Agent trace

- [in_progress] 1: Explore production model, data, and evaluation
- [pending] 2: Diagnose negative-sampling limitations
- [pending] 3: Implement improved training objective
- [pending] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission

- /task/
  - current_model/
    - train_current_model.py
    - two_tower_model.py
  - 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
  - evaluate_public.py
  - ranking_evaluation.py
  - requirements.txt

- /app/
  - solution/

1→#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
10→into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

20→import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
30→import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


40→@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
50→    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
60→    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
70→    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray
    packed: np.ndarray
80→
    @classmethod
    def read(cls, path: Path) -> "CandidateSet":
        with np.load(path, allow_pickle=False) as artifact:
            result = cls(
                uids=artifact["uids"].astype(np.int64, copy=True),
                item_ids=artifact["item_ids"].astype(np.int64, copy=True),
                packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
            )
        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
90→        if result.packed.shape != expected:
            raise ValueError(
                f"invalid candidate artifact shape {result.packed.shape}; "
                f"expected {expected}"
            )
        return result


@dataclass(frozen=True)
class SequenceData:
100→    item_ids: np.ndarray
    item_features: np.ndarray
    user_ids: np.ndarray
    offsets: np.ndarray
    sequence_timestamps: np.ndarray
    sequence_items: np.ndarray
    event_features: np.ndarray
    activity_features: np.ndarray
    state_ids: np.ndarray
    liked_states: np.ndarray
110→    disliked_states: np.ndarray
    state_counts: np.ndarray
    inference_user_rows: np.ndarray
    inference_liked_items: np.ndarray
    inference_disliked_items: np.ndarray
    inference_user_features: np.ndarray

    @property
    def lengths(self) -> np.ndarray:
        return np.diff(self.offsets)
120→
    @property
    def target_count(self) -> int:
        return int(np.maximum(self.lengths - 1, 0).sum())


@dataclass(frozen=True)
class TargetPartition:
    """A contiguous recent target window and its chronological BCE split."""

130→    window_starts: np.ndarray
    training_counts: np.ndarray
    validation_counts: np.ndarray
    history_training_counts: np.ndarray

    @property
    def selected_count(self) -> int:
        return int(self.training_counts.sum() + self.validation_counts.sum())


140→def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


150→def model_config(config: Config) -> base.TowerConfig:
    """Map the dense trainer configuration onto the unchanged tower."""
    return base.TowerConfig(
        sequence_length=config.bptt_steps,
        embedding_dim=config.embedding_dim,
        hidden_dim=config.hidden_dim,
        user_hidden_1=config.user_hidden_1,
        user_hidden_2=config.user_hidden_2,
        item_hidden_1=config.item_hidden_1,
        item_hidden_2=config.item_hidden_2,
160→        dropout=config.dropout,
        epochs=config.epochs,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        gradient_clip=config.gradient_clip,
        uniform_negatives=config.uniform_negatives,
        inbatch_negatives=config.inbatch_negatives,
        inbatch_correction_power=config.inbatch_correction_power,
        inbatch_correction_min=config.inbatch_correction_min,
        inbatch_correction_max=config.inbatch_correction_max,
170→        seed=config.seed,
    )


def append_state_snapshot(
    active_likes: dict[int, int],
    active_dislikes: dict[int, int],
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
180→    state_counts: list[tuple[int, int]],
) -> int:
    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
    disliked_states.append(
        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
    )
    state_counts.append((len(active_likes), len(active_dislikes)))
    return len(liked_states) - 1


190→def causal_activity_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    organic: np.ndarray,
    completion: np.ndarray,
) -> np.ndarray:
    """Compute the first four reviewed user features at every event cutoff."""
    length = len(items)
    result = np.zeros((length, 4), dtype=np.float16)
    if not length:
200→        return result
    left_rows = np.searchsorted(
        timestamps,
        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
        side="left",
    )
    rows = np.arange(length, dtype=np.int64)
    counts = rows - left_rows + 1
    organic_sum = np.cumsum(organic.astype(np.float64))
    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
210→    before_organic = np.where(
        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
    )
    before_completion = np.where(
        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
    )

    unique_counts = np.empty(length, dtype=np.int32)
    frequencies: dict[int, int] = {}
    left = 0
220→    for right, raw_item in enumerate(items):
        item = int(raw_item)
        frequencies[item] = frequencies.get(item, 0) + 1
        required_left = int(left_rows[right])
        while left < required_left:
            old_item = int(items[left])
            remaining = frequencies[old_item] - 1
            if remaining:
                frequencies[old_item] = remaining
            else:
230→                del frequencies[old_item]
            left += 1
        unique_counts[right] = len(frequencies)

    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
    result[:, 2] = (
        (organic_sum - before_organic) / np.maximum(counts, 1)
    ).astype(np.float16)
    result[:, 3] = (
240→        (completion_sum - before_completion)
        / np.maximum(counts, 1)
        / 100.0
    ).astype(np.float16)
    return result


def build_sequence_parts(
    listens: base.ListenEvents,
    explicit: base.ExplicitEvents,
250→    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
    items = listens.items[qualifying].astype(np.uint16, copy=False)
    organic = listens.organic[qualifying].astype(np.float32, copy=False)
    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
260→    if not len(items):
        return None

    event_features = np.zeros((len(items), 3), dtype=np.float16)
    event_features[:, 0] = organic.astype(np.float16)
    event_features[:, 1] = (
        np.minimum(completion, 100.0) / 100.0
    ).astype(np.float16)
    if len(items) > 1:
        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
270→        event_features[1:, 2] = np.exp(
            -gaps.astype(np.float64) / (60.0 * base.DAY)
        ).astype(np.float16)

    activity = causal_activity_features(
        timestamps, items, organic, completion
    )
    states = np.zeros(len(items), dtype=np.uint32)
    active_likes: dict[int, int] = {}
    active_dislikes: dict[int, int] = {}
280→    explicit_position = 0
    current_state = 0
    for row, timestamp in enumerate(timestamps):
        changed = False
        while (
            explicit_position < len(explicit.timestamps)
            and int(explicit.timestamps[explicit_position]) < int(timestamp)
        ):
            item = int(explicit.items[explicit_position])
            event_timestamp = int(explicit.timestamps[explicit_position])
290→            destination = (
                active_likes
                if int(explicit.channel[explicit_position]) == 0
                else active_dislikes
            )
            if int(explicit.active[explicit_position]) == 1:
                destination[item] = event_timestamp
            else:
                destination.pop(item, None)
            explicit_position += 1
300→            changed = True
        if changed:
            current_state = append_state_snapshot(
                active_likes,
                active_dislikes,
                n_items,
                liked_states,
                disliked_states,
                state_counts,
            )
310→        states[row] = np.uint32(current_state)
    return timestamps, items.copy(), event_features, activity, states


def final_user_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    event_features: np.ndarray,
    history_end: int,
    like_count: int,
320→    dislike_count: int,
) -> np.ndarray:
    start = int(
        np.searchsorted(
            timestamps,
            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
            side="left",
        )
    )
    result = np.zeros(6, dtype=np.float32)
330→    recent_items = items[start:]
    if len(recent_items):
        result[0] = np.log1p(len(recent_items)) / 8.0
        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
        result[2] = float(np.mean(event_features[start:, 0]))
        result[3] = float(np.mean(event_features[start:, 1]))
    result[4] = np.log1p(like_count) / 6.0
    result[5] = np.log1p(dislike_count) / 4.0
    return result

340→
def prepare_data(
    public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
    data = public / "data"
    catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
        "popularity_rank"
    )
    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
    item_index = pd.Index(item_ids)
350→    n_items = len(item_ids)
    if n_items >= np.iinfo(np.uint16).max:
        raise RuntimeError("uint16 item storage is too small for this catalog")

    if stage == "validation":
        prefixes = ("train",)
        history_end = base.TRAIN_END
        candidate_name = "validation_eligible_candidates.npz"
        state_name = "validation_preference_state.parquet"
    else:
360→        prefixes = ("train", "val")
        history_end = base.VALIDATION_END
        candidate_name = "test_eligible_candidates.npz"
        state_name = "test_preference_state.parquet"
    eligibility = CandidateSet.read(data / candidate_name)
    if not np.array_equal(eligibility.item_ids, item_ids):
        raise RuntimeError("catalog and candidate artifact item order differ")

    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
    inference_likes, inference_dislikes, like_counts, dislike_counts = (
370→        base.load_inference_state(
            data / state_name,
            item_index,
            eligibility.uids,
            n_items,
        )
    )
    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
380→
    # Snapshot zero is the shared empty explicit state.
    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
    disliked_states = [
        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
    ]
    state_counts: list[tuple[int, int]] = [(0, 0)]
    user_ids: list[int] = []
    offsets = [0]
    timestamp_parts: list[np.ndarray] = []
390→    item_parts: list[np.ndarray] = []
    event_parts: list[np.ndarray] = []
    activity_parts: list[np.ndarray] = []
    state_parts: list[np.ndarray] = []

    listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
    started = time.monotonic()
    for seen_users, (uid, listens) in enumerate(
        base.merge_listen_streams(listen_paths, item_index), start=1
    ):
400→        parts = build_sequence_parts(
            listens,
            explicit_by_user.get(uid, base.empty_explicit_events()),
            n_items,
            liked_states,
            disliked_states,
            state_counts,
        )
        if parts is None:
            continue
410→        sequence_row = len(user_ids)
        user_ids.append(uid)
        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
        item_parts.append(parts[1])
        event_parts.append(parts[2])
        activity_parts.append(parts[3])
        state_parts.append(parts[4])
        offsets.append(offsets[-1] + len(parts[1]))
        inference_row = inference_rows.get(uid)
        if inference_row is not None:
420→            inference_sequence_rows[inference_row] = sequence_row
            inference_user_features[inference_row] = final_user_features(
                parts[0],
                parts[1],
                parts[2],
                history_end,
                int(like_counts[inference_row]),
                int(dislike_counts[inference_row]),
            )
        if seen_users % 1_000 == 0:
430→            log(
                f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
                f"states={len(liked_states):,} "
                f"elapsed={time.monotonic() - started:.1f}s"
            )

    missing = np.flatnonzero(inference_sequence_rows < 0)
    if len(missing):
        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
        inference_user_features[missing, 5] = (
440→            np.log1p(dislike_counts[missing]) / 4.0
        )
        log(f"target users without qualifying catalog sequence={len(missing):,}")

    prepared = SequenceData(
        item_ids=item_ids,
        item_features=base.build_item_features(catalog),
        user_ids=np.asarray(user_ids, dtype=np.int64),
        offsets=np.asarray(offsets, dtype=np.int64),
        sequence_timestamps=np.concatenate(timestamp_parts),
450→        sequence_items=np.concatenate(item_parts),
        event_features=np.concatenate(event_parts),
        activity_features=np.concatenate(activity_parts),
        state_ids=np.concatenate(state_parts),
        liked_states=np.stack(liked_states),
        disliked_states=np.stack(disliked_states),
        state_counts=np.asarray(state_counts, dtype=np.int32),
        inference_user_rows=inference_sequence_rows,
        inference_liked_items=inference_likes,
        inference_disliked_items=inference_dislikes,
460→        inference_user_features=inference_user_features,
    )
    log(
        f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
        f"tokens={len(prepared.sequence_items):,} "
        f"next_item_targets={prepared.target_count:,} "
        f"states={len(prepared.liked_states):,}"
    )
    return prepared, eligibility

470→
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
    return {
        "cache_version": CACHE_VERSION,
        "stage": stage,
        "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
        "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
        "event_time_feature": "exp-negative-gap-over-60-days",
    }

480→
def save_cache(
    path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    np.savez(
        path,
        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
        **prepared.__dict__,
    )
490→    log(f"saved public-derived full-sequence cache: {path}")


def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
    with np.load(path, allow_pickle=False) as artifact:
        actual = json.loads(str(artifact["metadata"].item()))
        expected = cache_metadata(stage, config)
        if actual != expected:
            raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
        values = {
500→            field: artifact[field].copy()
            for field in SequenceData.__dataclass_fields__
        }
    prepared = SequenceData(**values)
    log(
        f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
        f"targets={prepared.target_count:,}"
    )
    return prepared

510→
def load_or_prepare(
    public: Path,
    stage: str,
    config: Config,
    cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
    candidate_name = (
        "validation_eligible_candidates.npz"
        if stage == "validation"
520→        else "test_eligible_candidates.npz"
    )
    eligibility = CandidateSet.read(public / "data" / candidate_name)
    if cache is not None and cache.is_file():
        prepared = load_cache(cache, stage, config)
        if not np.array_equal(prepared.item_ids, eligibility.item_ids):
            raise RuntimeError("cached catalog differs from candidate artifact")
        return prepared, eligibility
    prepared, built_eligibility = prepare_data(public, stage, config)
    if cache is not None:
530→        save_cache(cache, prepared, stage, config)
    return prepared, built_eligibility


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
540→    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
550→                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
560→    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
570→    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray
580→) -> np.ndarray:
    """Pack each user's complete public-training positive-item set."""
    width = (len(prepared.item_ids) + 7) // 8
    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
    retained = 0
    for user_row, target_count in enumerate(training_counts):
        if not target_count:
            continue
        offset = int(prepared.offsets[user_row])
        # T adjacent targets consume sequence positions 0 through T.  The
590→        # chronological BCE suffix starts at T + 1 and is intentionally absent.
        items = np.unique(
            prepared.sequence_items[
                offset : offset + int(target_count) + 1
            ].astype(np.int64)
        )
        byte_columns = items >> 3
        bit_values = np.left_shift(
            np.uint8(1), (items & 7).astype(np.uint8)
        )
600→        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
        retained += len(items)
    log(
        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
        f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
    )
    return packed


def invalid_full_history_negatives(
610→    negatives: np.ndarray,
    targets: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray,
    liked_items: np.ndarray,
) -> np.ndarray:
    """Return row-level invalid negatives using the complete training prefix."""
    byte_columns = negatives >> 3
    bit_columns = negatives & 7
    known = known_positive_packed[user_rows[:, None], byte_columns]
620→    invalid = ((known >> bit_columns) & 1).astype(bool)
    invalid |= negatives == targets[:, None]
    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= (
            negatives[:, column, None] == negatives[:, :column]
        ).any(axis=1)
    return invalid


630→def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
640→) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
650→    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
660→                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
670→            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)
680→
    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
690→            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
700→        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
710→        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
720→        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
730→    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
740→            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
750→    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
760→    )


def event_sequence(
    model: base.SequentialTwoTower,
    items: torch.Tensor,
    features: torch.Tensor,
    hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    inputs = torch.cat([model.context_embedding(items), features], dim=-1)
770→    projected = model.event_projection(inputs)
    return model.sequence_encoder(projected, hidden)


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
780→    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
790→    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_rows],
        torch.from_numpy(counts.astype(np.float32)).to(device),
    )


def user_queries(
    model: base.SequentialTwoTower,
800→    sequence_vectors: torch.Tensor,
    liked_vectors: torch.Tensor,
    disliked_vectors: torch.Tensor,
    user_features: torch.Tensor,
) -> torch.Tensor:
    return model.user_tower(
        torch.cat(
            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
            dim=1,
        )
810→    )


def candidate_vectors(
    model: base.SequentialTwoTower,
    positives: torch.Tensor,
    negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    all_items = torch.cat([positives, negatives.reshape(-1)])
    unique_items, inverse = torch.unique(all_items, return_inverse=True)
820→    encoded = model.encode_items(unique_items)
    positive_vectors = encoded[inverse[: len(positives)]]
    negative_vectors = encoded[inverse[len(positives) :]].reshape(
        len(positives), negatives.shape[1], -1
    )
    return positive_vectors, negative_vectors


def history_windows(
    sequence: np.ndarray,
830→    start: int,
    count: int,
    width: int,
    padding_item: int,
) -> np.ndarray:
    base_start = max(0, start - width + 1)
    segment = sequence[base_start : start + count]
    padded = np.concatenate(
        [np.full(width - 1, padding_item, dtype=np.uint16), segment]
    )
840→    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
850→    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
860→        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,
870→    training_counts: np.ndarray,
) -> np.ndarray:
    """Return the discarded prefix before the retained chronological suffix."""
    training_region_ends = total_targets - validation_counts
    window_starts = training_region_ends - training_counts
    if (
        np.any(window_starts < 0)
        or np.any(training_counts < 0)
        or np.any(validation_counts < 0)
    ):
880→        raise RuntimeError("chronological target partition produced negative counts")
    if not np.array_equal(
        window_starts + training_counts + validation_counts, total_targets
    ):
        raise RuntimeError("chronological target window is not a final contiguous suffix")
    return window_starts


def chronological_target_partition(
    prepared: SequenceData,
890→    validation_fraction: float,
    max_train_targets_per_user: int,
    max_validation_targets_per_user: int,
) -> TargetPartition:
    """Select a recent contiguous window with a final out-of-time BCE suffix."""
    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
    validation = np.floor(totals * validation_fraction).astype(np.int64)
    validation[(totals >= 2) & (validation == 0)] = 1
    if max_validation_targets_per_user:
        validation = np.minimum(validation, max_validation_targets_per_user)
900→
    history_training = totals - validation
    training = history_training.copy()
    if max_train_targets_per_user:
        training = np.minimum(training, max_train_targets_per_user)
    window_starts = retained_target_window_starts(totals, validation, training)
    return TargetPartition(
        window_starts=window_starts,
        training_counts=training,
        validation_counts=validation,
910→        history_training_counts=history_training,
    )


def load_organic_like_outcomes(
    public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
    """Load mapped organic-like timestamps from public fitting history."""
    prefixes = ("train",) if stage == "validation" else ("train", "val")
    frames = [
920→        pd.read_parquet(
            public / "data" / f"{prefix}_likes.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic"],
        )
        for prefix in prefixes
    ]
    likes = pd.concat(frames, ignore_index=True)
    likes = likes[likes.is_organic == 1].copy()
    mapped = pd.Index(item_ids).get_indexer(
        likes.item_id.to_numpy(dtype=np.int64)
930→    )
    likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
    likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
    likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
    nested: dict[int, dict[int, list[int]]] = {}
    for raw_uid, raw_timestamp, raw_item in likes.itertuples(
        index=False, name=None
    ):
        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
            int(raw_timestamp)
940→        )
    result = {
        uid: {
            item: np.asarray(timestamps, dtype=np.int64)
            for item, timestamps in items.items()
        }
        for uid, items in nested.items()
    }
    log(
        f"loaded organic-like outcomes: rows={len(likes):,} "
950→        f"users={len(result):,}"
    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
960→    config: Config,
) -> np.ndarray:
    """Build nonzero per-token weights without dropping adjacent targets."""
    if config.target_weighting == "uniform":
        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
    else:
        weights = np.full(
            len(prepared.sequence_items),
            config.ordinary_target_weight,
            dtype=np.float32,
970→        )
        organic_strong = (
            (prepared.event_features[:, 0] > 0.5)
            & (prepared.event_features[:, 1] >= 0.8)
        )
        weights[organic_strong] = config.organic_strong_target_weight
        if config.target_weighting == "discovery-graded":
            like_outcomes = load_organic_like_outcomes(
                public, stage, prepared.item_ids
            )
980→            history_end = (
                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
            )
            horizon = config.discovery_horizon_days * base.DAY
            grade_three = 0
            for user_row, uid in enumerate(prepared.user_ids):
                start = int(prepared.offsets[user_row])
                end = int(prepared.offsets[user_row + 1])
                if end - start < 2:
                    continue
990→                last_training_target = start + int(training_counts[user_row])
                first_validation_target = last_training_target + 1
                split_timestamp = (
                    int(prepared.sequence_timestamps[first_validation_target])
                    if first_validation_target < end
                    else history_end
                )
                user_likes = like_outcomes.get(int(uid), {})
                seen = {int(prepared.sequence_items[start])}
                for position in range(start + 1, end):
1000→                    item = int(prepared.sequence_items[position])

1→"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations

import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator

import numpy as np
10→import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn


DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
20→PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100


@dataclass(frozen=True)
class TowerConfig:
    sequence_length: int = 100
    embedding_dim: int = 192
30→    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
40→    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    seed: int = 29


@dataclass(frozen=True)
class ListenEvents:
50→    timestamps: np.ndarray
    items: np.ndarray
    organic: np.ndarray
    completion_pct: np.ndarray


@dataclass(frozen=True)
class ExplicitEvents:
    timestamps: np.ndarray
    items: np.ndarray
60→    channel: np.ndarray
    active: np.ndarray
    organic_like: np.ndarray


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
70→    """Return four standardized features built only from the public catalog."""
    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
    features = np.column_stack(
        [
            np.log1p(positives),
            np.log1p(organic),
            organic / np.maximum(positives, 1.0),
            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
80→        ]
    )
    means = features.mean(axis=0, keepdims=True)
    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
    standardized = ((features - means) / scales).astype(np.float32)
    if not np.isfinite(standardized).all():
        raise RuntimeError("catalog features contain non-finite values")
    return standardized


90→def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
    if len(parts) == 1:
        return parts[0]
    timestamps = np.concatenate([part.timestamps for part in parts])
    items = np.concatenate([part.items for part in parts])
    organic = np.concatenate([part.organic for part in parts])
    completion = np.concatenate([part.completion_pct for part in parts])
    order = np.argsort(timestamps, kind="stable")
    return ListenEvents(
        timestamps=timestamps[order],
100→        items=items[order],
        organic=organic[order],
        completion_pct=completion[order],
    )


def iter_listen_users(
    path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Stream one uid-sorted public listen file one user at a time."""
110→    parquet = pq.ParquetFile(path)
    pending_uid: int | None = None
    pending: list[ListenEvents] = []
    previous_uid = -1
    for batch in parquet.iter_batches(
        batch_size=750_000,
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
    ):
        frame = batch.to_pandas()
        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
120→        keep = mapped >= 0
        if not np.any(keep):
            continue
        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
        items = mapped[keep].astype(np.int32, copy=False)
        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
        if np.any(uids[1:] < uids[:-1]):
            raise RuntimeError(f"{path.name} is not sorted by uid")
130→        boundaries = np.r_[
            0,
            np.flatnonzero(uids[1:] != uids[:-1]) + 1,
            len(uids),
        ]
        for start, end in zip(boundaries[:-1], boundaries[1:]):
            uid = int(uids[start])
            part = ListenEvents(
                timestamps[start:end].copy(),
                items[start:end].copy(),
140→                organic[start:end].copy(),
                completion[start:end].copy(),
            )
            if pending_uid is None:
                pending_uid = uid
            elif uid != pending_uid:
                if pending_uid < previous_uid:
                    raise RuntimeError(f"{path.name} user order regressed")
                yield pending_uid, _combine_listen_chunks(pending)
                previous_uid = pending_uid
150→                pending_uid = uid
                pending = []
            pending.append(part)
    if pending_uid is not None:
        yield pending_uid, _combine_listen_chunks(pending)


def merge_listen_streams(
    paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
160→    """Merge uid-sorted public listen files without materializing them."""
    streams = [iter(iter_listen_users(path, item_index)) for path in paths]
    heads: list[tuple[int, ListenEvents] | None] = [
        next(stream, None) for stream in streams
    ]
    while any(head is not None for head in heads):
        uid = min(head[0] for head in heads if head is not None)
        parts: list[ListenEvents] = []
        for position, head in enumerate(heads):
            if head is not None and head[0] == uid:
170→                parts.append(head[1])
                heads[position] = next(streams[position], None)
        yield uid, _combine_listen_chunks(parts)


def load_explicit_events(
    data: Path,
    prefixes: tuple[str, ...],
    item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
180→    """Load public explicit actions as chronological state changes."""
    specifications = (
        ("likes", 0, 1, 0),
        ("unlikes", 0, 0, 1),
        ("dislikes", 1, 1, 0),
        ("undislikes", 1, 0, 1),
    )
    parts: list[pd.DataFrame] = []
    for family, channel, active, tie_priority in specifications:
        for prefix in prefixes:
190→            frame = pd.read_parquet(
                data / f"{prefix}_{family}.parquet",
                columns=["uid", "timestamp", "item_id", "is_organic"],
            )
            mapped = item_index.get_indexer(
                frame.item_id.to_numpy(dtype=np.int64, copy=False)
            )
            keep = mapped >= 0
            if not np.any(keep):
                continue
200→            retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
            retained["item"] = mapped[keep].astype(np.int32, copy=False)
            retained["channel"] = np.int8(channel)
            retained["active"] = np.int8(active)
            retained["tie_priority"] = np.int8(tie_priority)
            retained["organic_like"] = np.int8(
                family == "likes"
            ) * retained.is_organic.astype(np.int8)
            parts.append(retained.drop(columns="is_organic"))
    if not parts:
210→        return {}
    events = pd.concat(parts, ignore_index=True)
    events = events.sort_values(
        ["uid", "timestamp", "tie_priority"], kind="mergesort"
    ).reset_index(drop=True)
    result: dict[int, ExplicitEvents] = {}
    for uid, rows in events.groupby("uid", sort=False):
        result[int(uid)] = ExplicitEvents(
            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
            items=rows.item.to_numpy(dtype=np.int32, copy=True),
220→            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
            active=rows.active.to_numpy(dtype=np.int8, copy=True),
            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
        )
    log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
    return result


def empty_explicit_events() -> ExplicitEvents:
    return ExplicitEvents(
230→        timestamps=np.empty(0, dtype=np.int64),
        items=np.empty(0, dtype=np.int32),
        channel=np.empty(0, dtype=np.int8),
        active=np.empty(0, dtype=np.int8),
        organic_like=np.empty(0, dtype=np.int8),
    )


def pack_active_items(
    active: dict[int, int], width: int, padding_item: int
240→) -> np.ndarray:
    packed = np.full(width, padding_item, dtype=np.uint16)
    if not active:
        return packed
    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
    packed[: len(ordered)] = np.fromiter(
        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
    )
    return packed

250→
def load_inference_state(
    path: Path,
    item_index: pd.Index,
    uids: np.ndarray,
    n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Pack the task-provided active explicit state."""
    state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
260→    keep = mapped >= 0
    state = state.loc[keep, ["uid", "state"]].copy()
    state["item"] = mapped[keep].astype(np.int32, copy=False)
    user_rows = pd.Index(uids)
    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
    like_counts = np.zeros(len(uids), dtype=np.int32)
    dislike_counts = np.zeros(len(uids), dtype=np.int32)
    for value, width, destination, counts in (
        ("liked", LIKE_ITEMS, liked, like_counts),
270→        ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
    ):
        selected = state[state.state == value]
        for uid, rows in selected.groupby("uid", sort=False):
            row = int(user_rows.get_indexer([int(uid)])[0])
            if row < 0:
                continue
            items = np.sort(rows.item.unique())
            counts[row] = len(items)
            retained = items[:width]
280→            destination[row, : len(retained)] = retained.astype(np.uint16)
    return liked, disliked, like_counts, dislike_counts


class SequentialTwoTower(nn.Module):
    """Order-sensitive user tower and independent catalog-aware item tower."""

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
290→        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
300→            input_size=config.hidden_dim,
            hidden_size=dim,
            num_layers=1,
            batch_first=True,
        )
        self.user_tower = nn.Sequential(
            nn.Linear(dim * 3 + 6, config.user_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_1, config.user_hidden_2),
310→            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_2, dim),
        )
        self.candidate_embedding = nn.Embedding(n_items, dim)
        self.register_buffer(
            "item_features", torch.from_numpy(item_features), persistent=True
        )
        self.item_tower = nn.Sequential(
            nn.Linear(dim + n_item_features, config.item_hidden_1),
320→            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_1, config.item_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_2, dim),
        )
        nn.init.normal_(self.context_embedding.weight, std=0.02)
        nn.init.normal_(self.candidate_embedding.weight, std=0.02)
        with torch.no_grad():
330→            self.context_embedding.weight[n_items].zero_()

    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
        mask = items.ne(self.n_items)
        vectors = self.context_embedding(items)
        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
            dim=1, keepdim=True
        ).clamp_min(1)

    def encode_items(self, items: torch.Tensor) -> torch.Tensor:
340→        inputs = torch.cat(
            [self.candidate_embedding(items), self.item_features[items]], dim=1
        )
        return self.item_tower(inputs)


def invalid_negative_mask(
    negatives: torch.Tensor,
    targets: torch.Tensor,
    context_items: torch.Tensor,
350→    liked_items: torch.Tensor,
) -> torch.Tensor:
    """Identify targets, known positives, active likes, and row duplicates."""
    invalid = negatives.eq(targets[:, None])
    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= negatives[:, column, None].eq(
            negatives[:, :column]
        ).any(dim=1)
360→    return invalid


def draw_filtered_mixed_negatives(
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
    n_items: int,
    uniform_count: int,
    inbatch_count: int,
370→    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw uniform and unique-target in-batch negatives."""
    batch_size = len(targets)
    uniform = torch.randint(
        n_items,
        (batch_size, uniform_count),
        generator=generator,
380→        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
    corrected_count = 0
    if inbatch_count:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
390→            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
                -1.0,
            )
            columns = torch.topk(priorities, k=corrected_count, dim=1).indices
            inbatch = unique_targets[columns]
        if corrected_count < inbatch_count:
            filler = torch.randint(
                n_items,
400→                (batch_size, inbatch_count - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
    if corrected_count:
        correction_cpu[
410→            :, uniform_count : uniform_count + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]]
    negatives = negatives_cpu.to(device)
    correction = correction_cpu.to(device)
    for _ in range(32):
        invalid = invalid_negative_mask(
            negatives, targets, context_items, liked_items
        )
        if not bool(invalid.any()):
            return negatives, correction
420→        replacements = torch.randint(
            n_items,
            (int(invalid.sum().detach().cpu()),),
            generator=generator,
            device="cpu",
        ).to(device)
        negatives[invalid] = replacements
        correction[invalid] = 1.0
    invalid = invalid_negative_mask(
        negatives, targets, context_items, liked_items
430→    )
    if bool(invalid.any()):
        raise RuntimeError("mixed negative resampling failed false-negative filter")
    return negatives, correction

1→#!/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
10→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.

20→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

30→import json
import sys
from pathlib import Path

import pandas as pd

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


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

50→
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.
60→
    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")
70→    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()

80→    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
90→

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
100→    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.

110→    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"
120→        )
    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.

130→    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)


140→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>"
150→        )
    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()

1→"""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
10→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

20→
__all__ = [
    "CandidateSet",
    "QUALITY_K",
    "RankingValidationError",
    "REQUIRED_COLUMNS",
    "TOP_N",
    "ndcg_at_k",
    "ranked_items",
    "read_ranking",
30→    "score_rankings",
    "validate_ranking",
]

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


class RankingValidationError(ValueError):
40→    """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.
50→    """

    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)
60→            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``.

70→        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.
        """
80→        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.
90→
    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)

100→
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
110→    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.

120→    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")
130→        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)
140→
    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(
150→            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
    )
160→    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.

170→    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
180→    }


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:
190→        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))
200→    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]],
210→) -> 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.
    """
220→    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),
230→        "quality_users": len(graded),
    }

1→# 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
10→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
20→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
30→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
40→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
50→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
60→urllib3==2.7.0
webencodings==0.6.1

- /app/solution/

1000→                    item = int(prepared.sequence_items[position])
                    novel = item not in seen
                    seen.add(item)
                    if not novel:
                        # Repeats remain training examples, but they cannot be
                        # eligible discovery outcomes at the corresponding
                        # cutoff and therefore keep only the ordinary weight.
                        weights[position] = config.ordinary_target_weight
                        continue
                    timestamps = user_likes.get(item)
1010→                    if timestamps is None:
                        continue
                    timestamp = int(prepared.sequence_timestamps[position])
                    upper = min(
                        timestamp + horizon,
                        split_timestamp
                        if position <= last_training_target
                        else history_end,
                    )
                    outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
1020→                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
                        weights[position] = config.organic_like_target_weight
                        grade_three += 1
            log(
                f"grade-3 first-encounter targets={grade_three:,} "
                f"horizon_days={config.discovery_horizon_days}"
            )
    log(
        f"target weighting={config.target_weighting} "
        f"min={weights.min():.3f} mean={weights.mean():.3f} "
1030→        f"max={weights.max():.3f}"
    )
    return weights


def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
    window_starts: np.ndarray,
1040→    process_counts: np.ndarray,
    score_starts: np.ndarray,
    start: int,
    steps: int,
    config: Config,
) -> tuple[np.ndarray, ...]:
    """Build one causal sequence block and mark the target rows to score."""
    batch_size = len(users)
    padding_item = len(prepared.item_ids)
    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
1050→    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
    targets = np.zeros((batch_size, steps), dtype=np.uint16)
    weights = np.ones((batch_size, steps), dtype=np.float32)
    scored = np.zeros((batch_size, steps), dtype=bool)
    histories = np.full(
        (batch_size, steps, config.bptt_steps),
        padding_item,
        dtype=np.uint16,
1060→    )
    processed = np.zeros(batch_size, dtype=np.int32)

    lengths = prepared.lengths
    for row, user_row in enumerate(users):
        count = min(steps, max(int(process_counts[row]) - start, 0))
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        window_start = int(window_starts[row])
1070→        absolute_start = window_start + start
        positions = slice(
            offset + absolute_start,
            offset + absolute_start + count,
        )
        target_positions = slice(
            offset + absolute_start + 1,
            offset + absolute_start + count + 1,
        )
        input_items[row, :count] = prepared.sequence_items[positions]
1080→        event_features[row, :count] = prepared.event_features[positions]
        activity_features[row, :count] = prepared.activity_features[positions]
        state_ids[row, :count] = prepared.state_ids[positions]
        targets[row, :count] = prepared.sequence_items[target_positions]
        weights[row, :count] = target_weights[target_positions]
        score_from = min(count, max(int(score_starts[row]) - start, 0))
        scored[row, score_from:count] = True
        processed[row] = count
        user_sequence = prepared.sequence_items[
            offset : offset + int(lengths[user_row])
1090→        ]
        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
            padding_item,
        )
    return (
        input_items,
1100→        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
        processed,
        weights,
    )

1110→
def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    negatives: torch.Tensor,
1120→    negative_weights: torch.Tensor,
    sample_weights: np.ndarray,
    device: torch.device,
    backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compute one logical block's exact BCE in bounded-memory slices."""
    target_count = len(positive_items)
    total_weight = float(sample_weights.sum())
    if total_weight <= 0:
        raise RuntimeError("target block has no positive sample weight")
1130→    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
    statistics = {
        "loss_sum": 0.0,
        "positive_sum": 0.0,
        "negative_sum": 0.0,
        "pair_correct": 0.0,
        "weight_sum": 0.0,
    }
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
1140→        selected_states = flat_states[begin:end]
        if backward:
            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
1150→        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
1160→            torch.from_numpy(numeric).to(device),
        )
        positive_vectors, negative_vectors = candidate_vectors(
            model,
            positive_items[begin:end],
            negatives[begin:end],
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
1170→        )
        positive_loss = F.softplus(-positive_logits)
        negative_loss = corrected_negative_loss(
            negative_logits, negative_weights[begin:end], config
        )
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        loss_sum = (
            selected_weights * (positive_loss + negative_loss) / 2.0
1180→        ).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
1190→        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
        statistics["pair_correct"] += float(
            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
        )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
1200→        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
1210→    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
1220→        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
1230→    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
1240→        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
1250→                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
1260→                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
1270→            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
1280→                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
1290→            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
1300→                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
1310→                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=True,
            )
1320→            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()

            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
1330→            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
1340→                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
1350→        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
1360→    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
1370→    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
1380→    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
1390→    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
1400→        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
1410→                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
1420→                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
1430→            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
1440→                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
1450→                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
1460→            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
1470→                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
1480→    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
1490→        name: value.detach().cpu().clone()
        for name, value in model.state_dict().items()
    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
1500→    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
1510→            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path

1520→
def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
1530→    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
1540→        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
1550→    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
1560→        stage,
        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
1570→    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
1580→        )
        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
1590→        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
1600→        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
1610→            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
1620→            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
1630→            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
1640→            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
1650→                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
1660→                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
1670→                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
1680→            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

1690→    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
1700→            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
1710→            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
1720→        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
1730→        improved = (
            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
1740→                torch.save(
                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,
                        "model_state": current_state,
                        "optimizer_state": optimizer.state_dict(),
                        "generator_state": generator.get_state(),
                    },
                    checkpoint_dir / "best.pt",
1750→                )
        else:
            epochs_without_improvement += 1
        log(
            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
            f"validation_loss={diagnostics['validation_loss']:.5f} "
            f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
            f"train_targets={int(diagnostics['targets']):,} "
            f"validation_targets={int(diagnostics['validation_targets']):,} "
            f"train_seconds={diagnostics['seconds']:.1f} "
1760→            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
        )
        release_accelerator_cache(device)
        if epochs_without_improvement >= config.early_stopping_patience:
            log(
                f"early stopping after epoch={epoch}; "
                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
            )
            break

1770→    if best_state is None:
        raise RuntimeError("training did not produce a valid checkpoint")
    model.load_state_dict(best_state)
    model.to(device).eval()
    log(
        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
        f"loss={best_loss:.6f}"
    )
    return model, history

1780→
@torch.inference_mode()
def inference_queries(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
1790→        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
    )
    ordered = np.argsort(lengths, kind="stable")
    result = torch.zeros(
        len(sequence_rows), config.embedding_dim, dtype=torch.float32
    )
    for group_start in range(0, len(ordered), config.user_batch_size):
        output_rows = ordered[group_start : group_start + config.user_batch_size]
        rows = sequence_rows[output_rows]
        group_lengths = lengths[output_rows]

1780→
@torch.inference_mode()
def inference_queries(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
1790→        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
    )
    ordered = np.argsort(lengths, kind="stable")
    result = torch.zeros(
        len(sequence_rows), config.embedding_dim, dtype=torch.float32
    )
    for group_start in range(0, len(ordered), config.user_batch_size):
        output_rows = ordered[group_start : group_start + config.user_batch_size]
        rows = sequence_rows[output_rows]
        group_lengths = lengths[output_rows]
1800→        hidden = torch.zeros(
            1, len(output_rows), config.embedding_dim, device=device
        )
        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
            steps = min(
                config.bptt_steps,
                int(group_lengths.max(initial=0)) - start,
            )
            items = np.full(
                (len(output_rows), steps),
1810→                len(prepared.item_ids),
                dtype=np.uint16,
            )
            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
            counts = np.zeros(len(output_rows), dtype=np.int32)
            for local_row, sequence_row in enumerate(rows):
                if sequence_row < 0:
                    continue
                count = min(steps, max(int(group_lengths[local_row]) - start, 0))
                if not count:
1820→                    continue
                offset = int(prepared.offsets[sequence_row])
                selected = slice(offset + start, offset + start + count)
                items[local_row, :count] = prepared.sequence_items[selected]
                features[local_row, :count] = prepared.event_features[selected]
                counts[local_row] = count
            outputs, _ = event_sequence(
                model,
                torch.from_numpy(items.astype(np.int64)).to(device),
                torch.from_numpy(features.astype(np.float32)).to(device),
1830→                hidden,
            )
            next_rows: list[torch.Tensor] = []
            for local_row, count in enumerate(counts):
                next_rows.append(
                    outputs[local_row, count - 1]
                    if count
                    else hidden[0, local_row]
                )
            hidden = torch.stack(next_rows).unsqueeze(0)
1840→
        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
1850→            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


1860→@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
    model.eval()
1870→    queries = inference_queries(model, prepared, config, device)
    item_parts: list[torch.Tensor] = []
    for start in range(0, len(prepared.item_ids), 2_048):
        rows = torch.arange(
            start,
            min(start + 2_048, len(prepared.item_ids)),
            dtype=torch.long,
            device=device,
        )
        item_parts.append(model.encode_items(rows).cpu())
1880→    items = torch.cat(item_parts)
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        scores = queries[start:end] @ items.T
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
1890→        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
1900→            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
1910→

def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")
1920→

def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
1930→    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
1940→        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
1950→        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
1960→        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
1970→        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
1980→        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
1990→    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
2000→    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
2010→        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
2020→        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
    config = Config(
        bptt_steps=args.bptt_steps,
2030→        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
2040→        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
2050→    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
2060→    )
    target_partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
        print(
            json.dumps(
2070→                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
2080→                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
2090→        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
2100→        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,
        args.ranking_batch_size,
    )
2110→    write_ranking(ranking, args.output.resolve())
    print(
        json.dumps(
            {
                "baseline": "yambda-windowed-sequence-two-tower-base",
                "stage": args.stage,
                "output": str(args.output.resolve()),
                "rows": len(ranking),
                "users": int(ranking.uid.nunique()),
                "runtime_seconds": round(time.monotonic() - started, 3),
2120→                "public_inputs_only": True,
                "architecture": {
                    "family": "pure-windowed-sequence-two-tower",
                    "sequence_training": (
                        "every-adjacent-target-within-recent-chronological-window"
                    ),
                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
                    "bptt_hidden_state": "carried-and-detached",
                    "model_selection": "chronological-validation-BCE-early-stopping",
                    "loss_execution": "gradient-accumulated-microbatches",
2130→                    "user_id_embedding": False,
                    "user_mlp": "582-512-256-192",
                    "item_mlp": "196-512-256-192",
                    "normalization": "none",
                    "score": "raw-dot-product",
                    "objective": "sample-weighted-BCE-at-every-timestep",
                    "negative_sampling": (
                        "24-uniform-plus-8-propensity-corrected-inbatch"
                    ),
                    "false_negative_filter": config.negative_filter_scope,
2140→                    "target_weighting": config.target_weighting,
                },
                "config": asdict(config),
                "sequence_tokens": len(prepared.sequence_items),
                "all_next_item_targets": prepared.target_count,
                "selected_training_targets": int(
                    target_partition.training_counts.sum()
                ),
                "selected_validation_targets": int(
                    target_partition.validation_counts.sum()
2150→                ),
                "discarded_old_targets": int(target_partition.window_starts.sum()),
                "training_history": training_history,
                "embedding_diagnostics": embedding_diagnostics,
                "torch_version": torch.__version__,
                "device": str(device),
            },
            indent=2,
        )
    )
2160→    return 0


if __name__ == "__main__":
    raise SystemExit(main())

exit: 1
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pandas'

exit: 0
/usr/local/bin/python
/usr/local/bin/python3
ls: cannot access '/usr/bin/python*': No such file or directory
---
# 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
task-venv
bin
etc
games
include
lib
man
sbin
share
src
PATH=/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games:/home/agent/.grok/bin:/opt/task-venv/bin:/usr/local/sbin:/usr/sbin:/sbin
python3 is /usr/local/bin/python3
/usr/local/bin/python3 ['', '/usr/local/lib/python312.zip', '/usr/local/lib/python3.12', '/usr/local/lib/python3.12/lib-dynload', '/usr/local/lib/python3.12/site-packages']

exit: 0
candidate_catalog.parquet 30000 item_id: uint32
positive_listens: int64
organic_positive_listens: uint64
popularity_rank: int32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 591
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
---
target_users.parquet 8456 uid: uint32
recent_positive_listens: double
history_positive_listens: int64
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 472
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
---
validation_preference_state.parquet 469124 uid: uint32
item_id: uint32
state: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 403
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
---
test_preference_state.parquet 484776 uid: uint32
item_id: uint32
state: string
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 403
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
---
train_listens.parquet rows 43484159 row_groups 42
train_likes.parquet rows 814831 row_groups 1
train_dislikes.parquet rows 101075 row_groups 1
train_unlikes.parquet rows 292665 row_groups 1
train_undislikes.parquet rows 19960 row_groups 1
val_listens.parquet rows 1501697 row_groups 2
val_likes.parquet rows 37795 row_groups 1
val_dislikes.parquet rows 3563 row_groups 1
catalog cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
            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]
n_items 30000
target users 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
val el {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
test el {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
val unique uids 8456
test unique uids 8456
item_ids equal True
user 100 eligible 29318
user 200 eligible 29832
user 300 eligible 29931
user 600 eligible 29320
user 700 eligible 28347
avg eligible sample 29052.66 min 25023 max 29997
catalog popularity rank range 1 30000
   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

exit: 0
8
               total        used        free      shared  buff/cache   available
Mem:            15Gi       986Mi       2.1Gi       3.0Mi        12Gi        14Gi
Swap:             0B          0B          0B
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   14G   83G  14% /
overlay          96G   14G   83G  14% /
total 104
dr-xr-xr-x 1 root root  4096 Sep 29 15:50 .
dr-xr-xr-x 1 root root  4096 Sep 29 15:52 ..
-r--r--r-- 1 root root 80058 Sep 29 15:50 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 15:50 two_tower_model.py
2.8.0+cpu 4

1→#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
10→into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

20→import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
30→import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


40→@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
50→    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
60→    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
70→    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)

exit: 0
total 108
drwxr-xr-x 1 agent agent  4096 Sep 29 16:01 .
drwxr-xr-x 1 agent agent  4096 Sep 29 15:52 ..
-r--r--r-- 1 agent agent 80058 Sep 29 16:01 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 16:01 two_tower_model.py

<task-id>call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17.log</output-file>
<status>running</status>
<summary>Command "Prepare and cache validation-stage sequences" is still running after 30s (block_until_ms) and has been moved to the background as task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17.</summary>
Use get_command_or_subagent_output with task_ids=["call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18" (running for 30s): Analyze relevance vs popularity and negative hardness
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

<task-id>call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18.log</output-file>
<status>running</status>
<summary>Command "Analyze relevance vs popularity and negative hardness" is still running after 30s (block_until_ms) and has been moved to the background as task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18.</summary>
Use get_command_or_subagent_output with task_ids=["call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17" (running for 30s): Prepare and cache validation-stage sequences
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

exit: 0
total 108
drwxr-xr-x 1 agent agent  4096 Sep 29 16:01 .
drwxr-xr-x 1 agent agent  4096 Sep 29 15:52 ..
-rw-r--r-- 1 agent agent 80058 Sep 29 16:01 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 29 16:01 two_tower_model.py

=== Task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18 ===
Command: /opt/task-venv/bin/python - <<'PY'
import numpy as np, pandas as pd
from pathlib import Path

data = Path('/task/data')
cat = pd.read_parquet(data/'candidate_catalog.parquet').sort_values('popularity_rank')
item_ids = cat.item_id.to_numpy()
rank = {int(i): int(r) for i, r in zip(cat.item_id, cat.popularity_rank)}
pop = {int(i): int(p) for i, p in zip(cat.item_id, cat.positive_listens)}

targets = set(pd.read_parquet(data/'target_users.parquet').uid.astype(int))
el = np.load(data/'validation_eligible_candidates.npz')
uid_row = {int(u): i for i, u in enumerate(el['uids'])}
item_col = {int(it): i for i, it in enumerate(el['item_ids'])}
item_n = len(el['item_ids'])

def eligible(uid, item):
    r, c = uid_row.get(int(uid)), item_col.get(int(item))
    if r is None or c is None:
        return False
    byte, bit = divmod(c, 8)
    return bool((int(el['packed_eligible'][r, byte]) >> bit) & 1)

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>=80)][['uid','item_id']].drop_duplicates()
olikes = likes[likes.uid.isin(targets) & (likes.is_organic==1)][['uid','item_id']].drop_duplicates()

graded = {}
for uid, item in strong.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 1
for uid, item in olikes.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 3

print('scored users', len(graded), 'relevant pairs', sum(len(v) for v in graded.values()))
grades = [g for u in graded.values() for g in u.values()]
print('grade3', grades.count(3), 'grade1', grades.count(1))
rels = [(uid, item, g, rank.get(item, -1), pop.get(item, -1)) for uid, items in graded.items() for item, g in items.items()]
rdf = pd.DataFrame(rels, columns=['uid','item','grade','pop_rank','pos_listens'])
print(rdf.groupby('grade')[['pop_rank','pos_listens']].describe())
print('median rank all relevant', rdf.pop_rank.median(), 'mean', rdf.pop_rank.mean())
# how often would uniform sample hit a relevant item?
# per user relevant count
counts = [len(v) for v in graded.values()]
print('relevant per scored user mean/median/max', np.mean(counts), np.median(counts), max(counts))
print('P(uniform item is relevant | user) ~', np.mean(counts)/30000)
# popularity mass of relevant items
total_pos = cat.positive_listens.sum()
print('relevant items popularity share if sampled by freq', rdf.pos_listens.sum()/total_pos/len(rdf)*np.mean(counts))
# catalog popularity concentration
s = cat.positive_listens.sort_values(ascending=False).to_numpy()
cs = np.cumsum(s)/s.sum()
for p in [0.01,0.05,0.1,0.2,0.5]:
    k = int(np.searchsorted(cs, p)+1)
    print(f'top {k} items ({k/30000:.3%}) cover {p:.0%} of positive listens')

# train dislike overlap with catalog
dis = pd.read_parquet(data/'train_dislikes.parquet')
print('train dislikes catalog overlap', dis.item_id.isin(cat.item_id).mean(), 'rows', len(dis))
print('unique users with dislikes', dis.uid.nunique())
print('target users with dislikes', dis.uid.isin(targets).sum(), 'of rows; users', dis[dis.uid.isin(targets)].uid.nunique())
PY
Status: running
Duration: 93.42s
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18.log

=== Output ===


Waited the requested 60s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Multi-wait (wait_all) ===
--- Task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-17 [completed] ---
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --prepare-only --threads 8
Duration: 55.51s
Exit Code: 0
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=5.0s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=10.1s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.3s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.8s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.6s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.4s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=36.0s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.3s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.1s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_sequence_cache.npz
{
  "stage": "validation",
  "sequence_users": 9135,
  "sequence_tokens": 20056329,
  "next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "target_users": 8456,
  "runtime_seconds": 51.358,
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  }
}

--- Task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18 [running] ---
Command: /opt/task-venv/bin/python - <<'PY'
import numpy as np, pandas as pd
from pathlib import Path

data = Path('/task/data')
cat = pd.read_parquet(data/'candidate_catalog.parquet').sort_values('popularity_rank')
item_ids = cat.item_id.to_numpy()
rank = {int(i): int(r) for i, r in zip(cat.item_id, cat.popularity_rank)}
pop = {int(i): int(p) for i, p in zip(cat.item_id, cat.positive_listens)}

targets = set(pd.read_parquet(data/'target_users.parquet').uid.astype(int))
el = np.load(data/'validation_eligible_candidates.npz')
uid_row = {int(u): i for i, u in enumerate(el['uids'])}
item_col = {int(it): i for i, it in enumerate(el['item_ids'])}
item_n = len(el['item_ids'])

def eligible(uid, item):
    r, c = uid_row.get(int(uid)), item_col.get(int(item))
    if r is None or c is None:
        return False
    byte, bit = divmod(c, 8)
    return bool((int(el['packed_eligible'][r, byte]) >> bit) & 1)

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>=80)][['uid','item_id']].drop_duplicates()
olikes = likes[likes.uid.isin(targets) & (likes.is_organic==1)][['uid','item_id']].drop_duplicates()

graded = {}
for uid, item in strong.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 1
for uid, item in olikes.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 3

print('scored users', len(graded), 'relevant pairs', sum(len(v) for v in graded.values()))
grades = [g for u in graded.values() for g in u.values()]
print('grade3', grades.count(3), 'grade1', grades.count(1))
rels = [(uid, item, g, rank.get(item, -1), pop.get(item, -1)) for uid, items in graded.items() for item, g in items.items()]
rdf = pd.DataFrame(rels, columns=['uid','item','grade','pop_rank','pos_listens'])
print(rdf.groupby('grade')[['pop_rank','pos_listens']].describe())
print('median rank all relevant', rdf.pop_rank.median(), 'mean', rdf.pop_rank.mean())
# how often would uniform sample hit a relevant item?
# per user relevant count
counts = [len(v) for v in graded.values()]
print('relevant per scored user mean/median/max', np.mean(counts), np.median(counts), max(counts))
print('P(uniform item is relevant | user) ~', np.mean(counts)/30000)
# popularity mass of relevant items
total_pos = cat.positive_listens.sum()
print('relevant items popularity share if sampled by freq', rdf.pos_listens.sum()/total_pos/len(rdf)*np.mean(counts))
# catalog popularity concentration
s = cat.positive_listens.sort_values(ascending=False).to_numpy()
cs = np.cumsum(s)/s.sum()
for p in [0.01,0.05,0.1,0.2,0.5]:
    k = int(np.searchsorted(cs, p)+1)
    print(f'top {k} items ({k/30000:.3%}) cover {p:.0%} of positive listens')

# train dislike overlap with catalog
dis = pd.read_parquet(data/'train_dislikes.parquet')
print('train dislikes catalog overlap', dis.item_id.isin(cat.item_id).mean(), 'rows', len(dis))
print('unique users with dislikes', dis.uid.nunique())
print('target users with dislikes', dis.uid.isin(targets).sum(), 'of rows; users', dis[dis.uid.isin(targets)].uid.nunique())
PY
Duration: 215.72s


Waited the requested 120s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

1/2 tasks completed (wait_all)

exit: 0
total 511M
-rw-r--r-- 1 agent agent  79K Sep 29 16:01 train_current_model.py
-rw-r--r-- 1 agent agent  16K Sep 29 16:01 two_tower_model.py
-rw-r--r-- 1 agent agent 511M Sep 29 16:02 validation_sequence_cache.npz

=== Task call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18 ===
Command: /opt/task-venv/bin/python - <<'PY'
import numpy as np, pandas as pd
from pathlib import Path

data = Path('/task/data')
cat = pd.read_parquet(data/'candidate_catalog.parquet').sort_values('popularity_rank')
item_ids = cat.item_id.to_numpy()
rank = {int(i): int(r) for i, r in zip(cat.item_id, cat.popularity_rank)}
pop = {int(i): int(p) for i, p in zip(cat.item_id, cat.positive_listens)}

targets = set(pd.read_parquet(data/'target_users.parquet').uid.astype(int))
el = np.load(data/'validation_eligible_candidates.npz')
uid_row = {int(u): i for i, u in enumerate(el['uids'])}
item_col = {int(it): i for i, it in enumerate(el['item_ids'])}
item_n = len(el['item_ids'])

def eligible(uid, item):
    r, c = uid_row.get(int(uid)), item_col.get(int(item))
    if r is None or c is None:
        return False
    byte, bit = divmod(c, 8)
    return bool((int(el['packed_eligible'][r, byte]) >> bit) & 1)

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>=80)][['uid','item_id']].drop_duplicates()
olikes = likes[likes.uid.isin(targets) & (likes.is_organic==1)][['uid','item_id']].drop_duplicates()

graded = {}
for uid, item in strong.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 1
for uid, item in olikes.itertuples(index=False, name=None):
    if eligible(uid, item):
        graded.setdefault(int(uid), {})[int(item)] = 3

print('scored users', len(graded), 'relevant pairs', sum(len(v) for v in graded.values()))
grades = [g for u in graded.values() for g in u.values()]
print('grade3', grades.count(3), 'grade1', grades.count(1))
rels = [(uid, item, g, rank.get(item, -1), pop.get(item, -1)) for uid, items in graded.items() for item, g in items.items()]
rdf = pd.DataFrame(rels, columns=['uid','item','grade','pop_rank','pos_listens'])
print(rdf.groupby('grade')[['pop_rank','pos_listens']].describe())
print('median rank all relevant', rdf.pop_rank.median(), 'mean', rdf.pop_rank.mean())
# how often would uniform sample hit a relevant item?
# per user relevant count
counts = [len(v) for v in graded.values()]
print('relevant per scored user mean/median/max', np.mean(counts), np.median(counts), max(counts))
print('P(uniform item is relevant | user) ~', np.mean(counts)/30000)
# popularity mass of relevant items
total_pos = cat.positive_listens.sum()
print('relevant items popularity share if sampled by freq', rdf.pos_listens.sum()/total_pos/len(rdf)*np.mean(counts))
# catalog popularity concentration
s = cat.positive_listens.sort_values(ascending=False).to_numpy()
cs = np.cumsum(s)/s.sum()
for p in [0.01,0.05,0.1,0.2,0.5]:
    k = int(np.searchsorted(cs, p)+1)
    print(f'top {k} items ({k/30000:.3%}) cover {p:.0%} of positive listens')

# train dislike overlap with catalog
dis = pd.read_parquet(data/'train_dislikes.parquet')
print('train dislikes catalog overlap', dis.item_id.isin(cat.item_id).mean(), 'rows', len(dis))
print('unique users with dislikes', dis.uid.nunique())
print('target users with dislikes', dis.uid.isin(targets).sum(), 'of rows; users', dis[dis.uid.isin(targets)].uid.nunique())
PY
Status: running
Duration: 504.89s
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18.log

=== Output ===


Waited the requested 180s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

killed: Task was terminated successfully

- [completed] 1: Explore production model, data, and evaluation
- [in_progress] 2: Diagnose negative-sampling limitations
- [pending] 3: Implement improved training objective
- [pending] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission


<system-reminder>
Background task "call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18" completed (terminated by signal killed).
Description: Analyze relevance vs popularity and negative hardness | Duration: 508.0s
Use get_command_or_subagent_output("call-5ff9d780-fab5-44d7-b6ee-7d0e2faf1c79-18") to see the full output.
</system-reminder>

exit: 1
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'

exit: 0
strong pairs 255608 organic likes 17195
eligible strong 35597 eligible likes 4417
scored users 4263 pairs 37769 g3 4417 g1 33352
      pop_rank                                 ... pos_listens                        
         count         mean          std  min  ...         25%    50%     75%      max
grade                                          ...                                    
1      33352.0  8406.287659  8124.885100  1.0  ...       402.0  865.0  1993.5  26615.0
3       4417.0  8891.975323  8053.409263  2.0  ...       375.0  770.0  1745.0  25536.0

[2 rows x 16 columns]
median/mean rank 5622.0 8463.087743916969
relevant per scored user mean/median/p90/max 8.859723199624677 4.0 21.0 442
P(uniform relevant) 0.0002953241066541559
top 9 (0.03%) cover 1% listens
top 79 (0.26%) cover 5% listens
top 225 (0.75%) cover 10% listens
top 702 (2.34%) cover 20% listens
top 4147 (13.82%) cover 50% listens
relevant rank q 0.1 428.0
relevant rank q 0.25 1733.0
relevant rank q 0.5 5622.0
relevant rank q 0.75 13374.0
relevant rank q 0.9 21680.200000000004
relevant in top 1% catalog 0.07588233736662342
relevant in top 10% catalog 0.3511610050570574
relevant in top 20% catalog 0.5171172125287935
catalog median pos listens 354.0 relevant median 855.0
catalog mean pos listens 668.5443 relevant mean 1761.1246524927851

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

1645→    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()
1650→
    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
1660→            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
1670→            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
1680→                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
1690→                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)

1755→                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
1760→                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct / max(total_negative_pairs, 1),
1770→        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
1780→    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
1790→    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
1800→    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0

1690→                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            disliked_items = torch.from_numpy(
1700→                prepared.disliked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives = draw_mixed_hard_negatives(
                positive_items,
                flat_user_rows,
                liked_items,
                disliked_items,
1710→                known_positive_packed,
                len(prepared.item_ids),
                config,
                bank,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
1720→                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                bank,
                block[8].reshape(-1)[flat_scored],
                flat_user_rows,
1730→                liked_items,
                known_positive_packed,
                device,
                backward=True,
            )
            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()
1740→
            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
1750→                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
1760→                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct / max(total_negative_pairs, 1),

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

1778→
@torch.no_grad()
1780→def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
1790→    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
1800→        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
1810→        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
1820→                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
1830→                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
1840→                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
1850→            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
1860→                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
1870→                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
1880→                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=False,
1890→            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

<workspace_result workspace_path="/app">
No matches found
</workspace_result>

<workspace_result workspace_path="/app">
Found 13 matching lines
/app/solution/train_current_model.py
154:    item_correction: torch.Tensor
567:def build_item_correction(
576:    ).item_correction
635:        item_correction=torch.from_numpy(correction),
704:    item_correction: torch.Tensor,
753:        ] = item_correction[inbatch[:, :corrected_count]].numpy()
799:    item_correction: torch.Tensor,
813:            item_correction,
824:        item_correction,
1969:    item_correction = build_item_correction(
2093:                    item_correction,
2116:            item_correction,
2130:            item_correction,
</workspace_result>

1955→    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
1960→        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
1970→        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
        prepared,
        partition.history_training_counts,
1980→        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
1990→    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
        )
        stored_config = payload.get("config", {})
        architecture_fields = (
2000→            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
2010→            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
2020→        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
2030→            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
2040→        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
2050→        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
2060→                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
2070→            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
2080→                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
2090→                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
2100→        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
2110→            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
2120→            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
2130→            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
2140→                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/test_negative_sampling.py has been created.

exit: 0
ok

The file /app/solution/train_current_model.py has been updated successfully.

<task-id>call-105ad619-cccd-40f6-a4ed-04713b591ee8-65</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-105ad619-cccd-40f6-a4ed-04713b591ee8-65.log</output-file>
<status>running</status>
<summary>Command "Train improved two-tower on validation stage" is still running after 30s (block_until_ms) and has been moved to the background as task call-105ad619-cccd-40f6-a4ed-04713b591ee8-65.</summary>
Use get_command_or_subagent_output with task_ids=["call-105ad619-cccd-40f6-a4ed-04713b591ee8-65"] when you need the output.

- [completed] 1: Explore production model, data, and evaluation
- [completed] 2: Diagnose negative-sampling limitations
- [completed] 3: Implement improved training objective
- [in_progress] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission

=== Task call-105ad619-cccd-40f6-a4ed-04713b591ee8-65 ===
Command: cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
Status: running
Duration: 122.17s
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-105ad619-cccd-40f6-a4ed-04713b591ee8-65.log

=== Output ===
ok
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
mixed negatives: uniform=64 popularity=128 neighbor=16 dislike=8 inbatch_softmax=True pop_power=0.75
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250


Waited the requested 90s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Task call-105ad619-cccd-40f6-a4ed-04713b591ee8-65 ===
Command: cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
Status: running
Duration: 246.66s
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-105ad619-cccd-40f6-a4ed-04713b591ee8-65.log

=== Output ===
ok
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
mixed negatives: uniform=64 popularity=128 neighbor=16 dislike=8 inbatch_softmax=True pop_power=0.75
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250


Waited the requested 120s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

exit: 0 [truncated: showing first/last 19.7 KB of 50.9 KB - full output at: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-f25986fd-48be-40d3-ba0a-d1b7aac2d791-69.log]
    PID %CPU %MEM     ELAPSED CMD
   7167  592 38.5       04:07 /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
   5366  0.2  0.8       25:10 grok --no-auto-update --single # Improve a production music-discovery two-tower  You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team's next launch gate.  Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis.  The source used to reproduce the production model is included under `/task/current_model/`. You may copy it into `/app/solution/`, edit it, add tests, and improve its training objective, but keep the data protocol, two-tower serving contract, and evaluation boundary intact.  ## Production Context  The model must satisfy a strict request-time latency budget. Item vectors are precomputed offline. At request time, one user vector is computed from observed interaction history and compared with candidate item vectors by a batchable two-tower score. Do not introduce a cross-encoder, per-candidate sequence model, or user-ID lookup table.  The current trainer is a mature baseline with chronological internal validation. Focus your investigation on how its training objective selects and uses negative examples. Diagnose limitations in the current negative-sampling strategy and improve it under the fixed CPU budget. Validate changes with the public backtest. The final approach must still train and rank within the supplied CPU environment while preserving the serving and data boundaries above.  ## What You Have  All public inputs are under `/task/data/`. A stage cutoff is the timestamp at which observed history and candidate eligibility are frozen for a ranking. The public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute gap precedes the seven-day `val_*` w
indow. For the final submission, that validation window becomes observed history, and another 30-minute gap precedes the hidden seven-day outcome window.  The package includes:  - `/task/current_model/train_current_model.py`: the production training and   ranking script. It can reproduce the public validation ranking and produce a   hidden-test submission after refitting. - `/task/current_model/two_tower_model.py`: model primitives used by the   production script. - `/task/evaluate_public.py`: the public validation evaluator for development. - `/task/data/validation_eligible_candidates.npz` and   `/task/data/test_eligible_candidates.npz`: platform-owned candidate sets for   the validation and hidden-test cutoffs. - `/task/data/validation_preference_state.parquet` and   `/task/data/test_preference_state.parquet`: active explicit preference state   at the corresponding cutoffs. - `/task/requirements.txt`: the exact offline Python distribution inventory.  You can reproduce the production public backtest with:  ```sh python /task/current_model/train_current_model.py \   --stage validation \   --public-dir /task \   --output /app/solution/production_validation.parquet python /task/evaluate_public.py /app/solution/production_validation.parquet ```  ## Data  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 lis
ten is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.  The two public windows have different roles:  1. For model development and selection, train only from `train_*` and score the    subsequent `val_*` outcomes. 2. After selecting an improvement, 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")`.  Validation candidates exclude all `train_*` encounters. Test candidates exclude all `train_*` and `val_*` encounters. 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 same 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.  ## Deliverable  Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`.  The ranking must:  - include the columns `uid`, `item_id`, and `rank`; only these columns are   evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra   users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user's `test_eligible_candidates.npz`   candidate set.  Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate.  ## 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. - Do not alter cutoff definitions, target users, candidate eligibility, or   relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated   model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a   correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives   the deadline.  --always-approve --output-format streaming-json --session-id d48f62b5-a5f8-408c-b41c-1dbf6dc71514 --model grok-4.6 --background-wait-timeout 600 --reasoning-effort high
   5367  0.0  0.0       25:10 bash -c set -o pipefail; export PATH="$HOME/.grok/bin:$PATH" rm -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.pid /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.children  {   grok --no-auto-update --single '# Improve a production music-discovery two-tower  You have joined the recommendations team for a music streaming service. The "Made for You" shelf is served by a mature production two-tower model. Its chronological data protocol and indexed serving path are sound, but its offline retrieval quality no longer clears the team'"'"'s next launch gate.  Your job is to investigate the production model, improve its retrieval quality, and ship a better ranking artifact. Use the implementation and public validation data to form and test your own diagnosis.  The sou

... (output truncated) ...

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.  ## Deliverable  Submit one final ranking as `/app/solution/submission.parquet` or `/app/solution/submission.csv`. It must be a regular file, not a symbolic link. If both files exist, the verifier evaluates `submission.parquet` and ignores `submission.csv`.  The ranking must:  - include the columns `uid`, `item_id`, and `rank`; only these columns are   evaluated; - use finite, integer-valued entries in all three required columns; - cover exactly the users in `target_users.parquet`, with no missing or extra   users; - contain exactly 100 rows and 100 distinct items for every target user; - contain every integer rank from 1 through 100 exactly once per user; and - use only items present in that user'"'"'s `test_eligible_candidates.npz`   candidate set.  Also leave the improved implementation used to build the artifact at `/app/solution/train_current_model.py`, along with any local modules it imports. A concise `/app/solution/README.md` with the diagnosis, before-and-after public score, model change, and submission command is useful for the experiment record, but it is not an automated acceptance gate.  ## 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. - Do not alter cutoff definitions, target users, candidate eligibility, or   relevance labels. - Improve the production pipeline in place. Do not substitute an unrelated   model family or evade the indexed two-tower serving constraint. - The environment includes CPU PyTorch 2.8.0. No accele
rator is available. - You have up to 60 minutes. Prioritize measured validation improvements, a   correct final refit, and a valid submission over broad architecture search. - Keep the best valid submission saved throughout the attempt so it survives   the deadline. ' --always-approve --output-format streaming-json --session-id d48f62b5-a5f8-408c-b41c-1dbf6dc71514 --model grok-4.6 --background-wait-timeout 600 --reasoning-effort high </dev/null &   GROK_PID=$!   echo "$GROK_PID" > /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.pid   wait "$GROK_PID"   echo $? > /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit } 2>&1 | stdbuf -oL tee /logs/agent/grok-build.txt &  {   # Grok spawns background tool tasks as setsid'd session leaders, so killing   # each child's process group also reaps grandchildren (npm -> node etc.).   # Children sharing the script's own group (never grok's task style) get a   # plain kill so grok and this script are never signalled.   OWN_PGID=$(ps -o pgid= -p $$ 2>/dev/null | tr -d ' ')   kill_pids() {     SIG=$1; shift     for PID in "$@"; do       PGID=$(ps -o pgid= -p "$PID" 2>/dev/null | tr -d ' ')       if [ -n "$PGID" ] && [ "$PGID" != "$OWN_PGID" ]; then         kill -"$SIG" -- "-$PGID" 2>/dev/null || kill -"$SIG" "$PID" 2>/dev/null       else         kill -"$SIG" "$PID" 2>/dev/null       fi     done   }   # Sleep in poll-sized slices so we react promptly once grok exits.   wait_for_exit() {     REMAIN=$1     while [ "$REMAIN" -gt 0 ]; do       [ -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit ] && return 0       sleep 5       REMAIN=$((REMAIN - 5))     done     [ -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit ]   }   while [ ! -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit ]; do     GROK_PID=$(cat /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.pid 2>/dev/null)     # Snapshot live children so orphans can be swept after grok exits.     [ -n "$GROK_PID" ] && ps -o pid= --ppid "
$GROK_PID" 2>/dev/null > /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.children     sleep 5     grep -qs '"type":"turn_ended"' "$HOME"/.grok/sessions/*/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/events.jsonl || continue     wait_for_exit 660 && break     [ -n "$GROK_PID" ] || continue     CHILDREN=$(ps -o pid= --ppid "$GROK_PID" 2>/dev/null)     [ -n "$CHILDREN" ] || continue     echo "$(date -u +%FT%TZ) grok-build watchdog: grok still running 660s after turn end; terminating leftover child process groups: $CHILDREN" | tee -a /logs/agent/grok-build-watchdog.log >&2     kill_pids TERM $CHILDREN     wait_for_exit 10 && break     CHILDREN=$(ps -o pid= --ppid "$GROK_PID" 2>/dev/null)     [ -n "$CHILDREN" ] && kill_pids KILL $CHILDREN   done } &  # Key on grok's own exit signal, not pipeline completion: an orphan holding # the stdout pipe would otherwise stall tee (and this script) until it dies. while [ ! -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit ]; do   sleep 5 done  # Pre-reap grok versions exit their background wait leaving tasks orphaned # (reparented to init); sweep the last child snapshot so nothing outlives the # agent phase or holds ports during verification. if [ -s /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.children ]; then   SWEPT=""   for PID in $(cat /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.children); do     kill -0 "$PID" 2>/dev/null && SWEPT="$SWEPT $PID"   done   if [ -n "$SWEPT" ]; then     echo "$(date -u +%FT%TZ) grok-build watchdog: sweeping orphaned processes:$SWEPT" | tee -a /logs/agent/grok-build-watchdog.log >&2     OWN_PGID=$(ps -o pgid= -p $$ 2>/dev/null | tr -d ' ')     for PID in $SWEPT; do       PGID=$(ps -o pgid= -p "$PID" 2>/dev/null | tr -d ' ')       if [ -n "$PGID" ] && [ "$PGID" != "$OWN_PGID" ]; then         kill -TERM -- "-$PGID" 2>/dev/null || kill -TERM "$PID" 2>/dev/null       else         kill -TERM "$PID" 2>/dev/null       fi     done   fi fi  # Orphans are gone, so tee gets EOF prom
ptly and the watchdog sees the exit # file; both finish here. wait STATUS=$(cat /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit 2>/dev/null) rm -f /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.pid /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.exit /tmp/.grok-build-d48f62b5-a5f8-408c-b41c-1dbf6dc71514.children exit "${STATUS:-1}" 
   7145  0.0  0.0       04:11 /usr/bin/bash -O extglob -c snap=$(command cat <&3); builtin shopt -s extglob 2>/dev/null; builtin shopt -s expand_aliases 2>/dev/null; builtin eval -- "$snap"; builtin export GROK_AGENT=1; builtin export PWD="$(builtin pwd)"; unalias find 2>/dev/null || true; find() { local __grok_bin=''; [ -x "$__grok_bin" ] || __grok_bin=$(command -v bfs 2>/dev/null) || __grok_bin=''; if [ -z "$__grok_bin" ]; then command find "$@"; return; fi; if [[ -z ${ZSH_VERSION-} ]] && (( BASH_SUBSHELL > 0 )); then exec -a find "$__grok_bin" "$@"; else (exec -a find "$__grok_bin" "$@"); fi; }; __grok_shadow_find=1; unalias grep 2>/dev/null || true; grep() { local __grok_bin=''; [ -x "$__grok_bin" ] || __grok_bin=$(command -v ugrep 2>/dev/null) || __grok_bin=''; if [ -z "$__grok_bin" ]; then command grep "$@"; return; fi; if [[ -z ${ZSH_VERSION-} ]] && (( BASH_SUBSHELL > 0 )); then exec -a grep "$__grok_bin" -G --ignore-files --hidden -I --exclude-dir=.git --exclude-dir=.svn --exclude-dir=.hg --exclude-dir=.bzr --exclude-dir=.jj --exclude-dir=.sl "$@"; else (exec -a grep "$__grok_bin" -G --ignore-files --hidden -I --exclude-dir=.git --exclude-dir=.svn --exclude-dir=.hg --exclude-dir=.bzr --exclude-dir=.jj --exclude-dir=.sl "$@"); fi; }; __grok_shadow_grep=1; __grok_user_cmd="$1"; builtin declare +x __grok_user_cmd 2>/dev/null; builtin set --; builtin eval "$__grok_user_cmd" 2>&1 -- cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
   7478  0.0  0.0       00:04 sleep 5
   7483  0.0  0.0       00:01 sleep 5
   7484  0.0  0.0       00:00 /usr/bin/bash -O extglob -c snap=$(command cat <&3); builtin shopt -s extglob 2>/dev/null; builtin shopt -s expand_aliases 2>/dev/null; builtin eval -- "$snap"; builtin export GROK_AGENT=1; builtin export PWD="$(builtin pwd)"; unalias find 2>/dev/null || true; find() { local __grok_bin=''; [ -x "$__grok_bin" ] || __grok_bin=$(command -v bfs 2>/dev/null) || __grok_bin=''; if [ -z "$__grok_bin" ]; then command find "$@"; return; fi; if [[ -z ${ZSH_VERSION-} ]] && (( BASH_SUBSHELL > 0 )); then exec -a find "$__grok_bin" "$@"; else (exec -a find "$__grok_bin" "$@"); fi; }; __grok_shadow_find=1; unalias grep 2>/dev/null || true; grep() { local __grok_bin=''; [ -x "$__grok_bin" ] || __grok_bin=$(command -v ugrep 2>/dev/null) || __grok_bin=''; if [ -z "$__grok_bin" ]; then command grep "$@"; return; fi; if [[ -z ${ZSH_VERSION-} ]] && (( BASH_SUBSHELL > 0 )); then exec -a grep "$__grok_bin" -G --ignore-files --hidden -I --exclude-dir=.git --exclude-dir=.svn --exclude-dir=.hg --exclude-dir=.bzr --exclude-dir=.jj --exclude-dir=.sl "$@"; else (exec -a grep "$__grok_bin" -G --ignore-files --hidden -I --exclude-dir=.git --exclude-dir=.svn --exclude-dir=.hg --exclude-dir=.bzr --exclude-dir=.jj --exclude-dir=.sl "$@"); fi; }; __grok_shadow_grep=1; __grok_user_cmd="$1"; builtin declare +x __grok_user_cmd 2>/dev/null; builtin set --; builtin eval "$__grok_user_cmd" 2>&1 -- ps -eo pid,pcpu,pmem,etime,cmd --sort=-pcpu | head -20; echo '---'; ls -lh /proc/$(pgrep -f train_current_model.py | head -1)/status 2>/dev/null | head; pid=$(pgrep -f train_current_model.py | head -1); echo pid=$pid; if [ -n "$pid" ]; then awk '/VmRSS|VmSize|Threads/' /proc/$pid/status; ls -l /proc/$pid/fd 2>/dev/null | wc -l; fi
   7488  0.0  0.0       00:00 ps -eo pid,pcpu,pmem,etime,cmd --sort=-pcpu
   7489  0.0  0.0       00:00 head -20
---
-r--r--r-- 1 agent agent 0 Sep 29 15:55 /proc/5357/status
pid=5357
VmSize:    4508 kB
VmRSS:    3360 kB
Threads:1
4

=== Task call-105ad619-cccd-40f6-a4ed-04713b591ee8-65 ===
Command: cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
Status: completed
Duration: 408.61s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-105ad619-cccd-40f6-a4ed-04713b591ee8-65.log

=== Output ===
ok
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
mixed negatives: uniform=64 popularity=128 neighbor=16 dislike=8 inbatch_softmax=True pop_power=0.75
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=7.070562 running_bce=7.082296 elapsed=276.4s
epoch=1/1 train_loss=7.07077 validation_loss=6.49434 pair_accuracy=0.634 train_targets=1,052,953 validation_targets=130,103 train_seconds=294.5 validation_seconds=16.9
restored best validation-BCE checkpoint: epoch=1 loss=6.494339
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-mixed-negatives",
  "stage": "validation",
  "output": "/app/solution/improved_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 400.142,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-sampled-softmax-at-every-timestep",
    "negative_sampling": "mixed-uniform-popularity-neighbor-dislike-plus-inbatch-softmax",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 2048,
    "uniform_negatives": 64,
    "inbatch_negatives": 0,
    "popularity_negatives": 128,
    "neighbor_negatives": 16,
    "dislike_negatives": 8,
    "popularity_sampling_power": 0.75,
    "neighbor_window": 400,
    "softmax_temperature": 1.0,
    "use_inbatch_softmax": true,
    "use_logq_correction": true,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 7.070771085197942,
      "positive_logit": 10.781242669121571,
      "negative_logit": 10.167099209440526,
      "pair_accuracy": 0.6338379592362462,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 294.51840483699993,
      "validation_loss": 6.494339119284499,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 16.887628209000013
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.2487671375274658,
    "query_norm_std": 0.6540223956108093,
    "item_norm_mean": 2.1939032077789307,
    "item_norm_std": 1.216524600982666
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/improved_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.021949,
    "quality_users": 4263
  }
}

The file /app/solution/train_current_model.py has been updated successfully.

40→

@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
50→    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 2_048
    uniform_negatives: int = 64
    inbatch_negatives: int = 32
60→    popularity_negatives: int = 96
    neighbor_negatives: int = 24
    dislike_negatives: int = 8
    popularity_sampling_power: float = 0.75
    neighbor_window: int = 400
    softmax_temperature: float = 1.0
    use_inbatch_softmax: bool = False
    use_logq_correction: bool = False
    epochs: int = 2
    inbatch_correction_power: float = 0.5
70→    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
80→    max_validation_targets_per_user: int = 16
    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray

The file /app/solution/train_current_model.py has been updated successfully.

<task-id>call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75.log</output-file>
<status>running</status>
<summary>Command "Retrain mixed-negative softmax for two epochs" is still running after 30s (block_until_ms) and has been moved to the background as task call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75.</summary>
Use get_command_or_subagent_output with task_ids=["call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75"] when you need the output.

=== Task call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75 ===
Command: cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 2
Status: running
Duration: 392.71s
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75.log

=== Output ===
ok
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
mixed negatives: uniform=64 popularity=96 neighbor=24 dislike=8 inbatch_softmax=False pop_power=0.75
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/2 batches=250 targets=987,417 batch_bce=5.068508 running_bce=5.022711 elapsed=348.2s


Waited the requested 360s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Task call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75 ===
Command: cd /app/solution && /opt/task-venv/bin/python test_negative_sampling.py && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/improved_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 2
Status: completed
Duration: 893.57s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-80822912-cb1d-44c3-a4fe-ac937a6fd2e3-75.log

=== Output ===
ok
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
mixed negatives: uniform=64 popularity=96 neighbor=24 dislike=8 inbatch_softmax=False pop_power=0.75
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/2 batches=250 targets=987,417 batch_bce=5.068508 running_bce=5.022711 elapsed=348.2s
epoch=1/2 train_loss=5.01189 validation_loss=4.81941 pair_accuracy=0.699 train_targets=1,052,953 validation_targets=130,103 train_seconds=372.7 validation_seconds=24.4
epoch=2/2 batches=250 targets=991,385 batch_bce=4.303927 running_bce=4.595548 elapsed=354.2s
epoch=2/2 train_loss=4.58782 validation_loss=4.42242 pair_accuracy=0.801 train_targets=1,052,953 validation_targets=130,103 train_seconds=376.9 validation_seconds=22.1
restored best validation-BCE checkpoint: epoch=2 loss=4.422420
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-mixed-negatives",
  "stage": "validation",
  "output": "/app/solution/improved_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 884.983,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-sampled-softmax-at-every-timestep",
    "negative_sampling": "mixed-uniform-popularity-neighbor-dislike-plus-inbatch-softmax",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 2,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 2048,
    "uniform_negatives": 64,
    "inbatch_negatives": 32,
    "popularity_negatives": 96,
    "neighbor_negatives": 24,
    "dislike_negatives": 8,
    "popularity_sampling_power": 0.75,
    "neighbor_window": 400,
    "softmax_temperature": 1.0,
    "use_inbatch_softmax": false,
    "use_logq_correction": false,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 5.011891509342244,
      "positive_logit": 0.7621605275464349,
      "negative_logit": 0.006976467080285116,
      "pair_accuracy": 0.6989850184020207,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 372.7295036730002,
      "validation_loss": 4.819410366505141,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 24.40388506399995
    },
    {
      "epoch": 2.0,
      "loss": 4.587819170619667,
      "positive_logit": 1.5044480259854434,
      "negative_logit": -0.4527286800229592,
      "pair_accuracy": 0.8007795898720186,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 376.93080550700006,
      "validation_loss": 4.42241968050398,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 22.125928057999772
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.5478829145431519,
    "query_norm_std": 0.5191384553909302,
    "item_norm_mean": 2.7434515953063965,
    "item_norm_std": 1.646201729774475
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/improved_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01459,
    "quality_users": 4263
  }
}

<task-id>call-f38ba8ae-7349-4184-9991-81685c195559-79</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-f38ba8ae-7349-4184-9991-81685c195559-79.log</output-file>
<status>running</status>
<summary>Command "Train production two-tower for public baseline" is still running after 30s (block_until_ms) and has been moved to the background as task call-f38ba8ae-7349-4184-9991-81685c195559-79.</summary>
Use get_command_or_subagent_output with task_ids=["call-f38ba8ae-7349-4184-9991-81685c195559-79"] when you need the output.

exit: 0
improved_validation.parquet rows 845600 users 8456
 top10 pop_rank mean/median 2461.393862346263 456.0
 rank1 pop_rank mean/median 2144.3505203405866 256.0
 unique items in top10 2311 in rank1 623
 top10 rank percentiles {0.1: 20.0, 0.25: 83.0, 0.5: 456.0, 0.75: 2786.0, 0.9: 7783.0}
 fraction of top10 that are catalog head100 0.28100756859035003
 fraction of rank1 that are catalog head100 0.3608088930936613

exit: 0
{'quality_ndcg@10': 0.01071, 'quality_users': 4263}
random {'quality_ndcg@10': 0.000241, 'quality_users': 4263}

=== Task call-f38ba8ae-7349-4184-9991-81685c195559-79 ===
Command: cd /task/current_model && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/validation_sequence_cache.npz --threads 8 --epochs 1
Status: completed
Duration: 321.90s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-f38ba8ae-7349-4184-9991-81685c195559-79.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=202.6s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=216.8 validation_seconds=15.3
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 317.452,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5398380470389406,
      "positive_logit": 0.7427799644728054,
      "negative_logit": -0.6222247040033001,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 216.84161183199967,
      "validation_loss": 0.49663621816065123,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 15.333065745000113
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.603016972541809,
    "query_norm_std": 0.7697497606277466,
    "item_norm_mean": 1.7570279836654663,
    "item_norm_std": 0.8279485702514648
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
prod top10 pop mean/median 2597.0539025543994 111.0
prod rank1 mean/median 2618.422303689688 22.0
unique top10 592 rank1 161
frac top10 head100 0.4791154210028382
frac rank1 head100 0.620151371807001
percentiles {0.1: 5.0, 0.25: 14.0, 0.5: 111.0, 0.75: 2124.0, 0.9: 7317.0}

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

<task-id>call-db8dfab8-86c0-47ea-b3fd-a11a4bfc2f1b-88</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/d48f62b5-a5f8-408c-b41c-1dbf6dc71514/terminal/call-db8dfab8-86c0-47ea-b3fd-a11a4bfc2f1b-88.log</output-file>
<status>running</status>
<summary>Command "Train in-batch softmax with discovery-graded weights" is still running after 30s (block_until_ms) and has been moved to the background as task call-db8dfab8-86c0-47ea-b3fd-a11a4bfc2f1b-88.</summary>
Use get_command_or_subagent_output with task_ids=["call-db8dfab8-86c0-47ea-b3fd-a11a4bfc2f1b-88"] when you need the output.