optimizing Negative Sampling

I've realized a significant efficiency improvement. Instead of sampling negative items per user, I can sample a fixed set of negative items for the entire batch. This dramatically reduces computation for the item tower, allowing for a faster, elegant matrix multiplication for calculating negative scores and subsequent logits.

Tool callrun_shell_commandBenchmark batch-shared negative sampling throughput Returned

Tool call · Input +30:07

{
  "description": "Benchmark batch-shared negative sampling throughput",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nembed_dim = 64\noutput_dim = 64\nbatch_size = 256\nK_neg = 128\nseq_len = 64\n\n# Dummy towers\nemb = nn.Embedding(30001, embed_dim, padding_idx=30000)\nfc_u = nn.Linear(embed_dim, output_dim)\nfc_i = nn.Linear(embed_dim, output_dim)\n\nopt = torch.optim.Adam(list(emb.parameters()) + list(fc_u.parameters()) + list(fc_i.parameters()), lr=1e-3)\ntorch.set_num_threads(8)\n\nt0 = time.time()\nnum_steps = 100\nfor step in range(num_steps):\n    opt.zero_grad()\n    u_items = torch.randint(0, 30000, (batch_size, seq_len))\n    u_weights = torch.rand(batch_size, seq_len)\n    pos_items = torch.randint(0, 30000, (batch_size,))\n    neg_items = torch.randint(0, 30000, (K_neg,))\n    \n    u_raw = (emb(u_items) * u_weights.unsqueeze(-1)).sum(dim=1) / u_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n    u_vec = F.normalize(fc_u(u_raw), dim=-1)\n    \n    pos_vec = F.normalize(fc_i(emb(pos_items)), dim=-1)\n    neg_vec = F.normalize(fc_i(emb(neg_items)), dim=-1)\n    \n    pos_logits = (u_vec * pos_vec).sum(dim=-1, keepdim=True) / 0.1 # (B, 1)\n    neg_logits = torch.matmul(u_vec, neg_vec.T) / 0.1               # (B, K_neg)\n    \n    logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_neg)\n    labels = torch.zeros(batch_size, dtype=torch.long)\n    loss = F.cross_entropy(logits, labels)\n    loss.backward()\n    opt.step()\n\nelapsed = time.time() - t0\nsamples = num_steps * batch_size\nprint(f'100 steps ({samples} samples) took {elapsed:.2f}s -> {samples/elapsed:.1f} samples/sec')\n\""
}

Tool response +30:14

<untrusted_context>
Output: 100 steps (25600 samples) took 1.05s -> 24432.1 samples/sec
Process Group PGID: 2722
</untrusted_context>

write_file train_val.py

+30:56

Recorded reasoning summary 46 words

Accelerating Evaluation Speed

My recent focus has been on the performance improvements derived from the substantial sampling rate achieved. I'm now deeply considering the optimal input design for the ItemTower component, ensuring it efficiently processes the vast amount of data generated by this rapid evaluation pipeline.

Recorded reasoning summary 36 words

Designing Input Features

I'm refining the ItemTower by normalizing log-transformed listen counts and popularity rank for continuous features. For the UserTower, I'm exploring incorporating liked items, high-completion organic listens, and disliked items with weighted recency decay.

Recorded reasoning summary 38 words

Refining User Embeddings

I'm now focused on constructing robust user vectors by pooling embeddings from positive and negative item interactions. I'm experimenting with different pooling strategies and concatenation methods to maximize the discriminative power of the user representation.

Tool callwrite_file/app/solution/train_val.py Returned

Tool call · Input +30:56

{
  "content": "import time\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA_DIR = Path('/task/data')\nTRAIN_END = 24786800\nDAY_SEC = 86400\nNUM_CATALOG = 30000\nDEVICE = torch.device('cpu')\n\ntorch.set_num_threads(8)\ntorch.manual_seed(42)\nnp.random.seed(42)\n\nprint(\"Loading metadata...\")\ncat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\nidx2item = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet(DATA_DIR / 'target_users.parquet')\ntarget_uids = targets.uid.astype(int).to_numpy()\ntarget_set = set(target_uids)\nuid2idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f\"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}\")\n\n# Prepare Item Features from candidate catalog\nprint(\"Preparing item features...\")\ncat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)\n\n# Standardize features\nfeat_mean = cat_features.mean(axis=0, keepdims=True)\nfeat_std = cat_features.std(axis=0, keepdims=True) + 1e-6\ncat_features = (cat_features - feat_mean) / feat_std\ncat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)\n\n# 1. Load active explicit preference state at validation cutoff\nprint(\"Loading validation preference state...\")\nval_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked']\npref_dislikes = val_pref[val_pref.state == 'disliked']\n\nuser_pref_likes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_likes.uid, pref_likes.item_id):\n    if uid in user_pref_likes and item in item2idx:\n        user_pref_likes[uid].add(item2idx[item])\n\nuser_pref_dislikes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):\n    if uid in user_pref_dislikes and item in item2idx:\n        user_pref_dislikes[uid].add(item2idx[item])\n\n# 2. Load recent positive listens (last 90 days before cutoff)\nprint(\"Loading recent listens...\")\ncutoff_90d = TRAIN_END - 90 * DAY_SEC\nlistens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]\n)\nlistens = listens[listens.uid.isin(target_set) & (listens.played_ratio_pct >= 50)]\nlistens['item_idx'] = listens.item_id.map(item2idx)\nlistens = listens.dropna(subset=['item_idx'])\nlistens['item_idx'] = listens['item_idx'].astype(int)\n\n# Group listens per user\nprint(\"Aggregating user histories...\")\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)\nlistens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))\n\n# Pre-aggregate per user\nMAX_HIST_LEN = 128\nMAX_DISLIKE_LEN = 32\n\nuser_profiles = {}\n\nfor uid, group in listens.groupby('uid'):\n    item_weights = {}\n    for idx in user_pref_likes.get(uid, ()):\n        item_weights[idx] = 4.0\n        \n    for row in group.itertuples(index=False):\n        idx = row.item_idx\n        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight\n        item_weights[idx] = item_weights.get(idx, 0.0) + w\n        \n    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]\n    pos_items = [x[0] for x in sorted_items]\n    pos_weights = [x[1] for x in sorted_items]\n    \n    dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n    \n    user_profiles[uid] = {\n        'pos_items': np.array(pos_items, dtype=np.int64),\n        'pos_weights': np.array(pos_weights, dtype=np.float32),\n        'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n    }\n\nfor uid in target_uids:\n    if uid not in user_profiles:\n        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]\n        p_weights = [4.0] * len(p_likes) if p_likes else [1.0]\n        p_items = p_likes if p_likes else [0]\n        dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n        user_profiles[uid] = {\n            'pos_items': np.array(p_items, dtype=np.int64),\n            'pos_weights': np.array(p_weights, dtype=np.float32),\n            'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n        }\n\nprint(f\"User profiles built for {len(user_profiles)} users.\")\n\n# 3. Build training dataset of positive discovery pairs in last 60 days\nprint(\"Extracting training interaction pairs...\")\nt_train_start = TRAIN_END - 60 * DAY_SEC\n\n# Organic likes in last 60 days\ntrain_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet', filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)])\ntrain_likes = train_likes[train_likes.uid.isin(target_set) & (train_likes.is_organic == 1)]\ntrain_likes['item_idx'] = train_likes.item_id.map(item2idx)\ntrain_likes = train_likes.dropna(subset=['item_idx'])\ntrain_likes['item_idx'] = train_likes['item_idx'].astype(int)\n\n# Organic listens in last 60 days\ntrain_listens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)]\n)\ntrain_listens = train_listens[train_listens.uid.isin(target_set) & (train_listens.is_organic == 1) & (train_listens.played_ratio_pct >= 80)]\ntrain_listens['item_idx'] = train_listens.item_id.map(item2idx)\ntrain_listens = train_listens.dropna(subset=['item_idx'])\ntrain_listens['item_idx'] = train_listens['item_idx'].astype(int)\n\n# Deduplicate into pairs with grade\npair_dict = {} # (uid, item_idx) -> grade\nfor uid, idx in zip(train_listens.uid, train_listens.item_idx):\n    pair_dict[(int(uid), int(idx))] = 1\n\nfor uid, idx in zip(train_likes.uid, train_likes.item_idx):\n    pair_dict[(int(uid), int(idx))] = 3 # like takes precedence\n\ntrain_samples = []\nfor (uid, item_idx), grade in pair_dict.items():\n    # Weight: 3.0 for like, 1.0 for listen\n    w = 3.0 if grade == 3 else 1.0\n    train_samples.append((uid, item_idx, w))\n\nprint(f\"Total training pairs: {len(train_samples)}\")\n\n# Convert profiles to fixed-size numpy arrays for fast batch indexing\nuser_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)\nuser_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)\nuser_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)\nuser_neg_weights = np.zeros((len(target_uids), MAX_DISLIKE_LEN), dtype=np.float32)\n\nfor uid, idx in uid2idx.items():\n    prof = user_profiles[uid]\n    p_len = min(len(prof['pos_items']), MAX_HIST_LEN)\n    user_pos_items[idx, :p_len] = prof['pos_items'][:p_len]\n    user_pos_weights[idx, :p_len] = prof['pos_weights'][:p_len]\n    \n    n_len = min(len(prof['neg_items']), MAX_DISLIKE_LEN)\n    if n_len > 0:\n        user_neg_items[idx, :n_len] = prof['neg_items'][:n_len]\n        user_neg_weights[idx, :n_len] = 1.0\n\nuser_pos_items_t = torch.from_numpy(user_pos_items)\nuser_pos_weights_t = torch.from_numpy(user_pos_weights)\nuser_neg_items_t = torch.from_numpy(user_neg_items)\nuser_neg_weights_t = torch.from_numpy(user_neg_weights)\n\n# Model definitions\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, feat_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        nn.init.normal_(self.item_emb.weight, std=0.05)\n        self.feat_proj = nn.Linear(feat_dim, 16)\n        \n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim + 16, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        \n    def forward(self, item_ids, feats):\n        # item_ids: (N,), feats: (N, feat_dim)\n        emb = self.item_emb(item_ids)\n        feat_emb = self.feat_proj(feats)\n        combined = torch.cat([emb, feat_emb], dim=-1)\n        out = self.fc(combined)\n        return F.normalize(out, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim * 2, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        \n    def forward(self, pos_items, pos_weights, neg_items, neg_weights):\n        # pos_items: (B, L_pos), pos_weights: (B, L_pos)\n        pos_emb = self.item_emb(pos_items) # (B, L_pos, D)\n        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)\n        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled_pos = w_pos / w_pos_norm\n        \n        # neg_items: (B, L_neg), neg_weights: (B, L_neg)\n        neg_emb = self.item_emb(neg_items)\n        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)\n        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()\n        pooled_neg = (w_neg / w_neg_norm) * has_neg\n        \n        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)\n        out = self.fc(combined)\n        return F.normalize(out, p=2, dim=-1)\n\nclass FastDataset(Dataset):\n    def __init__(self, samples, uid2idx):\n        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)\n        self.target_items = np.array([s[1] for s in samples], dtype=np.int64)\n        self.weights = np.array([s[2] for s in samples], dtype=np.float32)\n        \n    def __len__(self):\n        return len(self.u_indices)\n        \n    def __getitem__(self, idx):\n        return self.u_indices[idx], self.target_items[idx], self.weights[idx]\n\nEMBED_DIM = 64\nOUTPUT_DIM = 64\nFEAT_DIM = 3\nBATCH_SIZE = 256\nK_NEG = 128\nLR = 2e-3\nTEMPERATURE = 0.08\nEPOCHS = 4\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM).to(DEVICE)\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\noptimizer = torch.optim.Adam(\n    list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n],\n    lr=LR,\n    weight_decay=1e-5\n)\n\n# Also train item_emb directly\noptimizer.add_param_group({'params': item_tower.item_emb.parameters(), 'lr': LR})\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\")\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        # User history tensors\n        p_items = user_pos_items_t[u_idxs].clone() # (B, L)\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        # Leave-one-out masking: mask out target item if present in positive history\n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        # Sample K_NEG negative catalog items\n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        # User vectors\n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D)\n        \n        # Positive item vectors\n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D)\n        \n        # Negative item vectors\n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D)\n        \n        # Logits\n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 400 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\")\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\")\n\n# Public Validation Evaluation\nprint(\"Evaluating on public validation set...\")\nitem_tower.eval()\nuser_tower.eval()\n\nwith torch.no_grad():\n    # 1. Precompute candidate item embeddings for all 30000 catalog items\n    print(\"Precomputing all 30,000 item embeddings offline...\")\n    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n    item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D)\n    \n    # 2. Compute user embeddings for all 8456 target users\n    print(\"Computing user embeddings for all target users...\")\n    user_vecs = user_tower(\n        user_pos_items_t,\n        user_pos_weights_t,\n        user_neg_items_t,\n        user_neg_weights_t\n    ) # (8456, D)\n    \n    # 3. Maximum inner product search: User x Item dot product\n    print(\"MIPS dot product search...\")\n    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n    \n    # 4. Mask ineligible candidates\n    print(\"Applying validation candidate eligibility mask...\")\n    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\n    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\n    \n    scores[~mask_tensor] = -1e9\n    \n    # 5. Retrieve top 100 per user\n    print(\"Selecting top 100 per user...\")\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.cpu().numpy()\n\n# 6. Build validation ranking dataframe\nprint(\"Formatting validation ranking...\")\nrecords = []\nfor r, uid in enumerate(target_uids):\n    ranked_catalog_indices = top100_indices[r]\n    ranked_item_ids = idx2item[ranked_catalog_indices]\n    for rank, item_id in enumerate(ranked_item_ids, 1):\n        records.append((int(uid), int(item_id), rank))\n\nval_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nval_ranking_path = Path('/app/solution/validation.parquet')\nval_ranking_df.to_parquet(val_ranking_path)\nprint(f\"Validation ranking written to {val_ranking_path}\")\n\n# Run evaluate_public\ngraded = validation_truth(eligibility, target_set)\nvalidated = validate_ranking(val_ranking_df, target_set)\nranked = ranked_items(validated)\nscore_report = score(ranked, graded)\nprint(f\"\\n==========================================\")\nprint(f\"Validation score report: {json.dumps(score_report, indent=2)}\")\nprint(f\"==========================================\")\n",
  "file_path": "/app/solution/train_val.py"
}

Tool response +30:56

Successfully overwrote file: /app/solution/train_val.py. Here is the updated code:
...
DAY_SEC = 86400
NUM_CATALOG = 30000
DEVICE = torch.device('cpu')

torch.set_num_threads(8)
torch.manual_seed(42)
np.random.seed(42)

print("Loading metadata...")
cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')
item2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}
idx2item = cat.item_id.to_numpy()
...
target_set = set(target_uids)
uid2idx = {uid: idx for idx, uid in enumerate(target_uids)}

print(f"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}")

# Prepare Item Features from candidate catalog
print("Preparing item features...")
cat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)

# Standardize features
feat_mean = cat_features.mean(axis=0, keepdims=True)
feat_std = cat_features.std(axis=0, keepdims=True) + 1e-6
cat_features = (cat_features - feat_mean) / feat_std
cat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)

# 1. Load active explicit preference state at validation cutoff
print("Loading validation preference state...")
val_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')
pref_likes = val_pref[val_pref.state == 'liked']
pref_dislikes = val_pref[val_pref.state == 'disliked']
...
listens = listens.dropna(subset=['item_idx'])
listens['item_idx'] = listens['item_idx'].astype(int)

# Group listens per user
print("Aggregating user histories...")
listens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)
ts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)
listens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))

# Pre-aggregate per user
MAX_HIST_LEN = 128
MAX_DISLIKE_LEN = 32

user_profiles = {}

for uid, group in listens.groupby('uid'):
    item_weights = {}
    for idx in user_pref_likes.get(uid, ()):
        item_weights[idx] = 4.0
        
    for row in group.itertuples(index=False):
        idx = row.item_idx
        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight
        item_weights[idx] = item_weights.get(idx, 0.0) + w
        
    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]
    pos_items = [x[0] for x in sorted_items]
    pos_weights = [x[1] for x in sorted_items]
    
    dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]
    
    user_profiles[uid] = {
        'pos_items': np.array(pos_items, dtype=np.int64),
        'pos_weights': np.array(pos_weights, dtype=np.float32),
        'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)
    }

for uid in target_uids:
    if uid not in user_profiles:
        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]
        p_weights = [4.0] * len(p_likes) if p_likes else [1.0]
        p_items = p_likes if p_likes else [0]
        dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]
        user_profiles[uid] = {
            'pos_items': np.array(p_items, dtype=np.int64),
            'pos_weights': np.array(p_weights, dtype=np.float32),
            'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)
        }

print(f"User profiles built for {len(user_profiles)} users.")

# 3. Build training dataset of positive discovery pairs in last 60 days
print("Extracting training interaction pairs...")
t_train_start = TRAIN_END - 60 * DAY_SEC

# Organic likes in last 60 days
train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet', filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)])
train_likes = train_likes[train_likes.uid.isin(target_set) & (train_likes.is_organic == 1)]
train_likes['item_idx'] = train_likes.item_id.map(item2idx)
train_likes = train_likes.dropna(subset=['item_idx'])
train_likes['item_idx'] = train_likes['item_idx'].astype(int)

# Organic listens in last 60 days
train_listens = pd.read_parquet(
    DATA_DIR / 'train_listens.parquet',
    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],
    filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)]
)
train_listens = train_listens[train_listens.uid.isin(target_set) & (train_listens.is_organic == 1) & (train_listens.played_ratio_pct >= 80)]
train_listens['item_idx'] = train_listens.item_id.map(item2idx)
train_listens = train_listens.dropna(subset=['item_idx'])
train_listens['item_idx'] = train_listens['item_idx'].astype(int)

# Deduplicate into pairs with grade
pair_dict = {} # (uid, item_idx) -> grade
for uid, idx in zip(train_listens.uid, train_listens.item_idx):
    pair_dict[(int(uid), int(idx))] = 1

for uid, idx in zip(train_likes.uid, train_likes.item_idx):
    pair_dict[(int(uid), int(idx))] = 3 # like takes precedence

train_samples = []
for (uid, item_idx), grade in pair_dict.items():
    # Weight: 3.0 for like, 1.0 for listen
    w = 3.0 if grade == 3 else 1.0
    train_samples.append((uid, item_idx, w))

print(f"Total training pairs: {len(train_samples)}")

# Convert profiles to fixed-size numpy arrays for fast batch indexing
user_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)
user_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)
user_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)
user_neg_weights = np.zeros((len(target_uids), MAX_DISLIKE_LEN), dtype=np.float32)

for uid, idx in uid2idx.items():
    prof = user_profiles[uid]
    p_len = min(len(prof['pos_items']), MAX_HIST_LEN)
    user_pos_items[idx, :p_len] = prof['pos_items'][:p_len]
    user_pos_weights[idx, :p_len] = prof['pos_weights'][:p_len]
    
    n_len = min(len(prof['neg_items']), MAX_DISLIKE_LEN)
    if n_len > 0:
        user_neg_items[idx, :n_len] = prof['neg_items'][:n_len]
        user_neg_weights[idx, :n_len] = 1.0

user_pos_items_t = torch.from_numpy(user_pos_items)
user_pos_weights_t = torch.from_numpy(user_pos_weights)
user_neg_items_t = torch.from_numpy(user_neg_items)
user_neg_weights_t = torch.from_numpy(user_neg_weights)

# Model definitions
class ItemTower(nn.Module):
    def __init__(self, num_items, embed_dim, feat_dim, output_dim, dropout=0.1):
        super().__init__()
        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)
        nn.init.normal_(self.item_emb.weight, std=0.05)
        self.feat_proj = nn.Linear(feat_dim, 16)
        
        self.fc = nn.Sequential(
            nn.Linear(embed_dim + 16, embed_dim),
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        
    def forward(self, item_ids, feats):
        # item_ids: (N,), feats: (N, feat_dim)
        emb = self.item_emb(item_ids)
        feat_emb = self.feat_proj(feats)
        combined = torch.cat([emb, feat_emb], dim=-1)
        out = self.fc(combined)
        return F.normalize(out, p=2, dim=-1)

class UserTower(nn.Module):
    def __init__(self, item_emb, embed_dim, output_dim, dropout=0.1):
        super().__init__()
        self.item_emb = item_emb
        self.fc = nn.Sequential(
            nn.Linear(embed_dim * 2, embed_dim),
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        
    def forward(self, pos_items, pos_weights, neg_items, neg_weights):
        # pos_items: (B, L_pos), pos_weights: (B, L_pos)
        pos_emb = self.item_emb(pos_items) # (B, L_pos, D)
        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)
        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        pooled_pos = w_pos / w_pos_norm
        
        # neg_items: (B, L_neg), neg_weights: (B, L_neg)
        neg_emb = self.item_emb(neg_items)
        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)
        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()
        pooled_neg = (w_neg / w_neg_norm) * has_neg
        
        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)
        out = self.fc(combined)
        return F.normalize(out, p=2, dim=-1)

class FastDataset(Dataset):
    def __init__(self, samples, uid2idx):
        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)
        self.target_items = np.array([s[1] for s in samples], dtype=np.int64)
        self.weights = np.array([s[2] for s in samples], dtype=np.float32)
        
    def __len__(self):
        return len(self.u_indices)
        
    def __getitem__(self, idx):
        return self.u_indices[idx], self.target_items[idx], self.weights[idx]

EMBED_DIM = 64
OUTPUT_DIM = 64
FEAT_DIM = 3
BATCH_SIZE = 256
K_NEG = 128
LR = 2e-3
TEMPERATURE = 0.08
EPOCHS = 4

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM).to(DEVICE)
user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

optimizer = torch.optim.Adam(
    list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n],
    lr=LR,
    weight_decay=1e-5
)

# Also train item_emb directly
optimizer.add_param_group({'params': item_tower.item_emb.parameters(), 'lr': LR})

train_ds = FastDataset(train_samples, uid2idx)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

print(f"Starting training: {len(train_loader)} batches per epoch...")

for epoch in range(1, EPOCHS + 1):
    item_tower.train()
    user_tower.train()
    total_loss = 0.0
    t0 = time.time()
    
    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):
        optimizer.zero_grad()
        
        # User history tensors
        p_items = user_pos_items_t[u_idxs].clone() # (B, L)
        p_weights = user_pos_weights_t[u_idxs].clone()
        n_items = user_neg_items_t[u_idxs]
        n_weights = user_neg_weights_t[u_idxs]
        
        # Leave-one-out masking: mask out target item if present in positive history
        match_mask = (p_items == tgt_items.unsqueeze(-1))
        p_weights[match_mask] = 0.0
        
        # Sample K_NEG negative catalog items
        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))
        
        # User vectors
        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D)
        
        # Positive item vectors
        pos_feats = cat_features_tensor[tgt_items]
        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D)
        
        # Negative item vectors
        neg_feats = cat_features_tensor[neg_items]
        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D)
        
        # Logits
        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)
        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)
        
        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)
        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)
        
        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')
        loss = (loss_per_sample * sample_w).mean()
        
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
        if (batch_idx + 1) % 400 == 0:
            avg_b_loss = total_loss / (batch_idx + 1)
            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()
            print(f"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}")
            
    print(f"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}")

# Public Validation Evaluation
print("Evaluating on public validation set...")
item_tower.eval()
user_tower.eval()

with torch.no_grad():
    # 1. Precompute candidate item embeddings for all 30000 catalog items
    print("Precomputing all 30,000 item embeddings offline...")
    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)
    item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D)
    
    # 2. Compute user embeddings for all 8456 target users
    print("Computing user embeddings for all target users...")
    user_vecs = user_tower(
        user_pos_items_t,
        user_pos_weights_t,
        user_neg_items_t,
        user_neg_weights_t
    ) # (8456, D)
    
    # 3. Maximum inner product search: User x Item dot product
    print("MIPS dot product search...")
    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)
    
    # 4. Mask ineligible candidates
    print("Applying validation candidate eligibility mask...")
    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')
    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]
    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)
    
    scores[~mask_tensor] = -1e9
    
    # 5. Retrieve top 100 per user
    print("Selecting top 100 per user...")
    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)
    top100_indices = top100_indices.cpu().numpy()

# 6. Build validation ranking dataframe
print("Formatting validation ranking...")
...
# Run evaluate_public
graded = validation_truth(eligibility, target_set)
validated = validate_ranking(val_ranking_df, target_set)
ranked = ranked_items(validated)
score_report = score(ranked, graded)
print(f"\n==========================================")
print(f"Validation score report: {json.dumps(score_report, indent=2)}")
print(f"==========================================")

Train and evaluate feature-enhanced two-tower model on validation set

+31:28

Model text

I have rewritten /app/solution/train_val.py with:

ItemTower integrating 3 candidate catalog features (log-popularity, log-organic-listens, and normalized popularity rank) alongside the item embedding table.
UserTower incorporating both positive history and negative preference state items into a two-tower projection.
Training on all historical discovery pairs (organic likes and organic $\ge 80\%$ listens from the last 60 days before cutoff) using leave-one-out masking and batch-sampled catalog negatives.
Fast batch indexing and evaluation.

Next step: Execute train_val.py to train and measure the new validation NDCG@10.

Tool callrun_shell_commandTrain and evaluate feature-enhanced two-tower model on validation set Returned

Tool call · Input +31:28

{
  "description": "Train and evaluate feature-enhanced two-tower model on validation set",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +31:56

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Preparing item features...
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Traceback (most recent call last):
  File "/app/solution/train_val.py", line 272, in <module>
    optimizer.add_param_group({'params': item_tower.item_emb.parameters(), 'lr': LR})
  File "/opt/task-venv/lib/python3.12/site-packages/torch/_compile.py", line 53, in inner
    return disable_fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/eval_frame.py", line 929, in _fn
    return fn(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py", line 1151, in add_param_group
    raise ValueError("some parameters appear in more than one parameter group")
ValueError: some parameters appear in more than one parameter group
Exit Code: 1
Process Group PGID: 2752
</untrusted_context>

replace train_val.py

+32:27

Recorded reasoning summary 39 words

Fixing Optimizer Duplicates

I've identified a ValueError stemming from duplicate parameters in different optimizer groups. It seems item_tower.item_emb was inadvertently included in user_tower.parameters(). I'm adjusting the optimizer initialization to correctly segregate parameters, ensuring each is accounted for only once.

Tool callreplace/app/solution/train_val.py Returned

Tool call · Input +32:27

{
  "instruction": "Fix optimizer parameter grouping to avoid duplicates",
  "old_string": "optimizer = torch.optim.Adam(\n    list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n],\n    lr=LR,\n    weight_decay=1e-5\n)\n\n# Also train item_emb directly\noptimizer.add_param_group({'params': item_tower.item_emb.parameters(), 'lr': LR})\n\ntrain_ds = FastDataset(train_samples, uid2idx)",
  "new_string": "params = list(user_tower.fc.parameters()) + list(item_tower.parameters())\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)",
  "allow_multiple": false,
  "file_path": "/app/solution/train_val.py"
}

Tool response +32:27

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
EPOCHS = 4

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM).to(DEVICE)
user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

params = list(user_tower.fc.parameters()) + list(item_tower.parameters())
optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)

train_ds = FastDataset(train_samples, uid2idx)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

print(f"Starting training: {len(train_loader)} batches per epoch...")

...

Train and evaluate feature-enhanced two-tower model on validation set

+32:30

Model text

I have updated the optimizer parameters in /app/solution/train_val.py to list(user_tower.fc.parameters()) + list(item_tower.parameters()), eliminating duplicate parameter entries.

Next step: Run train_val.py to train the feature-enhanced two-tower network and evaluate validation NDCG@10.

Tool callrun_shell_commandTrain and evaluate feature-enhanced two-tower model on validation set Returned

Tool call · Input +32:30

{
  "description": "Train and evaluate feature-enhanced two-tower model on validation set",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +35:55

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Preparing item features...
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Starting training: 3277 batches per epoch...
Epoch 1 [400/3277] Loss: 4.8337, Top-1 Acc: 0.070
Epoch 1 [800/3277] Loss: 4.7077, Top-1 Acc: 0.184
Epoch 1 [1200/3277] Loss: 4.6166, Top-1 Acc: 0.223
Epoch 1 [1600/3277] Loss: 4.5524, Top-1 Acc: 0.086
Epoch 1 [2000/3277] Loss: 4.4994, Top-1 Acc: 0.113
Epoch 1 [2400/3277] Loss: 4.4524, Top-1 Acc: 0.199
Epoch 1 [2800/3277] Loss: 4.4098, Top-1 Acc: 0.223
Epoch 1 [3200/3277] Loss: 4.3752, Top-1 Acc: 0.129
Epoch 1 finished in 43.04s, Avg Loss: 4.3693
Epoch 2 [400/3277] Loss: 3.9916, Top-1 Acc: 0.164
Epoch 2 [800/3277] Loss: 3.9817, Top-1 Acc: 0.191
Epoch 2 [1200/3277] Loss: 3.9780, Top-1 Acc: 0.105
Epoch 2 [1600/3277] Loss: 3.9652, Top-1 Acc: 0.086
Epoch 2 [2000/3277] Loss: 3.9584, Top-1 Acc: 0.203
Epoch 2 [2400/3277] Loss: 3.9495, Top-1 Acc: 0.137
Epoch 2 [2800/3277] Loss: 3.9421, Top-1 Acc: 0.141
Epoch 2 [3200/3277] Loss: 3.9352, Top-1 Acc: 0.059
Epoch 2 finished in 43.67s, Avg Loss: 3.9342
Epoch 3 [400/3277] Loss: 3.6913, Top-1 Acc: 0.180
Epoch 3 [800/3277] Loss: 3.7252, Top-1 Acc: 0.156
Epoch 3 [1200/3277] Loss: 3.7431, Top-1 Acc: 0.172
Epoch 3 [1600/3277] Loss: 3.7555, Top-1 Acc: 0.215
Epoch 3 [2000/3277] Loss: 3.7584, Top-1 Acc: 0.156
Epoch 3 [2400/3277] Loss: 3.7580, Top-1 Acc: 0.117
Epoch 3 [2800/3277] Loss: 3.7600, Top-1 Acc: 0.199
Epoch 3 [3200/3277] Loss: 3.7582, Top-1 Acc: 0.180
Epoch 3 finished in 43.67s, Avg Loss: 3.7583
Epoch 4 [400/3277] Loss: 3.5421, Top-1 Acc: 0.191
Epoch 4 [800/3277] Loss: 3.5765, Top-1 Acc: 0.133
Epoch 4 [1200/3277] Loss: 3.5927, Top-1 Acc: 0.246
Epoch 4 [1600/3277] Loss: 3.6046, Top-1 Acc: 0.176
Epoch 4 [2000/3277] Loss: 3.6183, Top-1 Acc: 0.152
Epoch 4 [2400/3277] Loss: 3.6238, Top-1 Acc: 0.188
Epoch 4 [2800/3277] Loss: 3.6290, Top-1 Acc: 0.246
Epoch 4 [3200/3277] Loss: 3.6303, Top-1 Acc: 0.324
Epoch 4 finished in 42.87s, Avg Loss: 3.6308
Evaluating on public validation set...
Precomputing all 30,000 item embeddings offline...
Computing user embeddings for all target users...
MIPS dot product search...
Applying validation candidate eligibility mask...
Selecting top 100 per user...
Formatting validation ranking...
Validation ranking written to /app/solution/validation.parquet

==========================================
Validation score report: {
  "quality_ndcg@10": 0.008328,
  "quality_users": 4263
}
==========================================
Process Group PGID: 2786
</untrusted_context>

Diagnose neural model top-10 predictions against ground truth

+37:37

Recorded reasoning summary 42 words

Observing Steady Gains

I'm pleased to report a significant positive trend. The loss has decreased substantially, while both top-1 accuracy and NDCG@10 have seen encouraging rises. The neural ranker's performance has improved threefold, and I'm now focused on surpassing the popularity baseline.

Tool callrun_shell_commandDiagnose neural model top-10 predictions against ground truth Returned

Tool call · Input +37:37

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, target_set)\n\n# Load neural predictions\nval_df = pd.read_parquet('/app/solution/validation.parquet')\nval_ranked = ranked_items(validate_ranking(val_df, target_set))\n\n# Analyze top-10 for scored users\nneural_top10_hits_grade3 = 0\nneural_top10_hits_grade1 = 0\nneural_top10_pop_ranks = []\n\ngold_pop_ranks = []\nfor uid, gold in graded.items():\n    for item, grade in gold.items():\n        gold_pop_ranks.append(item_pop[item])\n    for item in val_ranked[uid][:10]:\n        neural_top10_pop_ranks.append(item_pop[item])\n        g = gold.get(item, 0)\n        if g == 3:\n            neural_top10_hits_grade3 += 1\n        elif g == 1:\n            neural_top10_hits_grade1 += 1\n\nprint('Ground truth items in validation window:')\nprint(f'Total gold items: {len(gold_pop_ranks)}, mean pop rank: {np.mean(gold_pop_ranks):.1f}, median: {np.median(gold_pop_ranks):.1f}')\n\nprint('\\nNeural model top-10 predictions:')\nprint(f'Grade 3 hits in top-10: {neural_top10_hits_grade3}')\nprint(f'Grade 1 hits in top-10: {neural_top10_hits_grade1}')\nprint(f'Mean pop rank of predictions: {np.mean(neural_top10_pop_ranks):.1f}, median: {np.median(neural_top10_pop_ranks):.1f}')\n\"",
  "description": "Diagnose neural model top-10 predictions against ground truth"
}

Tool response +37:40

<untrusted_context>
Output: Ground truth items in validation window:
Total gold items: 37769, mean pop rank: 8463.1, median: 5622.0

Neural model top-10 predictions:
Grade 3 hits in top-10: 28
Grade 1 hits in top-10: 233
Mean pop rank of predictions: 1291.8, median: 282.0
Process Group PGID: 2850
</untrusted_context>

Diagnose popularity baseline top-10 predictions against ground truth

+37:46

Tool callrun_shell_commandDiagnose popularity baseline top-10 predictions against ground truth Returned

Tool call · Input +37:46

{
  "description": "Diagnose popularity baseline top-10 predictions against ground truth",
  "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, target_set)\n\n# Popularity ranking\ncatalog_items = cat.item_id.to_numpy()\nrecords = []\nfor r, uid in enumerate(targets.uid):\n    mask = np.unpackbits(eligibility.packed[r], count=len(catalog_items), bitorder='little')\n    eligible_items = catalog_items[mask == 1]\n    top_100 = eligible_items[:100]\n    for rank, item in enumerate(top_100, 1):\n        records.append((uid, item, rank))\n\npop_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\npop_ranked = ranked_items(validate_ranking(pop_df, target_set))\n\npop_top10_hits_grade3 = 0\npop_top10_hits_grade1 = 0\npop_top10_pop_ranks = []\n\nfor uid, gold in graded.items():\n    for item in pop_ranked[uid][:10]:\n        pop_top10_pop_ranks.append(item_pop[item])\n        g = gold.get(item, 0)\n        if g == 3:\n            pop_top10_hits_grade3 += 1\n        elif g == 1:\n            pop_top10_hits_grade1 += 1\n\nprint('Popularity baseline top-10 predictions:')\nprint(f'Grade 3 hits in top-10: {pop_top10_hits_grade3}')\nprint(f'Grade 1 hits in top-10: {pop_top10_hits_grade1}')\nprint(f'Mean pop rank of predictions: {np.mean(pop_top10_pop_ranks):.1f}, median: {np.median(pop_top10_pop_ranks):.1f}')\n\""
}

Tool response +37:50

<untrusted_context>
Output: Popularity baseline top-10 predictions:
Grade 3 hits in top-10: 34
Grade 1 hits in top-10: 334
Mean pop rank of predictions: 19.0, median: 12.0
Process Group PGID: 2870
</untrusted_context>

Check validation scoring setup

+38:26

Recorded reasoning summary 34 words

Analyzing Popularity Data

I'm reviewing the popularity metrics for both the baseline and neural models. My focus is on understanding the differences in their performance, particularly concerning the distribution of hits across popularity grades.

Recorded reasoning summary 47 words

Integrating Popularity Data

I'm exploring how to blend the neural model's personalized recommendations with popularity information. Currently, the neural model is finding deeper catalog tracks, but the popularity prior still excels at recommending top hits. I'm investigating the relevance computation and feature embeddings within the neural network.

Recorded reasoning summary 71 words

Modifying Item Vectors

I've realized my current item vectors, all with a norm of 1.0, can't inherently capture popularity. This means even a slightly better alignment with user vectors can unfairly favor obscure items. I'm now exploring how to incorporate learned item biases or popularity components directly into the item vectors, effectively transforming the scoring function into an extended inner product. I'm testing this by modifying existing predictions with popularity scores.

Tool callrun_shell_commandCheck validation scoring setup Returned

Tool call · Input +38:26

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nidx2item = cat.item_id.to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\ngraded = validation_truth(eligibility, target_set)\n\n# Load neural vectors from our trained model\n# Let's see if we have user_vecs and item_vecs\n# We can re-run the inference or test different blending\n\"",
  "description": "Check validation scoring setup"
}

Tool response +38:30

<untrusted_context>
Output: (empty)
Process Group PGID: 2896
</untrusted_context>

Test bias-augmented two-tower forward pass and dot product

+38:47

Recorded reasoning summary 47 words

Saving and Investigating Embeddings

I'm focusing on enhancing train_val.py. The plan now is to save user_vecs.pt and item_vecs.pt. I'm also considering incorporating a learned popularity bias, or at least testing its impact. A key question I'm exploring is whether the neural network can actually learn this bias.

Recorded reasoning summary 58 words

Integrating Item Bias Learning

I'm exploring how to integrate a learned popularity bias into my model architecture. The idea is to augment user and item embeddings with scalar values, allowing the dot product to effectively combine personalized scores with item-specific biases. I believe this setup naturally trains the bias term to reflect item frequency through the cross-entropy loss.

Tool callrun_shell_commandTest bias-augmented two-tower forward pass and dot product Returned

Tool call · Input +38:47

{
  "description": "Test bias-augmented two-tower forward pass and dot product",
  "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, feat_dim, output_dim):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        nn.init.normal_(self.item_emb.weight, std=0.05)\n        self.feat_proj = nn.Linear(feat_dim, 16)\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim + 16, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n        self.item_bias = nn.Embedding(num_items, 1)\n        nn.init.zeros_(self.item_bias.weight)\n        \n    def forward(self, item_ids, feats):\n        emb = self.item_emb(item_ids)\n        feat_emb = self.feat_proj(feats)\n        combined = torch.cat([emb, feat_emb], dim=-1)\n        v = F.normalize(self.fc(combined), p=2, dim=-1)\n        b = self.item_bias(item_ids)\n        # Output is (D + 1)\n        return torch.cat([v, b], dim=-1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim * 2, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Linear(embed_dim, output_dim)\n        )\n        # Learnable scaling for the bias term per user or fixed 1.0\n        self.user_bias_scale = nn.Sequential(\n            nn.Linear(embed_dim * 2, 1),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, pos_items, pos_weights, neg_items, neg_weights):\n        pos_emb = self.item_emb(pos_items)\n        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)\n        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled_pos = w_pos / w_pos_norm\n        \n        neg_emb = self.item_emb(neg_items)\n        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)\n        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()\n        pooled_neg = (w_neg / w_neg_norm) * has_neg\n        \n        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)\n        u = F.normalize(self.fc(combined), p=2, dim=-1)\n        scale = self.user_bias_scale(combined) * 2.0 # range [0, 2]\n        return torch.cat([u, scale], dim=-1)\n\n# Test forward pass and dot product\nit = ItemTower(100, 32, 3, 32)\nut = UserTower(it.item_emb, 32, 32)\n\nu_vec = ut(torch.randint(0, 100, (10, 8)), torch.rand(10, 8), torch.randint(0, 100, (10, 4)), torch.rand(10, 4))\ni_vec = it(torch.randint(0, 100, (10,)), torch.rand(10, 3))\n\nprint('User vector shape:', u_vec.shape)\nprint('Item vector shape:', i_vec.shape)\nscores = (u_vec * i_vec).sum(dim=-1)\nprint('Dot product shape:', scores.shape)\nprint('Dot product sample values:', scores[:3].tolist())\n\""
}

Tool response +38:50

<untrusted_context>
Output: User vector shape: torch.Size([10, 33])
Item vector shape: torch.Size([10, 33])
Dot product shape: torch.Size([10])
Dot product sample values: [0.1471569836139679, 0.18304696679115295, 0.10504821687936783]
Process Group PGID: 2916
</untrusted_context>

write_file train_val.py

+39:22

Recorded reasoning summary 28 words

Confirming Scoring Mechanism

I've confirmed that the scoring is a direct dot product between independently computed user and item vectors. Candidate item vectors are precomputed, streamlining the process.

Tool callwrite_file/app/solution/train_val.py Returned

Tool call · Input +39:22

{
  "file_path": "/app/solution/train_val.py",
  "content": "import time\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA_DIR = Path('/task/data')\nTRAIN_END = 24786800\nDAY_SEC = 86400\nNUM_CATALOG = 30000\nDEVICE = torch.device('cpu')\n\ntorch.set_num_threads(8)\ntorch.manual_seed(42)\nnp.random.seed(42)\n\nprint(\"Loading metadata...\")\ncat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\nidx2item = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet(DATA_DIR / 'target_users.parquet')\ntarget_uids = targets.uid.astype(int).to_numpy()\ntarget_set = set(target_uids)\nuid2idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f\"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}\")\n\n# Prepare Item Features from candidate catalog\nprint(\"Preparing item features...\")\ncat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)\n\n# Initial popularity bias based on log positive listens normalized to [0, 1]\nlog_pop = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\ninitial_bias = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\nfeat_mean = cat_features.mean(axis=0, keepdims=True)\nfeat_std = cat_features.std(axis=0, keepdims=True) + 1e-6\ncat_features = (cat_features - feat_mean) / feat_std\ncat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)\n\n# 1. Load active explicit preference state at validation cutoff\nprint(\"Loading validation preference state...\")\nval_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked']\npref_dislikes = val_pref[val_pref.state == 'disliked']\n\nuser_pref_likes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_likes.uid, pref_likes.item_id):\n    if uid in user_pref_likes and item in item2idx:\n        user_pref_likes[uid].add(item2idx[item])\n\nuser_pref_dislikes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):\n    if uid in user_pref_dislikes and item in item2idx:\n        user_pref_dislikes[uid].add(item2idx[item])\n\n# 2. Load recent positive listens (last 90 days before cutoff)\nprint(\"Loading recent listens...\")\ncutoff_90d = TRAIN_END - 90 * DAY_SEC\nlistens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]\n)\nlistens = listens[listens.uid.isin(target_set) & (listens.played_ratio_pct >= 50)]\nlistens['item_idx'] = listens.item_id.map(item2idx)\nlistens = listens.dropna(subset=['item_idx'])\nlistens['item_idx'] = listens['item_idx'].astype(int)\n\n# Group listens per user\nprint(\"Aggregating user histories...\")\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)\nlistens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))\n\n# Pre-aggregate per user\nMAX_HIST_LEN = 128\nMAX_DISLIKE_LEN = 32\n\nuser_profiles = {}\n\nfor uid, group in listens.groupby('uid'):\n    item_weights = {}\n    for idx in user_pref_likes.get(uid, ()):\n        item_weights[idx] = 4.0\n        \n    for row in group.itertuples(index=False):\n        idx = row.item_idx\n        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight\n        item_weights[idx] = item_weights.get(idx, 0.0) + w\n        \n    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]\n    pos_items = [x[0] for x in sorted_items]\n    pos_weights = [x[1] for x in sorted_items]\n    \n    dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n    \n    user_profiles[uid] = {\n        'pos_items': np.array(pos_items, dtype=np.int64),\n        'pos_weights': np.array(pos_weights, dtype=np.float32),\n        'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n    }\n\nfor uid in target_uids:\n    if uid not in user_profiles:\n        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]\n        p_weights = [4.0] * len(p_likes) if p_likes else [1.0]\n        p_items = p_likes if p_likes else [0]\n        dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n        user_profiles[uid] = {\n            'pos_items': np.array(p_items, dtype=np.int64),\n            'pos_weights': np.array(p_weights, dtype=np.float32),\n            'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n        }\n\nprint(f\"User profiles built for {len(user_profiles)} users.\")\n\n# 3. Build training dataset of positive discovery pairs in last 60 days\nprint(\"Extracting training interaction pairs...\")\nt_train_start = TRAIN_END - 60 * DAY_SEC\n\n# Organic likes in last 60 days\ntrain_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet', filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)])\ntrain_likes = train_likes[train_likes.uid.isin(target_set) & (train_likes.is_organic == 1)]\ntrain_likes['item_idx'] = train_likes.item_id.map(item2idx)\ntrain_likes = train_likes.dropna(subset=['item_idx'])\ntrain_likes['item_idx'] = train_likes['item_idx'].astype(int)\n\n# Organic listens in last 60 days\ntrain_listens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)]\n)\ntrain_listens = train_listens[train_listens.uid.isin(target_set) & (train_listens.is_organic == 1) & (train_listens.played_ratio_pct >= 80)]\ntrain_listens['item_idx'] = train_listens.item_id.map(item2idx)\ntrain_listens = train_listens.dropna(subset=['item_idx'])\ntrain_listens['item_idx'] = train_listens['item_idx'].astype(int)\n\npair_dict = {}\nfor uid, idx in zip(train_listens.uid, train_listens.item_idx):\n    pair_dict[(int(uid), int(idx))] = 1\n\nfor uid, idx in zip(train_likes.uid, train_likes.item_idx):\n    pair_dict[(int(uid), int(idx))] = 3\n\ntrain_samples = []\nfor (uid, item_idx), grade in pair_dict.items():\n    w = 3.0 if grade == 3 else 1.0\n    train_samples.append((uid, item_idx, w))\n\nprint(f\"Total training pairs: {len(train_samples)}\")\n\n# Convert profiles to fixed-size numpy arrays\nuser_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)\nuser_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)\nuser_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)\nuser_neg_weights = np.zeros((len(target_uids), MAX_DISLIKE_LEN), dtype=np.float32)\n\nfor uid, idx in uid2idx.items():\n    prof = user_profiles[uid]\n    p_len = min(len(prof['pos_items']), MAX_HIST_LEN)\n    user_pos_items[idx, :p_len] = prof['pos_items'][:p_len]\n    user_pos_weights[idx, :p_len] = prof['pos_weights'][:p_len]\n    \n    n_len = min(len(prof['neg_items']), MAX_DISLIKE_LEN)\n    if n_len > 0:\n        user_neg_items[idx, :n_len] = prof['neg_items'][:n_len]\n        user_neg_weights[idx, :n_len] = 1.0\n\nuser_pos_items_t = torch.from_numpy(user_pos_items)\nuser_pos_weights_t = torch.from_numpy(user_pos_weights)\nuser_neg_items_t = torch.from_numpy(user_neg_items)\nuser_neg_weights_t = torch.from_numpy(user_neg_weights)\n\n# Model definitions\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, feat_dim, output_dim, init_bias, dropout=0.1):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        nn.init.normal_(self.item_emb.weight, std=0.05)\n        self.feat_proj = nn.Linear(feat_dim, 16)\n        \n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim + 16, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        self.item_bias = nn.Embedding(num_items + 1, 1, padding_idx=num_items)\n        with torch.no_grad():\n            self.item_bias.weight[:num_items, 0] = torch.from_numpy(init_bias) * 0.5\n            self.item_bias.weight[num_items, 0] = 0.0\n            \n    def forward(self, item_ids, feats):\n        emb = self.item_emb(item_ids)\n        feat_emb = self.feat_proj(feats)\n        combined = torch.cat([emb, feat_emb], dim=-1)\n        v = F.normalize(self.fc(combined), p=2, dim=-1)\n        b = self.item_bias(item_ids) # (N, 1)\n        return torch.cat([v, b], dim=-1) # (N, D + 1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim * 2, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        self.bias_scale = nn.Sequential(\n            nn.Linear(embed_dim * 2, 16),\n            nn.ReLU(),\n            nn.Linear(16, 1),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, pos_items, pos_weights, neg_items, neg_weights):\n        pos_emb = self.item_emb(pos_items) # (B, L_pos, D)\n        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)\n        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled_pos = w_pos / w_pos_norm\n        \n        neg_emb = self.item_emb(neg_items)\n        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)\n        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()\n        pooled_neg = (w_neg / w_neg_norm) * has_neg\n        \n        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)\n        u = F.normalize(self.fc(combined), p=2, dim=-1)\n        scale = self.bias_scale(combined) * 1.5 + 0.5 # range [0.5, 2.0]\n        return torch.cat([u, scale], dim=-1) # (B, D + 1)\n\nclass FastDataset(Dataset):\n    def __init__(self, samples, uid2idx):\n        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)\n        self.target_items = np.array([s[1] for s in samples], dtype=np.int64)\n        self.weights = np.array([s[2] for s in samples], dtype=np.float32)\n        \n    def __len__(self):\n        return len(self.u_indices)\n        \n    def __getitem__(self, idx):\n        return self.u_indices[idx], self.target_items[idx], self.weights[idx]\n\nEMBED_DIM = 64\nOUTPUT_DIM = 64\nFEAT_DIM = 3\nBATCH_SIZE = 256\nK_NEG = 128\nLR = 2e-3\nTEMPERATURE = 0.08\nEPOCHS = 4\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\nparams = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\")\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        p_items = user_pos_items_t[u_idxs].clone()\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        # Mask out target item in positive history\n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        # Sample K_NEG negative catalog items\n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n        \n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n        \n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n        \n        # Dot product\n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 400 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\")\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\")\n\n# Public Validation Evaluation\nprint(\"Evaluating on public validation set...\")\nitem_tower.eval()\nuser_tower.eval()\n\nwith torch.no_grad():\n    # 1. Precompute candidate item embeddings for all 30000 catalog items\n    print(\"Precomputing all 30,000 item embeddings offline...\")\n    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n    item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n    \n    # 2. Compute user embeddings for all 8456 target users\n    print(\"Computing user embeddings for all target users...\")\n    user_vecs = user_tower(\n        user_pos_items_t,\n        user_pos_weights_t,\n        user_neg_items_t,\n        user_neg_weights_t\n    ) # (8456, D + 1)\n    \n    # 3. Maximum inner product search: User x Item dot product\n    print(\"MIPS dot product search...\")\n    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n    \n    # 4. Mask ineligible candidates\n    print(\"Applying validation candidate eligibility mask...\")\n    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\n    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\n    \n    scores[~mask_tensor] = -1e9\n    \n    # 5. Retrieve top 100 per user\n    print(\"Selecting top 100 per user...\")\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.cpu().numpy()\n\n# 6. Build validation ranking dataframe\nprint(\"Formatting validation ranking...\")\nrecords = []\nfor r, uid in enumerate(target_uids):\n    ranked_catalog_indices = top100_indices[r]\n    ranked_item_ids = idx2item[ranked_catalog_indices]\n    for rank, item_id in enumerate(ranked_item_ids, 1):\n        records.append((int(uid), int(item_id), rank))\n\nval_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nval_ranking_path = Path('/app/solution/validation.parquet')\nval_ranking_df.to_parquet(val_ranking_path)\nprint(f\"Validation ranking written to {val_ranking_path}\")\n\n# Run evaluate_public\ngraded = validation_truth(eligibility, target_set)\nvalidated = validate_ranking(val_ranking_df, target_set)\nranked = ranked_items(validated)\nscore_report = score(ranked, graded)\nprint(f\"\\n==========================================\")\nprint(f\"Validation score report: {json.dumps(score_report, indent=2)}\")\nprint(f\"==========================================\")\n"
}

Tool response +39:22

Successfully overwrote file: /app/solution/train_val.py. Here is the updated code:
...
cat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)

# Initial popularity bias based on log positive listens normalized to [0, 1]
log_pop = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))
initial_bias = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

feat_mean = cat_features.mean(axis=0, keepdims=True)
feat_std = cat_features.std(axis=0, keepdims=True) + 1e-6
cat_features = (cat_features - feat_mean) / feat_std
cat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)

...
train_listens = train_listens[train_listens.uid.isin(target_set) & (train_listens.is_organic == 1) & (train_listens.played_ratio_pct >= 80)]
train_listens['item_idx'] = train_listens.item_id.map(item2idx)
train_listens = train_listens.dropna(subset=['item_idx'])
train_listens['item_idx'] = train_listens['item_idx'].astype(int)

pair_dict = {}
for uid, idx in zip(train_listens.uid, train_listens.item_idx):
    pair_dict[(int(uid), int(idx))] = 1

for uid, idx in zip(train_likes.uid, train_likes.item_idx):
    pair_dict[(int(uid), int(idx))] = 3

train_samples = []
for (uid, item_idx), grade in pair_dict.items():
    w = 3.0 if grade == 3 else 1.0
    train_samples.append((uid, item_idx, w))

print(f"Total training pairs: {len(train_samples)}")

# Convert profiles to fixed-size numpy arrays
user_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)
user_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)
user_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)
user_neg_weights = np.zeros((len(target_uids), MAX_DISLIKE_LEN), dtype=np.float32)

...
user_neg_items_t = torch.from_numpy(user_neg_items)
user_neg_weights_t = torch.from_numpy(user_neg_weights)

# Model definitions
class ItemTower(nn.Module):
    def __init__(self, num_items, embed_dim, feat_dim, output_dim, init_bias, dropout=0.1):
        super().__init__()
        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)
        nn.init.normal_(self.item_emb.weight, std=0.05)
        self.feat_proj = nn.Linear(feat_dim, 16)
        
...
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        self.item_bias = nn.Embedding(num_items + 1, 1, padding_idx=num_items)
        with torch.no_grad():
            self.item_bias.weight[:num_items, 0] = torch.from_numpy(init_bias) * 0.5
            self.item_bias.weight[num_items, 0] = 0.0
            
    def forward(self, item_ids, feats):
        emb = self.item_emb(item_ids)
        feat_emb = self.feat_proj(feats)
        combined = torch.cat([emb, feat_emb], dim=-1)
        v = F.normalize(self.fc(combined), p=2, dim=-1)
        b = self.item_bias(item_ids) # (N, 1)
        return torch.cat([v, b], dim=-1) # (N, D + 1)

class UserTower(nn.Module):
    def __init__(self, item_emb, embed_dim, output_dim, dropout=0.1):
        super().__init__()
        self.item_emb = item_emb
...
            nn.LayerNorm(embed_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        self.bias_scale = nn.Sequential(
            nn.Linear(embed_dim * 2, 16),
            nn.ReLU(),
            nn.Linear(16, 1),
            nn.Sigmoid()
        )
        
    def forward(self, pos_items, pos_weights, neg_items, neg_weights):
        pos_emb = self.item_emb(pos_items) # (B, L_pos, D)
        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)
        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        pooled_pos = w_pos / w_pos_norm
        
        neg_emb = self.item_emb(neg_items)
        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)
        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()
        pooled_neg = (w_neg / w_neg_norm) * has_neg
        
        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)
        u = F.normalize(self.fc(combined), p=2, dim=-1)
        scale = self.bias_scale(combined) * 1.5 + 0.5 # range [0.5, 2.0]
        return torch.cat([u, scale], dim=-1) # (B, D + 1)

class FastDataset(Dataset):
    def __init__(self, samples, uid2idx):
        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)
        self.target_items = np.array([s[1] for s in samples], dtype=np.int64)
...
K_NEG = 128
LR = 2e-3
TEMPERATURE = 0.08
EPOCHS = 4

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)
user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

params = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]
optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)

train_ds = FastDataset(train_samples, uid2idx)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

...
    t0 = time.time()
    
    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):
        optimizer.zero_grad()
        
        p_items = user_pos_items_t[u_idxs].clone()
        p_weights = user_pos_weights_t[u_idxs].clone()
        n_items = user_neg_items_t[u_idxs]
        n_weights = user_neg_weights_t[u_idxs]
        
        # Mask out target item in positive history
        match_mask = (p_items == tgt_items.unsqueeze(-1))
        p_weights[match_mask] = 0.0
        
        # Sample K_NEG negative catalog items
        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))
        
        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)
        
        pos_feats = cat_features_tensor[tgt_items]
        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)
        
        neg_feats = cat_features_tensor[neg_items]
        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)
        
        # Dot product
        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)
        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)
        
        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)
        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)
...

with torch.no_grad():
    # 1. Precompute candidate item embeddings for all 30000 catalog items
    print("Precomputing all 30,000 item embeddings offline...")
    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)
    item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)
    
    # 2. Compute user embeddings for all 8456 target users
    print("Computing user embeddings for all target users...")
    user_vecs = user_tower(
        user_pos_items_t,
        user_pos_weights_t,
        user_neg_items_t,
        user_neg_weights_t
    ) # (8456, D + 1)
    
    # 3. Maximum inner product search: User x Item dot product
    print("MIPS dot product search...")
    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)
    
...

Train and evaluate bias-augmented two-tower model on validation set

+39:26

Model text

I have updated /app/solution/train_val.py to add the popularity bias coordinate to both ItemTower (initialized from normalized log positive listens) and UserTower (via a learned user scaling network), expanding the vector representations to $(D+1)$ dimensions while strictly preserving the maximum inner product search decomposition.

Next step: Execute train_val.py and inspect the new validation NDCG@10 score.

Tool callrun_shell_commandTrain and evaluate bias-augmented two-tower model on validation set Returned

Tool call · Input +39:26

{
  "description": "Train and evaluate bias-augmented two-tower model on validation set",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +43:01

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Preparing item features...
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Starting training: 3277 batches per epoch...
Epoch 1 [400/3277] Loss: 4.8815, Top-1 Acc: 0.141
Epoch 1 [800/3277] Loss: 4.7497, Top-1 Acc: 0.055
Epoch 1 [1200/3277] Loss: 4.6551, Top-1 Acc: 0.086
Epoch 1 [1600/3277] Loss: 4.5725, Top-1 Acc: 0.121
Epoch 1 [2000/3277] Loss: 4.5051, Top-1 Acc: 0.094
Epoch 1 [2400/3277] Loss: 4.4490, Top-1 Acc: 0.199
Epoch 1 [2800/3277] Loss: 4.4082, Top-1 Acc: 0.133
Epoch 1 [3200/3277] Loss: 4.3776, Top-1 Acc: 0.145
Epoch 1 finished in 46.27s, Avg Loss: 4.3712
Epoch 2 [400/3277] Loss: 3.9886, Top-1 Acc: 0.203
Epoch 2 [800/3277] Loss: 3.9964, Top-1 Acc: 0.148
Epoch 2 [1200/3277] Loss: 3.9904, Top-1 Acc: 0.051
Epoch 2 [1600/3277] Loss: 3.9809, Top-1 Acc: 0.184
Epoch 2 [2000/3277] Loss: 3.9753, Top-1 Acc: 0.215
Epoch 2 [2400/3277] Loss: 3.9644, Top-1 Acc: 0.242
Epoch 2 [2800/3277] Loss: 3.9576, Top-1 Acc: 0.195
Epoch 2 [3200/3277] Loss: 3.9464, Top-1 Acc: 0.223
Epoch 2 finished in 45.99s, Avg Loss: 3.9443
Epoch 3 [400/3277] Loss: 3.6815, Top-1 Acc: 0.203
Epoch 3 [800/3277] Loss: 3.7066, Top-1 Acc: 0.164
Epoch 3 [1200/3277] Loss: 3.7259, Top-1 Acc: 0.121
Epoch 3 [1600/3277] Loss: 3.7394, Top-1 Acc: 0.230
Epoch 3 [2000/3277] Loss: 3.7435, Top-1 Acc: 0.176
Epoch 3 [2400/3277] Loss: 3.7468, Top-1 Acc: 0.191
Epoch 3 [2800/3277] Loss: 3.7463, Top-1 Acc: 0.281
Epoch 3 [3200/3277] Loss: 3.7497, Top-1 Acc: 0.168
Epoch 3 finished in 45.29s, Avg Loss: 3.7494
Epoch 4 [400/3277] Loss: 3.5165, Top-1 Acc: 0.164
Epoch 4 [800/3277] Loss: 3.5669, Top-1 Acc: 0.168
Epoch 4 [1200/3277] Loss: 3.5909, Top-1 Acc: 0.188
Epoch 4 [1600/3277] Loss: 3.6085, Top-1 Acc: 0.277
Epoch 4 [2000/3277] Loss: 3.6214, Top-1 Acc: 0.059
Epoch 4 [2400/3277] Loss: 3.6331, Top-1 Acc: 0.152
Epoch 4 [2800/3277] Loss: 3.6410, Top-1 Acc: 0.117
Epoch 4 [3200/3277] Loss: 3.6481, Top-1 Acc: 0.062
Epoch 4 finished in 46.06s, Avg Loss: 3.6482
Evaluating on public validation set...
Precomputing all 30,000 item embeddings offline...
Computing user embeddings for all target users...
MIPS dot product search...
Applying validation candidate eligibility mask...
Selecting top 100 per user...
Formatting validation ranking...
Validation ranking written to /app/solution/validation.parquet

==========================================
Validation score report: {
  "quality_ndcg@10": 0.009467,
  "quality_users": 4263
}
==========================================
Process Group PGID: 2935
</untrusted_context>

Check normalized log popularity values

+43:34

Recorded reasoning summary 36 words

Improving NDCG Performance

I've observed a positive shift in our NDCG@10, now reaching 0.009467 from 0.008328. I'm now examining performance across individual epochs and monitoring the declining training loss, which has dropped from 4.37 to 3.7.

Recorded reasoning summary 52 words

Detecting Overfitting Triggers

I've pinpointed slight overfitting around Epoch 4, evidenced by a sharp rise in Top-1 accuracy on training pairs and a dip in general retrieval quality. I'm now exploring checkpointing the best model based on per-epoch evaluation and investigating learning rate schedules, embedding dimensions, and the scaling of bias terms.

Tool callrun_shell_commandCheck normalized log popularity values Returned

Tool call · Input +43:34

{
  "description": "Check normalized log popularity values",
  "command": "python3 -c \"\nimport numpy as np\n\n# Let's check how init_bias behaves\ncat_pos = np.log1p([26615, 10000, 2000, 500, 163])\nmin_v = np.log1p(163)\nmax_v = np.log1p(26615)\nnorm_b = (cat_pos - min_v) / (max_v - min_v)\nprint('Norm b for top items down to 163:', norm_b)\n\""
}

Tool response +43:34

<untrusted_context>
Output: Norm b for top items down to 163: [1.         0.80767336 0.49151869 0.21942456 0.        ]
Process Group PGID: 2993
</untrusted_context>

Verify Python environment

+43:38

Recorded reasoning summary 38 words

Observing listen data

I'm noticing a clear trend: as listen counts increase, the associated values seem to rise, though not linearly. The jump from 163 to 500 listens shows a significant increase in value from 0.0 to 0.22.

Tool callrun_shell_commandVerify Python environment Returned

Tool call · Input +43:38

{
  "command": "python3 -c \"\nimport torch\nimport numpy as np\n# Let's inspect what happens to item_bias during training\n\"",
  "description": "Verify Python environment"
}

Tool response +43:41

<untrusted_context>
Output: (empty)
Process Group PGID: 3003
</untrusted_context>

replace train_val.py

+44:02

Recorded reasoning summary 28 words

Evaluating After Each Epoch

I'm focusing on enhancing train_val.py by implementing evaluation after every epoch. This will provide a much clearer, granular view of validation performance throughout training.

Tool callreplace/app/solution/train_val.py Returned

Tool call · Input +44:02

{
  "new_string": "EMBED_DIM = 128\nOUTPUT_DIM = 128\nFEAT_DIM = 3\nBATCH_SIZE = 256\nK_NEG = 128\nLR = 1e-3\nTEMPERATURE = 0.08\nEPOCHS = 5\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\nwith torch.no_grad():\n    item_tower.item_bias.weight[:NUM_CATALOG, 0] = torch.from_numpy(initial_bias) * 1.0\n\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\nparams = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\")\n\neligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\ngraded = validation_truth(eligibility, target_set)\n\nbest_ndcg = 0.0\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        p_items = user_pos_items_t[u_idxs].clone()\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        # Mask out target item in positive history\n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        # Sample K_NEG negative catalog items\n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n        \n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n        \n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n        \n        # Dot product\n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 800 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\")\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\")\n\n    # Evaluate validation score after each epoch\n    item_tower.eval()\n    user_tower.eval()\n    with torch.no_grad():\n        all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n        item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n        user_vecs = user_tower(\n            user_pos_items_t,\n            user_pos_weights_t,\n            user_neg_items_t,\n            user_neg_weights_t\n        ) # (8456, D + 1)\n        scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n        scores[~mask_tensor] = -1e9\n        top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n        top100_indices = top100_indices.cpu().numpy()\n\n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n\n    val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_ranking_df, target_set)\n    ranked = ranked_items(validated)\n    score_report = score(ranked, graded)\n    current_ndcg = score_report['quality_ndcg@10']\n    print(f\"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best: {best_ndcg:.6f})\")\n    \n    if current_ndcg > best_ndcg:\n        best_ndcg = current_ndcg\n        val_ranking_path = Path('/app/solution/validation.parquet')\n        val_ranking_df.to_parquet(val_ranking_path)\n        torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')\n        torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')\n        print(f\"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}\")",
  "old_string": "EMBED_DIM = 64\nOUTPUT_DIM = 64\nFEAT_DIM = 3\nBATCH_SIZE = 256\nK_NEG = 128\nLR = 2e-3\nTEMPERATURE = 0.08\nEPOCHS = 4\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\nparams = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\")\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        p_items = user_pos_items_t[u_idxs].clone()\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        # Mask out target item in positive history\n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        # Sample K_NEG negative catalog items\n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n        \n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n        \n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n        \n        # Dot product\n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE # (B, 1)\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE               # (B, K_NEG)\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1) # (B, 1 + K_NEG)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 400 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\")\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\")\n\n# Public Validation Evaluation\nprint(\"Evaluating on public validation set...\")\nitem_tower.eval()\nuser_tower.eval()\n\nwith torch.no_grad():\n    # 1. Precompute candidate item embeddings for all 30000 catalog items\n    print(\"Precomputing all 30,000 item embeddings offline...\")\n    all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n    item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n    \n    # 2. Compute user embeddings for all 8456 target users\n    print(\"Computing user embeddings for all target users...\")\n    user_vecs = user_tower(\n        user_pos_items_t,\n        user_pos_weights_t,\n        user_neg_items_t,\n        user_neg_weights_t\n    ) # (8456, D + 1)\n    \n    # 3. Maximum inner product search: User x Item dot product\n    print(\"MIPS dot product search...\")\n    scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n    \n    # 4. Mask ineligible candidates\n    print(\"Applying validation candidate eligibility mask...\")\n    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\n    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\n    \n    scores[~mask_tensor] = -1e9\n    \n    # 5. Retrieve top 100 per user\n    print(\"Selecting top 100 per user...\")\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.cpu().numpy()\n\n# 6. Build validation ranking dataframe\nprint(\"Formatting validation ranking...\")",
  "instruction": "Update model hyperparameters: EMBED_DIM=128, initial_bias scale=1.0, LR=1e-3, and evaluate validation NDCG@10 after each epoch saving the best ranking.",
  "allow_multiple": false,
  "file_path": "/app/solution/train_val.py"
}

Tool response +44:02

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
        return len(self.u_indices)
        
    def __getitem__(self, idx):
        return self.u_indices[idx], self.target_items[idx], self.weights[idx]

EMBED_DIM = 128
OUTPUT_DIM = 128
FEAT_DIM = 3
BATCH_SIZE = 256
K_NEG = 128
LR = 1e-3
TEMPERATURE = 0.08
EPOCHS = 5

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)
with torch.no_grad():
    item_tower.item_bias.weight[:NUM_CATALOG, 0] = torch.from_numpy(initial_bias) * 1.0

user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

params = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]
optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)

train_ds = FastDataset(train_samples, uid2idx)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

print(f"Starting training: {len(train_loader)} batches per epoch...")

eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')
unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]
mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)
graded = validation_truth(eligibility, target_set)

best_ndcg = 0.0

for epoch in range(1, EPOCHS + 1):
    item_tower.train()
    user_tower.train()
    total_loss = 0.0
    t0 = time.time()
...
        
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
        if (batch_idx + 1) % 800 == 0:
            avg_b_loss = total_loss / (batch_idx + 1)
            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()
            print(f"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}")
            
    print(f"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}")

    # Evaluate validation score after each epoch
    item_tower.eval()
    user_tower.eval()
    with torch.no_grad():
        all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)
        item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)
        user_vecs = user_tower(
            user_pos_items_t,
            user_pos_weights_t,
            user_neg_items_t,
            user_neg_weights_t
        ) # (8456, D + 1)
        scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)
        scores[~mask_tensor] = -1e9
        top100_vals, top100_indices = torch.topk(scores, 100, dim=1)
        top100_indices = top100_indices.cpu().numpy()

    records = []
    for r, uid in enumerate(target_uids):
        ranked_catalog_indices = top100_indices[r]
        ranked_item_ids = idx2item[ranked_catalog_indices]
        for rank, item_id in enumerate(ranked_item_ids, 1):
            records.append((int(uid), int(item_id), rank))

    val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])
    validated = validate_ranking(val_ranking_df, target_set)
    ranked = ranked_items(validated)
    score_report = score(ranked, graded)
    current_ndcg = score_report['quality_ndcg@10']
    print(f"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best: {best_ndcg:.6f})")
    
    if current_ndcg > best_ndcg:
        best_ndcg = current_ndcg
        val_ranking_path = Path('/app/solution/validation.parquet')
        val_ranking_df.to_parquet(val_ranking_path)
        torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')
        torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')
        print(f"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}")
records = []
for r, uid in enumerate(target_uids):
    ranked_catalog_indices = top100_indices[r]
    ranked_item_ids = idx2item[ranked_catalog_indices]
    for rank, item_id in enumerate(ranked_item_ids, 1):
...

Train 128-dim two-tower model and evaluate each epoch

+44:06

Model text

I have updated /app/solution/train_val.py with EMBED_DIM=128, OUTPUT_DIM=128, LR=1e-3, initial_bias * 1.0, and per-epoch evaluation against the public discovery metric.

Next step: Run train_val.py to evaluate validation performance across epochs.

Tool callrun_shell_commandTrain 128-dim two-tower model and evaluate each epoch Returned

Tool call · Input +44:06

{
  "description": "Train 128-dim two-tower model and evaluate each epoch",
  "command": "PYTHONPATH=/task python3 /app/solution/train_val.py"
}

Tool response +49:08

<untrusted_context>
Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.
</untrusted_context>

Benchmark step time with B=512, D=128, K=128

+49:30

Recorded reasoning summary 32 words

Investigating No Output

I'm looking into why the command produced no output before being canceled for exceeding its timeout. My current focus is on identifying the root cause of this unexpected silence.

Tool callrun_shell_commandBenchmark step time with B=512, D=128, K=128 Returned

Tool call · Input +49:30

{
  "description": "Benchmark step time with B=512, D=128, K=128",
  "command": "python3 -c \"\nimport torch\nimport time\n\ntorch.set_num_threads(8)\nB = 512\nD = 128\nK = 128\nL = 128\n\nt0 = time.time()\nfor _ in range(50):\n    u = torch.randn(B, D)\n    v = torch.randn(B, D)\n    v_neg = torch.randn(K, D)\n    pos = (u * v).sum(-1, keepdim=True)\n    neg = torch.matmul(u, v_neg.T)\n    logits = torch.cat([pos, neg], dim=1)\n    loss = torch.nn.functional.cross_entropy(logits, torch.zeros(B, dtype=torch.long))\n    loss.backward()\n\nprint(f'50 steps took {time.time() - t0:.3f}s')\n\""
}

Tool response +49:33

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 20, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py", line 647, in backward
    torch.autograd.backward(
  File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py", line 354, in backward
    _engine_run_backward(
  File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
Exit Code: 1
Process Group PGID: 3078
</untrusted_context>

Benchmark autograd backwards time

+49:43

Tool callrun_shell_commandBenchmark autograd backwards time Returned

Tool call · Input +49:43

{
  "description": "Benchmark autograd backwards time",
  "command": "python3 -c \"\nimport torch\nimport time\n\ntorch.set_num_threads(8)\nB = 512\nD = 128\nK = 128\n\nt0 = time.time()\nfor _ in range(50):\n    u = torch.randn(B, D, requires_grad=True)\n    v = torch.randn(B, D, requires_grad=True)\n    v_neg = torch.randn(K, D, requires_grad=True)\n    pos = (u * v).sum(-1, keepdim=True)\n    neg = torch.matmul(u, v_neg.T)\n    logits = torch.cat([pos, neg], dim=1)\n    loss = torch.nn.functional.cross_entropy(logits, torch.zeros(B, dtype=torch.long))\n    loss.backward()\n\nprint(f'50 steps took {time.time() - t0:.3f}s')\n\""
}

Tool response +49:47

<untrusted_context>
Output: 50 steps took 0.102s
Process Group PGID: 3108
</untrusted_context>

Benchmark Python DataLoader overhead on 800k items

+49:57

Recorded reasoning summary 45 words

Analyzing Script Performance

I've identified that a recent 50-step execution completed in just 0.102 seconds. My current focus is understanding why a previous script exhibited performance issues. I'm currently examining train_val.py, specifically the loop where tensor cloning and matching operations occur, to pinpoint the bottleneck.

Tool callrun_shell_commandBenchmark Python DataLoader overhead on 800k items Returned

Tool call · Input +49:57

{
  "description": "Benchmark Python DataLoader overhead on 800k items",
  "command": "python3 -c \"\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset, DataLoader\nimport time\n\nclass Dummy(Dataset):\n    def __init__(self, n):\n        self.a = np.arange(n)\n        self.b = np.arange(n)\n        self.c = np.arange(n, dtype=np.float32)\n    def __len__(self):\n        return len(self.a)\n    def __getitem__(self, idx):\n        return self.a[idx], self.b[idx], self.c[idx]\n\nn = 800000\nds = Dummy(n)\nloader = DataLoader(ds, batch_size=256, shuffle=True)\n\nt0 = time.time()\nfor i, batch in enumerate(loader):\n    if i == 500:\n        break\nprint(f'500 batches took {time.time() - t0:.2f}s -> {len(loader)} batches would take {(time.time() - t0) * (len(loader) / 500):.2f}s')\n\""
}

Tool response +50:00

<untrusted_context>
Output: 500 batches took 0.27s -> 3125 batches would take 1.68s
Process Group PGID: 3132
</untrusted_context>

Verify unbuffered output

+50:04

Recorded reasoning summary 41 words

Investigating Script Cancellation

I'm currently digging into why my script was automatically cancelled due to exceeding the 5-minute timeout without producing output. I'm reviewing the exact error message to understand the root cause, as this seems to be the primary blocker.

Tool callrun_shell_commandVerify unbuffered output Returned

Tool call · Input +50:04

{
  "command": "PYTHONPATH=/task python3 -u -c \"\nimport sys\nprint('Hello from unbuffered python!', flush=True)\n\"",
  "description": "Verify unbuffered output"
}

Tool response +50:04

<untrusted_context>
Output: Hello from unbuffered python!
Process Group PGID: 3145
</untrusted_context>

write_file train_val.py

+50:41

Recorded reasoning summary 33 words

Reviewing Training Timings

I'm examining the execution times from previous runs of train_val.py. Specifically, I've noted that each epoch averaged around 45 seconds. This provides a benchmark for performance during my current analysis.

Recorded reasoning summary 49 words

Optimizing Training Efficiency

I'm now focusing on speeding up train_val.py. My plan includes adding flush=True to prints and increasing BATCH_SIZE to 512 for faster epochs. I'll evaluate after epochs 1 through 4 to monitor progress and investigate why the neural model previously underperformed compared to the baseline popularity metric.

Recorded reasoning summary 45 words

Adjusting Ranking Bias

I'm exploring how to adjust initial_bias to prevent obscure tracks from dominating personalized recommendations. My hypothesis is that scaling initial_bias can significantly boost the impact of popular items, keeping recommendations grounded while still leveraging personalization. I plan to test this in Python.

Tool callwrite_file/app/solution/train_val.py Returned

Tool call · Input +50:41

{
  "file_path": "/app/solution/train_val.py",
  "content": "import time\nimport json\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import Dataset, DataLoader\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA_DIR = Path('/task/data')\nTRAIN_END = 24786800\nDAY_SEC = 86400\nNUM_CATALOG = 30000\nDEVICE = torch.device('cpu')\n\ntorch.set_num_threads(8)\ntorch.manual_seed(42)\nnp.random.seed(42)\n\nprint(\"Loading metadata...\", flush=True)\ncat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\nidx2item = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet(DATA_DIR / 'target_users.parquet')\ntarget_uids = targets.uid.astype(int).to_numpy()\ntarget_set = set(target_uids)\nuid2idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f\"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}\", flush=True)\n\n# Catalog features and normalized popularity\ncat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\ncat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)\n\nlog_pop = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))\ninitial_bias = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\nfeat_mean = cat_features.mean(axis=0, keepdims=True)\nfeat_std = cat_features.std(axis=0, keepdims=True) + 1e-6\ncat_features = (cat_features - feat_mean) / feat_std\ncat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)\n\n# 1. Active explicit preference state at validation cutoff\nprint(\"Loading validation preference state...\", flush=True)\nval_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked']\npref_dislikes = val_pref[val_pref.state == 'disliked']\n\nuser_pref_likes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_likes.uid, pref_likes.item_id):\n    if uid in user_pref_likes and item in item2idx:\n        user_pref_likes[uid].add(item2idx[item])\n\nuser_pref_dislikes = {uid: set() for uid in target_uids}\nfor uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):\n    if uid in user_pref_dislikes and item in item2idx:\n        user_pref_dislikes[uid].add(item2idx[item])\n\n# 2. Recent positive listens\nprint(\"Loading recent listens...\", flush=True)\ncutoff_90d = TRAIN_END - 90 * DAY_SEC\nlistens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]\n)\nlistens = listens[listens.uid.isin(target_set) & (listens.played_ratio_pct >= 50)]\nlistens['item_idx'] = listens.item_id.map(item2idx)\nlistens = listens.dropna(subset=['item_idx'])\nlistens['item_idx'] = listens['item_idx'].astype(int)\n\n# Group listens per user\nprint(\"Aggregating user histories...\", flush=True)\nlistens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)\nts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)\nlistens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))\n\nMAX_HIST_LEN = 128\nMAX_DISLIKE_LEN = 32\n\nuser_profiles = {}\n\nfor uid, group in listens.groupby('uid'):\n    item_weights = {}\n    for idx in user_pref_likes.get(uid, ()):\n        item_weights[idx] = 4.0\n        \n    for row in group.itertuples(index=False):\n        idx = row.item_idx\n        w = (2.0 if row.is_strong_organic else 1.0) * row.recency_weight\n        item_weights[idx] = item_weights.get(idx, 0.0) + w\n        \n    sorted_items = sorted(item_weights.items(), key=lambda x: x[1], reverse=True)[:MAX_HIST_LEN]\n    pos_items = [x[0] for x in sorted_items]\n    pos_weights = [x[1] for x in sorted_items]\n    \n    dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n    \n    user_profiles[uid] = {\n        'pos_items': np.array(pos_items, dtype=np.int64),\n        'pos_weights': np.array(pos_weights, dtype=np.float32),\n        'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n    }\n\nfor uid in target_uids:\n    if uid not in user_profiles:\n        p_likes = list(user_pref_likes.get(uid, ()))[:MAX_HIST_LEN]\n        p_weights = [4.0] * len(p_likes) if p_likes else [1.0]\n        p_items = p_likes if p_likes else [0]\n        dislikes = list(user_pref_dislikes.get(uid, ()))[:MAX_DISLIKE_LEN]\n        user_profiles[uid] = {\n            'pos_items': np.array(p_items, dtype=np.int64),\n            'pos_weights': np.array(p_weights, dtype=np.float32),\n            'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)\n        }\n\nprint(f\"User profiles built for {len(user_profiles)} users.\", flush=True)\n\n# 3. Training dataset of discovery pairs\nprint(\"Extracting training interaction pairs...\", flush=True)\nt_train_start = TRAIN_END - 60 * DAY_SEC\n\ntrain_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet', filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)])\ntrain_likes = train_likes[train_likes.uid.isin(target_set) & (train_likes.is_organic == 1)]\ntrain_likes['item_idx'] = train_likes.item_id.map(item2idx)\ntrain_likes = train_likes.dropna(subset=['item_idx'])\ntrain_likes['item_idx'] = train_likes['item_idx'].astype(int)\n\ntrain_listens = pd.read_parquet(\n    DATA_DIR / 'train_listens.parquet',\n    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n    filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)]\n)\ntrain_listens = train_listens[train_listens.uid.isin(target_set) & (train_listens.is_organic == 1) & (train_listens.played_ratio_pct >= 80)]\ntrain_listens['item_idx'] = train_listens.item_id.map(item2idx)\ntrain_listens = train_listens.dropna(subset=['item_idx'])\ntrain_listens['item_idx'] = train_listens['item_idx'].astype(int)\n\npair_dict = {}\nfor uid, idx in zip(train_listens.uid, train_listens.item_idx):\n    pair_dict[(int(uid), int(idx))] = 1\n\nfor uid, idx in zip(train_likes.uid, train_likes.item_idx):\n    pair_dict[(int(uid), int(idx))] = 3\n\ntrain_samples = []\nfor (uid, item_idx), grade in pair_dict.items():\n    w = 3.0 if grade == 3 else 1.0\n    train_samples.append((uid, item_idx, w))\n\nprint(f\"Total training pairs: {len(train_samples)}\", flush=True)\n\n# Convert profiles to fixed-size numpy arrays\nuser_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)\nuser_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)\nuser_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)\nuser_neg_weights = np.zeros((len(target_uids), MAX_DISLIKE_LEN), dtype=np.float32)\n\nfor uid, idx in uid2idx.items():\n    prof = user_profiles[uid]\n    p_len = min(len(prof['pos_items']), MAX_HIST_LEN)\n    user_pos_items[idx, :p_len] = prof['pos_items'][:p_len]\n    user_pos_weights[idx, :p_len] = prof['pos_weights'][:p_len]\n    \n    n_len = min(len(prof['neg_items']), MAX_DISLIKE_LEN)\n    if n_len > 0:\n        user_neg_items[idx, :n_len] = prof['neg_items'][:n_len]\n        user_neg_weights[idx, :n_len] = 1.0\n\nuser_pos_items_t = torch.from_numpy(user_pos_items)\nuser_pos_weights_t = torch.from_numpy(user_pos_weights)\nuser_neg_items_t = torch.from_numpy(user_neg_items)\nuser_neg_weights_t = torch.from_numpy(user_neg_weights)\n\n# Model definitions\nBIAS_SCALE = 2.0\n\nclass ItemTower(nn.Module):\n    def __init__(self, num_items, embed_dim, feat_dim, output_dim, init_bias, dropout=0.1):\n        super().__init__()\n        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)\n        nn.init.normal_(self.item_emb.weight, std=0.05)\n        self.feat_proj = nn.Linear(feat_dim, 16)\n        \n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim + 16, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        self.item_bias = nn.Embedding(num_items + 1, 1, padding_idx=num_items)\n        with torch.no_grad():\n            self.item_bias.weight[:num_items, 0] = torch.from_numpy(init_bias) * BIAS_SCALE\n            self.item_bias.weight[num_items, 0] = 0.0\n            \n    def forward(self, item_ids, feats):\n        emb = self.item_emb(item_ids)\n        feat_emb = self.feat_proj(feats)\n        combined = torch.cat([emb, feat_emb], dim=-1)\n        v = F.normalize(self.fc(combined), p=2, dim=-1)\n        b = self.item_bias(item_ids) # (N, 1)\n        return torch.cat([v, b], dim=-1) # (N, D + 1)\n\nclass UserTower(nn.Module):\n    def __init__(self, item_emb, embed_dim, output_dim, dropout=0.1):\n        super().__init__()\n        self.item_emb = item_emb\n        self.fc = nn.Sequential(\n            nn.Linear(embed_dim * 2, embed_dim),\n            nn.LayerNorm(embed_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(embed_dim, output_dim)\n        )\n        self.bias_scale = nn.Sequential(\n            nn.Linear(embed_dim * 2, 16),\n            nn.ReLU(),\n            nn.Linear(16, 1),\n            nn.Sigmoid()\n        )\n        \n    def forward(self, pos_items, pos_weights, neg_items, neg_weights):\n        pos_emb = self.item_emb(pos_items)\n        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)\n        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        pooled_pos = w_pos / w_pos_norm\n        \n        neg_emb = self.item_emb(neg_items)\n        w_neg = (neg_emb * neg_weights.unsqueeze(-1)).sum(dim=1)\n        w_neg_norm = neg_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)\n        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()\n        pooled_neg = (w_neg / w_neg_norm) * has_neg\n        \n        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)\n        u = F.normalize(self.fc(combined), p=2, dim=-1)\n        # Scale range: [0.75, 1.25] centered around 1.0\n        scale = self.bias_scale(combined) * 0.5 + 0.75\n        return torch.cat([u, scale], dim=-1) # (B, D + 1)\n\nclass FastDataset(Dataset):\n    def __init__(self, samples, uid2idx):\n        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)\n        self.target_items = np.array([s[1] for s in samples], dtype=np.int64)\n        self.weights = np.array([s[2] for s in samples], dtype=np.float32)\n        \n    def __len__(self):\n        return len(self.u_indices)\n        \n    def __getitem__(self, idx):\n        return self.u_indices[idx], self.target_items[idx], self.weights[idx]\n\nEMBED_DIM = 64\nOUTPUT_DIM = 64\nFEAT_DIM = 3\nBATCH_SIZE = 512\nK_NEG = 128\nLR = 1.5e-3\nTEMPERATURE = 0.08\nEPOCHS = 5\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\nparams = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\", flush=True)\n\neligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\ngraded = validation_truth(eligibility, target_set)\n\nbest_ndcg = 0.0\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        p_items = user_pos_items_t[u_idxs].clone()\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n        \n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n        \n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n        \n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 400 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\", flush=True)\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\", flush=True)\n\n    # Evaluate validation score after each epoch\n    item_tower.eval()\n    user_tower.eval()\n    with torch.no_grad():\n        all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n        item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n        user_vecs = user_tower(\n            user_pos_items_t,\n            user_pos_weights_t,\n            user_neg_items_t,\n            user_neg_weights_t\n        ) # (8456, D + 1)\n        scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n        scores[~mask_tensor] = -1e9\n        top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n        top100_indices = top100_indices.cpu().numpy()\n\n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n\n    val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_ranking_df, target_set)\n    ranked = ranked_items(validated)\n    score_report = score(ranked, graded)\n    current_ndcg = score_report['quality_ndcg@10']\n    print(f\"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best so far: {best_ndcg:.6f})\", flush=True)\n    \n    if current_ndcg > best_ndcg:\n        best_ndcg = current_ndcg\n        val_ranking_path = Path('/app/solution/validation.parquet')\n        val_ranking_df.to_parquet(val_ranking_path)\n        torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')\n        torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')\n        print(f\"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}\", flush=True)\n\nprint(f\"\\nFinal Best Validation NDCG@10: {best_ndcg:.6f}\", flush=True)\n"
}

Tool response +50:41

Successfully overwrote file: /app/solution/train_val.py. Here is the updated code:
...

torch.set_num_threads(8)
torch.manual_seed(42)
np.random.seed(42)

print("Loading metadata...", flush=True)
cat = pd.read_parquet(DATA_DIR / 'candidate_catalog.parquet')
item2idx = {int(item): idx for idx, item in enumerate(cat.item_id)}
idx2item = cat.item_id.to_numpy()

targets = pd.read_parquet(DATA_DIR / 'target_users.parquet')
target_uids = targets.uid.astype(int).to_numpy()
target_set = set(target_uids)
uid2idx = {uid: idx for idx, uid in enumerate(target_uids)}

print(f"Target users: {len(target_uids)}, Catalog items: {NUM_CATALOG}", flush=True)

# Catalog features and normalized popularity
cat_features = np.zeros((NUM_CATALOG, 3), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 1] = np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))
cat_features[:, 2] = cat.popularity_rank.to_numpy(dtype=np.float32) / float(NUM_CATALOG)

log_pop = np.log1p(cat.positive_listens.to_numpy(dtype=np.float32))
initial_bias = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

feat_mean = cat_features.mean(axis=0, keepdims=True)
feat_std = cat_features.std(axis=0, keepdims=True) + 1e-6
cat_features = (cat_features - feat_mean) / feat_std
cat_features_tensor = torch.from_numpy(cat_features).to(DEVICE)

# 1. Active explicit preference state at validation cutoff
print("Loading validation preference state...", flush=True)
val_pref = pd.read_parquet(DATA_DIR / 'validation_preference_state.parquet')
pref_likes = val_pref[val_pref.state == 'liked']
pref_dislikes = val_pref[val_pref.state == 'disliked']

user_pref_likes = {uid: set() for uid in target_uids}
...
user_pref_dislikes = {uid: set() for uid in target_uids}
for uid, item in zip(pref_dislikes.uid, pref_dislikes.item_id):
    if uid in user_pref_dislikes and item in item2idx:
        user_pref_dislikes[uid].add(item2idx[item])

# 2. Recent positive listens
print("Loading recent listens...", flush=True)
cutoff_90d = TRAIN_END - 90 * DAY_SEC
listens = pd.read_parquet(
    DATA_DIR / 'train_listens.parquet',
    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],
    filters=[('timestamp', '>=', cutoff_90d), ('timestamp', '<', TRAIN_END)]
...
listens['item_idx'] = listens.item_id.map(item2idx)
listens = listens.dropna(subset=['item_idx'])
listens['item_idx'] = listens['item_idx'].astype(int)

# Group listens per user
print("Aggregating user histories...", flush=True)
listens['is_strong_organic'] = (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)
ts_diff = (listens.timestamp.astype(np.float64) - float(TRAIN_END)) / (30.0 * DAY_SEC)
listens['recency_weight'] = np.exp(np.clip(ts_diff, -10.0, 0.0))

MAX_HIST_LEN = 128
MAX_DISLIKE_LEN = 32

user_profiles = {}

...
            'pos_items': np.array(p_items, dtype=np.int64),
            'pos_weights': np.array(p_weights, dtype=np.float32),
            'neg_items': np.array(dislikes, dtype=np.int64) if dislikes else np.array([], dtype=np.int64)
        }

print(f"User profiles built for {len(user_profiles)} users.", flush=True)

# 3. Training dataset of discovery pairs
print("Extracting training interaction pairs...", flush=True)
t_train_start = TRAIN_END - 60 * DAY_SEC

train_likes = pd.read_parquet(DATA_DIR / 'train_likes.parquet', filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)])
train_likes = train_likes[train_likes.uid.isin(target_set) & (train_likes.is_organic == 1)]
train_likes['item_idx'] = train_likes.item_id.map(item2idx)
train_likes = train_likes.dropna(subset=['item_idx'])
train_likes['item_idx'] = train_likes['item_idx'].astype(int)

train_listens = pd.read_parquet(
    DATA_DIR / 'train_listens.parquet',
    columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],
    filters=[('timestamp', '>=', t_train_start), ('timestamp', '<', TRAIN_END)]
)
...
train_samples = []
for (uid, item_idx), grade in pair_dict.items():
    w = 3.0 if grade == 3 else 1.0
    train_samples.append((uid, item_idx, w))

print(f"Total training pairs: {len(train_samples)}", flush=True)

# Convert profiles to fixed-size numpy arrays
user_pos_items = np.full((len(target_uids), MAX_HIST_LEN), NUM_CATALOG, dtype=np.int64)
user_pos_weights = np.zeros((len(target_uids), MAX_HIST_LEN), dtype=np.float32)
user_neg_items = np.full((len(target_uids), MAX_DISLIKE_LEN), NUM_CATALOG, dtype=np.int64)
...
user_pos_weights_t = torch.from_numpy(user_pos_weights)
user_neg_items_t = torch.from_numpy(user_neg_items)
user_neg_weights_t = torch.from_numpy(user_neg_weights)

# Model definitions
BIAS_SCALE = 2.0

class ItemTower(nn.Module):
    def __init__(self, num_items, embed_dim, feat_dim, output_dim, init_bias, dropout=0.1):
        super().__init__()
        self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=num_items)
        nn.init.normal_(self.item_emb.weight, std=0.05)
...
            nn.Dropout(dropout),
            nn.Linear(embed_dim, output_dim)
        )
        self.item_bias = nn.Embedding(num_items + 1, 1, padding_idx=num_items)
        with torch.no_grad():
            self.item_bias.weight[:num_items, 0] = torch.from_numpy(init_bias) * BIAS_SCALE
            self.item_bias.weight[num_items, 0] = 0.0
            
    def forward(self, item_ids, feats):
        emb = self.item_emb(item_ids)
        feat_emb = self.feat_proj(feats)
...
            nn.Linear(16, 1),
            nn.Sigmoid()
        )
        
    def forward(self, pos_items, pos_weights, neg_items, neg_weights):
        pos_emb = self.item_emb(pos_items)
        w_pos = (pos_emb * pos_weights.unsqueeze(-1)).sum(dim=1)
        w_pos_norm = pos_weights.sum(dim=1, keepdim=True).clamp(min=1e-6)
        pooled_pos = w_pos / w_pos_norm
        
        neg_emb = self.item_emb(neg_items)
...
        has_neg = (neg_weights.sum(dim=1, keepdim=True) > 0).float()
        pooled_neg = (w_neg / w_neg_norm) * has_neg
        
        combined = torch.cat([pooled_pos, pooled_neg], dim=-1)
        u = F.normalize(self.fc(combined), p=2, dim=-1)
        # Scale range: [0.75, 1.25] centered around 1.0
        scale = self.bias_scale(combined) * 0.5 + 0.75
        return torch.cat([u, scale], dim=-1) # (B, D + 1)

class FastDataset(Dataset):
    def __init__(self, samples, uid2idx):
        self.u_indices = np.array([uid2idx[s[0]] for s in samples], dtype=np.int64)
...
        return len(self.u_indices)
        
    def __getitem__(self, idx):
        return self.u_indices[idx], self.target_items[idx], self.weights[idx]

EMBED_DIM = 64
OUTPUT_DIM = 64
FEAT_DIM = 3
BATCH_SIZE = 512
K_NEG = 128
LR = 1.5e-3
TEMPERATURE = 0.08
EPOCHS = 5

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)
user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

params = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]
optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)

train_ds = FastDataset(train_samples, uid2idx)
train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

print(f"Starting training: {len(train_loader)} batches per epoch...", flush=True)

eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')
unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]
mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)
graded = validation_truth(eligibility, target_set)
...
        p_items = user_pos_items_t[u_idxs].clone()
        p_weights = user_pos_weights_t[u_idxs].clone()
        n_items = user_neg_items_t[u_idxs]
        n_weights = user_neg_weights_t[u_idxs]
        
        match_mask = (p_items == tgt_items.unsqueeze(-1))
        p_weights[match_mask] = 0.0
        
        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))
        
        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)
        
        pos_feats = cat_features_tensor[tgt_items]
        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)
        
        neg_feats = cat_features_tensor[neg_items]
        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)
        
        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE
        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE
        
        all_logits = torch.cat([pos_logits, neg_logits], dim=1)
        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)
        
        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')
        loss = (loss_per_sample * sample_w).mean()
        
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
        if (batch_idx + 1) % 400 == 0:
            avg_b_loss = total_loss / (batch_idx + 1)
            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()
            print(f"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}", flush=True)
            
    print(f"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}", flush=True)

    # Evaluate validation score after each epoch
    item_tower.eval()
    user_tower.eval()
    with torch.no_grad():
...
    val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])
    validated = validate_ranking(val_ranking_df, target_set)
    ranked = ranked_items(validated)
    score_report = score(ranked, graded)
    current_ndcg = score_report['quality_ndcg@10']
    print(f"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best so far: {best_ndcg:.6f})", flush=True)
    
    if current_ndcg > best_ndcg:
        best_ndcg = current_ndcg
        val_ranking_path = Path('/app/solution/validation.parquet')
        val_ranking_df.to_parquet(val_ranking_path)
        torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')
        torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')
        print(f"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}", flush=True)

print(f"\nFinal Best Validation NDCG@10: {best_ndcg:.6f}", flush=True)

Run train_val.py with unbuffered output and monitor validation NDCG

+50:44

Model text

I have updated /app/solution/train_val.py with flush=True on all outputs, BATCH_SIZE=512, BIAS_SCALE=2.0, and epoch-by-epoch evaluation and checkpointing.

Next step: Execute python3 -u /app/solution/train_val.py to monitor training progress and observe validation NDCG@10 across epochs.

Tool callrun_shell_commandRun train_val.py with unbuffered output and monitor validation NDCG Returned

Tool call · Input +50:44

{
  "command": "PYTHONPATH=/task python3 -u /app/solution/train_val.py",
  "description": "Run train_val.py with unbuffered output and monitor validation NDCG"
}

Tool response +54:16

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Starting training: 1638 batches per epoch...
Epoch 1 [400/1638] Loss: 5.1988, Top-1 Acc: 0.037
Epoch 1 [800/1638] Loss: 5.0681, Top-1 Acc: 0.094
Epoch 1 [1200/1638] Loss: 4.9515, Top-1 Acc: 0.105
Epoch 1 [1600/1638] Loss: 4.8595, Top-1 Acc: 0.072
Epoch 1 finished in 34.27s, Avg Loss: 4.8509
--> Epoch 1 Validation NDCG@10: 0.005847 (Best so far: 0.000000)
Saved new best model checkpoint with NDCG@10 = 0.005847
Epoch 2 [400/1638] Loss: 4.3883, Top-1 Acc: 0.131
Epoch 2 [800/1638] Loss: 4.3721, Top-1 Acc: 0.049
Epoch 2 [1200/1638] Loss: 4.3514, Top-1 Acc: 0.004
Epoch 2 [1600/1638] Loss: 4.3326, Top-1 Acc: 0.207
Epoch 2 finished in 34.75s, Avg Loss: 4.3311
--> Epoch 2 Validation NDCG@10: 0.006319 (Best so far: 0.005847)
Saved new best model checkpoint with NDCG@10 = 0.006319
Epoch 3 [400/1638] Loss: 4.1473, Top-1 Acc: 0.094
Epoch 3 [800/1638] Loss: 4.1536, Top-1 Acc: 0.125
Epoch 3 [1200/1638] Loss: 4.1479, Top-1 Acc: 0.201
Epoch 3 [1600/1638] Loss: 4.1456, Top-1 Acc: 0.111
Epoch 3 finished in 34.29s, Avg Loss: 4.1466
--> Epoch 3 Validation NDCG@10: 0.009438 (Best so far: 0.006319)
Saved new best model checkpoint with NDCG@10 = 0.009438
Epoch 4 [400/1638] Loss: 4.0273, Top-1 Acc: 0.154
Epoch 4 [800/1638] Loss: 4.0238, Top-1 Acc: 0.191
Epoch 4 [1200/1638] Loss: 4.0314, Top-1 Acc: 0.123
Epoch 4 [1600/1638] Loss: 4.0297, Top-1 Acc: 0.074
Epoch 4 finished in 34.16s, Avg Loss: 4.0295
--> Epoch 4 Validation NDCG@10: 0.010204 (Best so far: 0.009438)
Saved new best model checkpoint with NDCG@10 = 0.010204
Epoch 5 [400/1638] Loss: 3.9178, Top-1 Acc: 0.150
Epoch 5 [800/1638] Loss: 3.9293, Top-1 Acc: 0.160
Epoch 5 [1200/1638] Loss: 3.9377, Top-1 Acc: 0.164
Epoch 5 [1600/1638] Loss: 3.9389, Top-1 Acc: 0.211
Epoch 5 finished in 34.65s, Avg Loss: 3.9397
--> Epoch 5 Validation NDCG@10: 0.010172 (Best so far: 0.010204)

Final Best Validation NDCG@10: 0.010204
Process Group PGID: 3154
</untrusted_context>

Verify loaded checkpoint state dicts

+54:31

Recorded reasoning summary 47 words

Observing the Training

I'm noticing a steady decrease in my loss function, with Epoch 4 showing a promising 0.010204, all achieved within a swift 34 seconds per epoch. I'm now focusing on the specific dynamics observed between epochs 3, 4, and 5 to understand this trend better.

Recorded reasoning summary 45 words

Adjusting Bias Scale

My NDCG@10 is now 0.010204, and I'm aiming to surpass the 0.01071 popularity baseline. I'm investigating the impact of increasing the BIAS_SCALE to 3.0, 4.0, and even 3.5, to observe how strengthening the popularity prior influences re-ordering based on personalized user affinity.

Tool callrun_shell_commandVerify loaded checkpoint state dicts Returned

Tool call · Input +54:31

{
  "command": "PYTHONPATH=/task python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nidx2item = cat.item_id.to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\ngraded = validation_truth(eligibility, target_set)\n\n# Load best user tower and item tower checkpoints\n# First inspect what keys exist in saved state dict\nu_sd = torch.load('/app/solution/best_user_tower.pt')\ni_sd = torch.load('/app/solution/best_item_tower.pt')\nprint('Loaded state dicts successfully!')\n\"",
  "description": "Verify loaded checkpoint state dicts"
}

Tool response +54:36

<untrusted_context>
Output: Loaded state dicts successfully!
Process Group PGID: 3207
</untrusted_context>

Evaluate sensitivity to popularity bias scaling

+54:44

Recorded reasoning summary 51 words

Testing $\beta$ Scalings

I'm planning to run an evaluation on the validation set. My focus is on testing several $\beta$ scaling factors, specifically within the range of 1.0 to 5.0, to assess their impact on the bias dimension using the existing saved model. This will be done through a Python test.

Tool callrun_shell_commandEvaluate sensitivity to popularity bias scaling Returned

Tool call · Input +54:44

{
  "description": "Evaluate sensitivity to popularity bias scaling",
  "command": "PYTHONPATH=/task python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nidx2item = cat.item_id.to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool))\ngraded = validation_truth(eligibility, target_set)\n\n# Import model classes\nfrom train_val import ItemTower, UserTower, user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t, cat_features_tensor, NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias)\nitem_tower.load_state_dict(torch.load('/app/solution/best_item_tower.pt'))\nitem_tower.eval()\n\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM)\nuser_tower.load_state_dict(torch.load('/app/solution/best_user_tower.pt'))\nuser_tower.eval()\n\nwith torch.no_grad():\n    all_item_ids = torch.arange(NUM_CATALOG)\n    raw_item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n    raw_user_vecs = user_tower(user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t) # (8456, D + 1)\n\n    # Separate personalized part and bias part\n    u_pers = raw_user_vecs[:, :-1]\n    u_scale = raw_user_vecs[:, -1:]\n    \n    i_pers = raw_item_vecs[:, :-1]\n    i_bias = raw_item_vecs[:, -1:]\n    \n    pers_scores = torch.matmul(u_pers, i_pers.T) # (8456, 30000)\n    bias_scores = torch.matmul(u_scale, i_bias.T) # (8456, 30000)\n\nfor beta in [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0]:\n    scores = (pers_scores + beta * bias_scores).clone()\n    scores[~mask_tensor] = -1e9\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.numpy()\n    \n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n            \n    val_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_df, target_set)\n    res = score(ranked_items(validated), graded)\n    print(f'Beta {beta:4.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}

Tool response +54:49

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 23, in <module>
ModuleNotFoundError: No module named 'train_val'
Exit Code: 1
Process Group PGID: 3227
</untrusted_context>

Evaluate beta parameter grid on validation NDCG

+54:56

Tool callrun_shell_commandEvaluate beta parameter grid on validation NDCG Returned

Tool call · Input +54:56

{
  "description": "Evaluate beta parameter grid on validation NDCG",
  "command": "PYTHONPATH=/task:/app/solution python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nsys.path.append('/app/solution')\n\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nidx2item = cat.item_id.to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool))\ngraded = validation_truth(eligibility, target_set)\n\nfrom train_val import ItemTower, UserTower, user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t, cat_features_tensor, NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias)\nitem_tower.load_state_dict(torch.load('/app/solution/best_item_tower.pt'))\nitem_tower.eval()\n\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM)\nuser_tower.load_state_dict(torch.load('/app/solution/best_user_tower.pt'))\nuser_tower.eval()\n\nwith torch.no_grad():\n    all_item_ids = torch.arange(NUM_CATALOG)\n    raw_item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n    raw_user_vecs = user_tower(user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t) # (8456, D + 1)\n\n    u_pers = raw_user_vecs[:, :-1]\n    u_scale = raw_user_vecs[:, -1:]\n    \n    i_pers = raw_item_vecs[:, :-1]\n    i_bias = raw_item_vecs[:, -1:]\n    \n    pers_scores = torch.matmul(u_pers, i_pers.T)\n    bias_scores = torch.matmul(u_scale, i_bias.T)\n\nfor beta in [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0]:\n    scores = (pers_scores + beta * bias_scores).clone()\n    scores[~mask_tensor] = -1e9\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.numpy()\n    \n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n            \n    val_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_df, target_set)\n    res = score(ranked_items(validated), graded)\n    print(f'Beta {beta:4.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}

Tool response +58:25

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Starting training: 1638 batches per epoch...
Epoch 1 [400/1638] Loss: 5.1988, Top-1 Acc: 0.037
Epoch 1 [800/1638] Loss: 5.0681, Top-1 Acc: 0.094
Epoch 1 [1200/1638] Loss: 4.9515, Top-1 Acc: 0.105
Epoch 1 [1600/1638] Loss: 4.8595, Top-1 Acc: 0.072
Epoch 1 finished in 33.07s, Avg Loss: 4.8509
--> Epoch 1 Validation NDCG@10: 0.005847 (Best so far: 0.000000)
Saved new best model checkpoint with NDCG@10 = 0.005847
Epoch 2 [400/1638] Loss: 4.3883, Top-1 Acc: 0.131
Epoch 2 [800/1638] Loss: 4.3721, Top-1 Acc: 0.049
Epoch 2 [1200/1638] Loss: 4.3514, Top-1 Acc: 0.004
Epoch 2 [1600/1638] Loss: 4.3326, Top-1 Acc: 0.207
Epoch 2 finished in 34.70s, Avg Loss: 4.3311
--> Epoch 2 Validation NDCG@10: 0.006319 (Best so far: 0.005847)
Saved new best model checkpoint with NDCG@10 = 0.006319
Epoch 3 [400/1638] Loss: 4.1473, Top-1 Acc: 0.094
Epoch 3 [800/1638] Loss: 4.1536, Top-1 Acc: 0.125
Epoch 3 [1200/1638] Loss: 4.1479, Top-1 Acc: 0.201
Epoch 3 [1600/1638] Loss: 4.1456, Top-1 Acc: 0.111
Epoch 3 finished in 33.16s, Avg Loss: 4.1466
--> Epoch 3 Validation NDCG@10: 0.009438 (Best so far: 0.006319)
Saved new best model checkpoint with NDCG@10 = 0.009438
Epoch 4 [400/1638] Loss: 4.0273, Top-1 Acc: 0.154
Epoch 4 [800/1638] Loss: 4.0238, Top-1 Acc: 0.191
Epoch 4 [1200/1638] Loss: 4.0314, Top-1 Acc: 0.123
Epoch 4 [1600/1638] Loss: 4.0297, Top-1 Acc: 0.074
Epoch 4 finished in 34.12s, Avg Loss: 4.0295
--> Epoch 4 Validation NDCG@10: 0.010204 (Best so far: 0.009438)
Saved new best model checkpoint with NDCG@10 = 0.010204
Epoch 5 [400/1638] Loss: 3.9178, Top-1 Acc: 0.150
Epoch 5 [800/1638] Loss: 3.9293, Top-1 Acc: 0.160
Epoch 5 [1200/1638] Loss: 3.9377, Top-1 Acc: 0.164
Epoch 5 [1600/1638] Loss: 3.9389, Top-1 Acc: 0.211
Epoch 5 finished in 33.64s, Avg Loss: 3.9397
--> Epoch 5 Validation NDCG@10: 0.010172 (Best so far: 0.010204)

Final Best Validation NDCG@10: 0.010204
/usr/bin/bash: line 71:  3249 Killed                  PYTHONPATH=/task:/app/solution python3 -c "
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pandas as pd
from pathlib import Path
import sys
sys.path.append('/task')
sys.path.append('/app/solution')

from evaluate_public import validation_truth, score
from ranking_evaluation import CandidateSet, ranked_items, validate_ranking

data_dir = Path('/task/data')
targets = pd.read_parquet(data_dir / 'target_users.parquet')
target_set = set(targets.uid)
target_uids = targets.uid.astype(int).to_numpy()
cat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')
idx2item = cat.item_id.to_numpy()
eligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')
unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]
mask_tensor = torch.from_numpy(unpacked_mask.astype(bool))
graded = validation_truth(eligibility, target_set)

from train_val import ItemTower, UserTower, user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t, cat_features_tensor, NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias

item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias)
item_tower.load_state_dict(torch.load('/app/solution/best_item_tower.pt'))
item_tower.eval()

user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM)
user_tower.load_state_dict(torch.load('/app/solution/best_user_tower.pt'))
user_tower.eval()

with torch.no_grad():
    all_item_ids = torch.arange(NUM_CATALOG)
    raw_item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)
    raw_user_vecs = user_tower(user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t) # (8456, D + 1)

    u_pers = raw_user_vecs[:, :-1]
    u_scale = raw_user_vecs[:, -1:]
    
    i_pers = raw_item_vecs[:, :-1]
    i_bias = raw_item_vecs[:, -1:]
    
    pers_scores = torch.matmul(u_pers, i_pers.T)
    bias_scores = torch.matmul(u_scale, i_bias.T)

for beta in [0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0]:
    scores = (pers_scores + beta * bias_scores).clone()
    scores[~mask_tensor] = -1e9
    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)
    top100_indices = top100_indices.numpy()
    
    records = []
    for r, uid in enumerate(target_uids):
        ranked_catalog_indices = top100_indices[r]
        ranked_item_ids = idx2item[ranked_catalog_indices]
        for rank, item_id in enumerate(ranked_item_ids, 1):
            records.append((int(uid), int(item_id), rank))
            
    val_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])
    validated = validate_ranking(val_df, target_set)
    res = score(ranked_items(validated), graded)
    print(f'Beta {beta:4.1f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}')
"
Exit Code: 137
Process Group PGID: 3247
</untrusted_context>

replace train_val.py

+58:50

Recorded reasoning summary 43 words

Refining Import Statements

I've identified a critical issue: re-importing from train_val inadvertently triggered a full training loop, consuming all available memory. My current focus is on implementing if __name__ == '__main__': guards within train_val.py to prevent this recursive execution and ensure stable development.

Tool callreplace/app/solution/train_val.py Returned

Tool call · Input +58:50

{
  "allow_multiple": false,
  "old_string": "item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\nparams = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\noptimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\ntrain_ds = FastDataset(train_samples, uid2idx)\ntrain_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\nprint(f\"Starting training: {len(train_loader)} batches per epoch...\", flush=True)\n\neligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\ngraded = validation_truth(eligibility, target_set)\n\nbest_ndcg = 0.0\n\nfor epoch in range(1, EPOCHS + 1):\n    item_tower.train()\n    user_tower.train()\n    total_loss = 0.0\n    t0 = time.time()\n    \n    for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n        optimizer.zero_grad()\n        \n        p_items = user_pos_items_t[u_idxs].clone()\n        p_weights = user_pos_weights_t[u_idxs].clone()\n        n_items = user_neg_items_t[u_idxs]\n        n_weights = user_neg_weights_t[u_idxs]\n        \n        match_mask = (p_items == tgt_items.unsqueeze(-1))\n        p_weights[match_mask] = 0.0\n        \n        neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n        \n        u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n        \n        pos_feats = cat_features_tensor[tgt_items]\n        pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n        \n        neg_feats = cat_features_tensor[neg_items]\n        neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n        \n        pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE\n        neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE\n        \n        all_logits = torch.cat([pos_logits, neg_logits], dim=1)\n        labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n        \n        loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n        loss = (loss_per_sample * sample_w).mean()\n        \n        loss.backward()\n        optimizer.step()\n        \n        total_loss += loss.item()\n        if (batch_idx + 1) % 400 == 0:\n            avg_b_loss = total_loss / (batch_idx + 1)\n            acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n            print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\", flush=True)\n            \n    print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\", flush=True)\n\n    # Evaluate validation score after each epoch\n    item_tower.eval()\n    user_tower.eval()\n    with torch.no_grad():\n        all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n        item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n        user_vecs = user_tower(\n            user_pos_items_t,\n            user_pos_weights_t,\n            user_neg_items_t,\n            user_neg_weights_t\n        ) # (8456, D + 1)\n        scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n        scores[~mask_tensor] = -1e9\n        top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n        top100_indices = top100_indices.cpu().numpy()\n\n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n\n    val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_ranking_df, target_set)\n    ranked = ranked_items(validated)\n    score_report = score(ranked, graded)\n    current_ndcg = score_report['quality_ndcg@10']\n    print(f\"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best so far: {best_ndcg:.6f})\", flush=True)\n    \n    if current_ndcg > best_ndcg:\n        best_ndcg = current_ndcg\n        val_ranking_path = Path('/app/solution/validation.parquet')\n        val_ranking_df.to_parquet(val_ranking_path)\n        torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')\n        torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')\n        print(f\"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}\", flush=True)\n\nprint(f\"\\nFinal Best Validation NDCG@10: {best_ndcg:.6f}\", flush=True)",
  "file_path": "/app/solution/train_val.py",
  "new_string": "if __name__ == '__main__':\n    item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)\n    user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)\n\n    params = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]\n    optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)\n\n    train_ds = FastDataset(train_samples, uid2idx)\n    train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)\n\n    print(f\"Starting training: {len(train_loader)} batches per epoch...\", flush=True)\n\n    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')\n    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]\n    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)\n    graded = validation_truth(eligibility, target_set)\n\n    best_ndcg = 0.0\n\n    for epoch in range(1, EPOCHS + 1):\n        item_tower.train()\n        user_tower.train()\n        total_loss = 0.0\n        t0 = time.time()\n        \n        for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):\n            optimizer.zero_grad()\n            \n            p_items = user_pos_items_t[u_idxs].clone()\n            p_weights = user_pos_weights_t[u_idxs].clone()\n            n_items = user_neg_items_t[u_idxs]\n            n_weights = user_neg_weights_t[u_idxs]\n            \n            match_mask = (p_items == tgt_items.unsqueeze(-1))\n            p_weights[match_mask] = 0.0\n            \n            neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))\n            \n            u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)\n            \n            pos_feats = cat_features_tensor[tgt_items]\n            pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)\n            \n            neg_feats = cat_features_tensor[neg_items]\n            neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)\n            \n            pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE\n            neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE\n            \n            all_logits = torch.cat([pos_logits, neg_logits], dim=1)\n            labels = torch.zeros(BATCH_SIZE, dtype=torch.long)\n            \n            loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')\n            loss = (loss_per_sample * sample_w).mean()\n            \n            loss.backward()\n            optimizer.step()\n            \n            total_loss += loss.item()\n            if (batch_idx + 1) % 400 == 0:\n                avg_b_loss = total_loss / (batch_idx + 1)\n                acc = (all_logits.argmax(dim=1) == 0).float().mean().item()\n                print(f\"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}\", flush=True)\n                \n        print(f\"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}\", flush=True)\n\n        # Evaluate validation score after each epoch\n        item_tower.eval()\n        user_tower.eval()\n        with torch.no_grad():\n            all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)\n            item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n            user_vecs = user_tower(\n                user_pos_items_t,\n                user_pos_weights_t,\n                user_neg_items_t,\n                user_neg_weights_t\n            ) # (8456, D + 1)\n            scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n            scores[~mask_tensor] = -1e9\n            top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n            top100_indices = top100_indices.cpu().numpy()\n\n        records = []\n        for r, uid in enumerate(target_uids):\n            ranked_catalog_indices = top100_indices[r]\n            ranked_item_ids = idx2item[ranked_catalog_indices]\n            for rank, item_id in enumerate(ranked_item_ids, 1):\n                records.append((int(uid), int(item_id), rank))\n\n        val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n        validated = validate_ranking(val_ranking_df, target_set)\n        ranked = ranked_items(validated)\n        score_report = score(ranked, graded)\n        current_ndcg = score_report['quality_ndcg@10']\n        print(f\"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best so far: {best_ndcg:.6f})\", flush=True)\n        \n        if current_ndcg > best_ndcg:\n            best_ndcg = current_ndcg\n            val_ranking_path = Path('/app/solution/validation.parquet')\n            val_ranking_df.to_parquet(val_ranking_path)\n            torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')\n            torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')\n            print(f\"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}\", flush=True)\n\n    print(f\"\\nFinal Best Validation NDCG@10: {best_ndcg:.6f}\", flush=True)",
  "instruction": "Wrap the training and evaluation loop under if __name__ == '__main__':"
}

Tool response +58:50

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
K_NEG = 128
LR = 1.5e-3
TEMPERATURE = 0.08
EPOCHS = 5

if __name__ == '__main__':
    item_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias).to(DEVICE)
    user_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM).to(DEVICE)

    params = list(user_tower.parameters()) + [p for n, p in item_tower.named_parameters() if 'item_emb' not in n]
    optimizer = torch.optim.Adam(params, lr=LR, weight_decay=1e-5)

    train_ds = FastDataset(train_samples, uid2idx)
    train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)

    print(f"Starting training: {len(train_loader)} batches per epoch...", flush=True)

    eligibility = CandidateSet(DATA_DIR / 'validation_eligible_candidates.npz')
    unpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :NUM_CATALOG]
    mask_tensor = torch.from_numpy(unpacked_mask.astype(bool)).to(DEVICE)
    graded = validation_truth(eligibility, target_set)

    best_ndcg = 0.0

    for epoch in range(1, EPOCHS + 1):
        item_tower.train()
        user_tower.train()
        total_loss = 0.0
        t0 = time.time()
        
        for batch_idx, (u_idxs, tgt_items, sample_w) in enumerate(train_loader):
            optimizer.zero_grad()
            
            p_items = user_pos_items_t[u_idxs].clone()
            p_weights = user_pos_weights_t[u_idxs].clone()
            n_items = user_neg_items_t[u_idxs]
            n_weights = user_neg_weights_t[u_idxs]
            
            match_mask = (p_items == tgt_items.unsqueeze(-1))
            p_weights[match_mask] = 0.0
            
            neg_items = torch.randint(0, NUM_CATALOG, (K_NEG,))
            
            u_vecs = user_tower(p_items, p_weights, n_items, n_weights) # (B, D + 1)
            
            pos_feats = cat_features_tensor[tgt_items]
            pos_vecs = item_tower(tgt_items, pos_feats) # (B, D + 1)
            
            neg_feats = cat_features_tensor[neg_items]
            neg_vecs = item_tower(neg_items, neg_feats) # (K_NEG, D + 1)
            
            pos_logits = (u_vecs * pos_vecs).sum(dim=-1, keepdim=True) / TEMPERATURE
            neg_logits = torch.matmul(u_vecs, neg_vecs.T) / TEMPERATURE
            
            all_logits = torch.cat([pos_logits, neg_logits], dim=1)
            labels = torch.zeros(BATCH_SIZE, dtype=torch.long)
            
            loss_per_sample = F.cross_entropy(all_logits, labels, reduction='none')
            loss = (loss_per_sample * sample_w).mean()
            
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
            if (batch_idx + 1) % 400 == 0:
                avg_b_loss = total_loss / (batch_idx + 1)
                acc = (all_logits.argmax(dim=1) == 0).float().mean().item()
                print(f"Epoch {epoch} [{batch_idx+1}/{len(train_loader)}] Loss: {avg_b_loss:.4f}, Top-1 Acc: {acc:.3f}", flush=True)
                
        print(f"Epoch {epoch} finished in {time.time() - t0:.2f}s, Avg Loss: {total_loss/len(train_loader):.4f}", flush=True)

        # Evaluate validation score after each epoch
        item_tower.eval()
        user_tower.eval()
        with torch.no_grad():
            all_item_ids = torch.arange(NUM_CATALOG, device=DEVICE)
            item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)
            user_vecs = user_tower(
                user_pos_items_t,
                user_pos_weights_t,
                user_neg_items_t,
                user_neg_weights_t
            ) # (8456, D + 1)
            scores = torch.matmul(user_vecs, item_vecs.T) # (8456, 30000)
            scores[~mask_tensor] = -1e9
            top100_vals, top100_indices = torch.topk(scores, 100, dim=1)
            top100_indices = top100_indices.cpu().numpy()

        records = []
        for r, uid in enumerate(target_uids):
            ranked_catalog_indices = top100_indices[r]
            ranked_item_ids = idx2item[ranked_catalog_indices]
            for rank, item_id in enumerate(ranked_item_ids, 1):
                records.append((int(uid), int(item_id), rank))

        val_ranking_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])
        validated = validate_ranking(val_ranking_df, target_set)
        ranked = ranked_items(validated)
        score_report = score(ranked, graded)
        current_ndcg = score_report['quality_ndcg@10']
        print(f"--> Epoch {epoch} Validation NDCG@10: {current_ndcg:.6f} (Best so far: {best_ndcg:.6f})", flush=True)
        
        if current_ndcg > best_ndcg:
            best_ndcg = current_ndcg
            val_ranking_path = Path('/app/solution/validation.parquet')
            val_ranking_df.to_parquet(val_ranking_path)
            torch.save(user_tower.state_dict(), '/app/solution/best_user_tower.pt')
            torch.save(item_tower.state_dict(), '/app/solution/best_item_tower.pt')
            print(f"Saved new best model checkpoint with NDCG@10 = {best_ndcg:.6f}", flush=True)

    print(f"\nFinal Best Validation NDCG@10: {best_ndcg:.6f}", flush=True)

Grid search over beta on trained checkpoint

+59:05

Model text

I have updated /app/solution/train_val.py to guard the execution under if __name__ == '__main__': so helper modules can import classes safely.

Next step: Run the grid search script to evaluate $\beta$ scaling on the saved model checkpoint.

Tool callrun_shell_commandGrid search over beta on trained checkpoint Returned

Tool call · Input +59:05

{
  "description": "Grid search over beta on trained checkpoint",
  "command": "PYTHONPATH=/task:/app/solution python3 -u -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nsys.path.append('/app/solution')\n\nfrom evaluate_public import validation_truth, score\nfrom ranking_evaluation import CandidateSet, ranked_items, validate_ranking\n\ndata_dir = Path('/task/data')\ntargets = pd.read_parquet(data_dir / 'target_users.parquet')\ntarget_set = set(targets.uid)\ntarget_uids = targets.uid.astype(int).to_numpy()\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nidx2item = cat.item_id.to_numpy()\neligibility = CandidateSet(data_dir / 'validation_eligible_candidates.npz')\nunpacked_mask = np.unpackbits(eligibility.packed, axis=1)[:, :30000]\nmask_tensor = torch.from_numpy(unpacked_mask.astype(bool))\ngraded = validation_truth(eligibility, target_set)\n\nfrom train_val import ItemTower, UserTower, user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t, cat_features_tensor, NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias\n\nitem_tower = ItemTower(NUM_CATALOG, EMBED_DIM, FEAT_DIM, OUTPUT_DIM, initial_bias)\nitem_tower.load_state_dict(torch.load('/app/solution/best_item_tower.pt'))\nitem_tower.eval()\n\nuser_tower = UserTower(item_tower.item_emb, EMBED_DIM, OUTPUT_DIM)\nuser_tower.load_state_dict(torch.load('/app/solution/best_user_tower.pt'))\nuser_tower.eval()\n\nwith torch.no_grad():\n    all_item_ids = torch.arange(NUM_CATALOG)\n    raw_item_vecs = item_tower(all_item_ids, cat_features_tensor) # (30000, D + 1)\n    raw_user_vecs = user_tower(user_pos_items_t, user_pos_weights_t, user_neg_items_t, user_neg_weights_t) # (8456, D + 1)\n\n    u_pers = raw_user_vecs[:, :-1]\n    u_scale = raw_user_vecs[:, -1:]\n    \n    i_pers = raw_item_vecs[:, :-1]\n    i_bias = raw_item_vecs[:, -1:]\n    \n    pers_scores = torch.matmul(u_pers, i_pers.T)\n    bias_scores = torch.matmul(u_scale, i_bias.T)\n\nfor beta in [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0]:\n    scores = (pers_scores + beta * bias_scores).clone()\n    scores[~mask_tensor] = -1e9\n    top100_vals, top100_indices = torch.topk(scores, 100, dim=1)\n    top100_indices = top100_indices.numpy()\n    \n    records = []\n    for r, uid in enumerate(target_uids):\n        ranked_catalog_indices = top100_indices[r]\n        ranked_item_ids = idx2item[ranked_catalog_indices]\n        for rank, item_id in enumerate(ranked_item_ids, 1):\n            records.append((int(uid), int(item_id), rank))\n            \n    val_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\n    validated = validate_ranking(val_df, target_set)\n    res = score(ranked_items(validated), graded)\n    print(f'Beta {beta:4.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}', flush=True)\n\""
}

Tool response +59:51

<untrusted_context>
Output: Loading metadata...
Target users: 8456, Catalog items: 30000
Loading validation preference state...
Loading recent listens...
Aggregating user histories...
User profiles built for 8456 users.
Extracting training interaction pairs...
Total training pairs: 838948
Beta  0.0: NDCG@10 = 0.001630
Beta  0.5: NDCG@10 = 0.005615
Beta  1.0: NDCG@10 = 0.010204
Beta  1.5: NDCG@10 = 0.006595
Beta  2.0: NDCG@10 = 0.006431
Beta  2.5: NDCG@10 = 0.006227
Beta  3.0: NDCG@10 = 0.006271
Beta  4.0: NDCG@10 = 0.006041
Beta  5.0: NDCG@10 = 0.006029
Process Group PGID: 3305
</untrusted_context>

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About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt: gemini-3-8-flash-high-base-fixed-r01-3d6fca60
Recorded model: gemini-3.8-flash
Harness version: 0.61.0
Session: 745e5fd8-fc73-43bd-b056-fb5c2729e058
Source SHA-256: 9584457e970c622ff4d96477b39bf01001139a9f72af9d3a4b11f8b24a018f0c

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.

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