Agent trace

- [in_progress] 1: Explore production model, data, and evaluation setup
- [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 hidden-test 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]
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,
    )

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
---
task-venv
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 === shape=(30000, 4) cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3

=== target_users.parquet === shape=(8456, 3) cols=['uid', 'recent_positive_listens', 'history_positive_listens']
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359

=== validation_preference_state.parquet === shape=(469124, 3) cols=['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked

=== test_preference_state.parquet === shape=(484776, 3) cols=['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked

=== train_listens.parquet === shape=(43484159, 6) uids=9238 items=845395
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100      39420  8326270           0               100                   170
1  100      39420  1441281           0               100                   105
organic mean 0.5169151828370419 pos50 0.6324315712303417 pos80 0.5922891828263254
organic&80 0.2800464187429726
organic&50 0.3036181520723443

=== val_listens.parquet === shape=(1501697, 6) uids=8196 items=191747
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100   24943430  9321269           0               100                   195
1  100   24943605  5452713           0               100                   175
organic mean 0.517072352145606 pos50 0.6425790289252759 pos80 0.6023385543155511
organic&80 0.28377562184648436
organic&50 0.3078190873391903

=== train_likes.parquet === shape=(814831, 4) uids=8144 items=172303
   uid  timestamp  item_id  is_organic
0  100      44755   732449           1
1  100    1155860  6568592           0

=== val_likes.parquet === shape=(37795, 4) uids=4114 items=22902
   uid  timestamp  item_id  is_organic
0  100   25249855  3526521           1
1  300   25046245  4867327           1

=== train_dislikes.parquet === shape=(101075, 4) uids=5732 items=50876
   uid  timestamp  item_id  is_organic
0  100    3087560  9170134           1
1  100    3936560  8661238           1

=== catalog === ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] n 30000
            item_id  positive_listens  organic_positive_listens  popularity_rank
count  3.000000e+04      30000.000000              30000.000000     30000.000000
mean   4.698278e+06        668.544300                313.926433     15000.500000
std    2.700274e+06       1053.389553                636.039902      8660.398374
min    2.060000e+02        163.000000                  0.000000         1.000000
25%    2.382368e+06        228.000000                 93.000000      7500.750000
50%    4.687992e+06        354.000000                162.000000     15000.500000
75%    7.040711e+06        669.250000                309.000000     22500.250000
max    9.390294e+06      26615.000000              20620.000000     30000.000000
n_target_users 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
val elig uids (8456,) items (30000,) packed (8456, 3750)
eligible per user mean/min/max/median 29067.306291390727 24657 30000 29242.0
test elig uids (8456,)
test eligible mean/min/max/median 29041.3178807947 24552 30000 29220.0
same users? True
same users order? True

exit: 0
8
2.8.0+cpu 4
total 340M
-r--r--r-- 1 root root 342K Sep 29 23:24 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 29 23:24 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 23:24 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 23:25 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 23:24 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 23:24 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 23:24 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 23:24 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 23:24 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 29 23:24 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 23:24 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 23:24 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 23:24 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 29 23:24 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 23:24 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 23:25 validation_preference_state.parquet
               total        used        free      shared  buff/cache   available
Mem:            15Gi       1.0Gi       2.3Gi       3.0Mi        12Gi        14Gi
Swap:             0B          0B          0B
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                5300.00
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               128 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)

exit: 0
/app/solution:
total 108
drwxr-xr-x 1 agent agent  4096 Sep 29 23:35 .
drwxr-xr-x 1 agent agent  4096 Sep 29 23:26 ..
-r--r--r-- 1 agent agent 80058 Sep 29 23:35 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 23:35 two_tower_model.py

/task/current_model:
total 104
dr-xr-xr-x 1 root root  4096 Sep 29 23:24 .
dr-xr-xr-x 1 root root  4096 Sep 29 23:26 ..
-r--r--r-- 1 root root 80058 Sep 29 23:24 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 23:24 two_tower_model.py

- [completed] 1: Explore production model, data, and evaluation setup
- [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 hidden-test submission

exit: 0

exit: 0
forward 1024x30k 0.05687992572784424 s shape torch.Size([1024, 30000])
fwd+bwd CE 1024x30k 0.20512776374816893 s
fwd+bwd CE 512x30k 0.10714635848999024 s
sampled softmax 6400x96 0.2132190942764282 s
encode 30k 0.08262429237365723 s

exit: 0
B=2048 fwd+bwd CE 0.3950541019439697 s score_mb 245.76
B=4096 fwd+bwd CE 0.7739651997884115 s score_mb 491.52
B=6400 fwd+bwd CE 1.1823493639628093 s score_mb 768.0
GRU 64x100 fwd+bwd 0.062187910079956055 s
done

<task-id>call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20.log</output-file>
<status>running</status>
<summary>Command "Build and cache validation-stage sequence data" is still running after 30s (block_until_ms) and has been moved to the background as task call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20.</summary>
Use get_command_or_subagent_output with task_ids=["call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20"] when you need the output.

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

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.

1485→                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
1490→                    sequence_outputs[row, count - 1]
                    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)
1500→            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(
                    np.int64
1510→                )
            ).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]
            if config.loss_type == "full-softmax":
                negatives = None
1520→                negative_weights = None
            else:
                negatives, negative_weights = draw_training_negatives(
                    positive_items,
                    known_history,
                    liked_items,
                    flat_user_rows,
                    known_positive_packed,
                    prepared,
                    config,
1530→                    item_correction,
                    generator,
                    device,
                )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
1540→                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,
                user_rows=flat_user_rows,
                known_positive_packed=known_positive_packed,
1550→            )
            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
1560→            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1

1685→                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
1690→                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
1700→                negatives,
                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:
1710→            release_accelerator_cache(device)
    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]:
1720→    return {
        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],
1730→    config: Config,
    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,
1740→            "config": asdict(config),
            "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
1750→

def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
1760→) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    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(
1770→        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()):,} "
        f"all={prepared.target_count:,}"

1635→                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
1640→                start,
                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,
            )
1650→            hidden = torch.stack(
                [
                    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):
1660→                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(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
1670→                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)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
1680→            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
1690→                device,
            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
1700→                negatives,
                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:
1710→            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,

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.

<workspace_result workspace_path="/app">
Found 17 matching lines
/app/solution/train_current_model.py
144:    validation_counts: np.ndarray
149:        return int(self.training_counts.sum() + self.validation_counts.sum())
891:    validation_counts: np.ndarray,
895:    training_region_ends = total_targets - validation_counts
900:        or np.any(validation_counts < 0)
904:        window_starts + training_counts + validation_counts, total_targets
931:        validation_counts=validation,
1602:    validation_counts: np.ndarray,
1609:    totals = training_counts + validation_counts
1610:    users_with_validation = np.flatnonzero(validation_counts > 0)
1773:    holdout_counts = partition.validation_counts
1776:        training_counts = partition.training_counts + partition.validation_counts
1810:        filter_counts = filter_counts + partition.validation_counts
1921:                    validation_counts,
1958:            validation_counts,
2325:                        target_partition.validation_counts.sum()
2395:                    target_partition.validation_counts.sum()
</workspace_result>

1895→            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
1900→            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}, "
                f"target window {stored_window_values}->{current_window_values}; "
1910→                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
1920→                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
1930→        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,
            optimizer,
1940→            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
1950→        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
1960→            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,
                epoch,
1970→                current_state,
                config,
                diagnostics,
                optimizer,
                generator,

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.

=== Task call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet --prepared-cache /app/solution/cache_validation.npz --prepare-only --threads 8
Status: completed
Duration: 54.54s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-be1f86d3-7b5b-4e30-8275-2c176694efbf-20.log

=== Output ===
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.9s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.2s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.0s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.7s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.3s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.6s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.4s
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/cache_validation.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": 50.505,
  "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
  }
}

exit: 0
loss full-softmax micro 2048 weighting discovery-graded full_window True
per_row 96
ok

exit: 0
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
config full-softmax discovery-graded True 2048
loaded 9135 users 20056329 tokens
smoke users 8 targets 861
gru 0.011569738388061523 s
targets in block 641
loss time 0.8098206520080566 s {'loss_sum': 2661.2678, 'positive_sum': -6.1886, 'negative_sum': 0.0, 'pair_correct': 10383236.0, 'weight_sum': 258.5, 'rank_sum': 8386265.0}
step ok loss 10.295039931395067

exit: 0
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
users 64 targets 9216 max 144
gru 0.04504108428955078
block targets 6400
micro 1024 time 4.713 loss 10.3187
micro 2048 time 4.102 loss 10.3186
micro 4096 time 3.686 loss 10.3186
micro 6400 time 3.956 loss 10.3186

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.

exit: 0
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
block targets 6400
loss+bwd 2.551 gru_bwd+step 0.095 loss 10.3203 pair 62221.0
finite True

- [completed] 1: Explore production model, data, and evaluation setup
- [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 hidden-test submission

<task-id>call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.log</output-file>
<status>running</status>
<summary>Command "Train improved two-tower and rank public validation" is still running after 30s (block_until_ms) and has been moved to the background as task call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.</summary>
Use get_command_or_subagent_output with task_ids=["call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52"] when you need the output.

=== Task call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/cache_validation.npz \
  --checkpoint-dir /app/solution/checkpoints_val \
  --threads 8 \
  --epochs 1 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window
Status: running
Duration: 122.72s
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 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.

exit: 0 [truncated: showing first/last 20.2 KB of 51.3 KB - full output at: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-071d1483-af38-49e6-85bf-5ba6b7f1557d-54.log]
    PID %CPU %MEM     ELAPSED CMD
   5357  0.0  0.0       19:25 bash -c set -o pipefail; export PATH="$HOME/.grok/bin:$PATH" rm -f /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.pid /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.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 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 inpu
ts 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_*` window. 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 listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.  The two public windows have different roles:  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 O
bjective  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.  ## Constra
ints  - 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 5e8dea72-19fb-47d2-9e13-9e422343b32d --model grok-4.6 --background-wait-timeout 600 --reasoning-effort high </dev/null &   GROK_PID=$!   echo "$GROK_PID" > /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.pid   wait "$GROK_PID"   echo $? > /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit } 2>&1 | stdbuf -oL tee /logs/agent/grok-build.txt &  {   # Grok spawns background tool tasks as setsid'd session leaders, so killing   # each child's process group also reaps grandchildren (npm -> node etc.).   # Children sharing the script's own group (never grok's task style) get a   # plain kill so grok and this script are never signalled.   OWN_PGID=$(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       els

... (output truncated) ...

ssion.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. ' --alw
ays-approve --output-format streaming-json --session-id 5e8dea72-19fb-47d2-9e13-9e422343b32d --model grok-4.6 --background-wait-timeout 600 --reasoning-effort high </dev/null &   GROK_PID=$!   echo "$GROK_PID" > /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.pid   wait "$GROK_PID"   echo $? > /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit } 2>&1 | stdbuf -oL tee /logs/agent/grok-build.txt &  {   # Grok spawns background tool tasks as setsid'd session leaders, so killing   # each child's process group also reaps grandchildren (npm -> node etc.).   # Children sharing the script's own group (never grok's task style) get a   # plain kill so grok and this script are never signalled.   OWN_PGID=$(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-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit ] && return 0       sleep 5       REMAIN=$((REMAIN - 5))     done     [ -f /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit ]   }   while [ ! -f /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit ]; do     GROK_PID=$(cat /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.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-5e8dea72-19fb-47d2-9e13-9e422343b32d.children     sleep 5     grep -qs '"type":"turn_ended"' "$HOME"/.grok/sessions/*/5e8dea72-19fb-47d2-9e13-9e422343b32d/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-5e8dea72-19fb-47d2-9e13-9e422343b32d.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-5e8dea72-19fb-47d2-9e13-9e422343b32d.children ]; then   SWEPT=""   for PID in $(cat /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.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 "$PID" 2>/dev/null       fi     done   fi fi  # Orphans are gone, so tee gets EOF promptly and the watchdog sees the exit # file; both finish here. wait STATUS=$(cat /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit 2>/dev/null) rm -f /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.pid /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.exit
 /tmp/.grok-build-5e8dea72-19fb-47d2-9e13-9e422343b32d.children exit "${STATUS:-1}" 
   6934  0.0  0.0       02:10 /usr/bin/bash -O extglob -c snap=$(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 train_current_model.py \   --stage validation \   --public-dir /task \   --output /app/solution/validation_ranking.parquet \   --prepared-cache /app/solution/cache_validation.npz \   --checkpoint-dir /app/solution/checkpoints_val \   --threads 8 \   --epochs 1 \   --loss-type full-softmax \   --target-weighting discovery-graded \   --fit-full-window
   6938  561 12.2       02:10 /opt/task-venv/bin/python train_current_model.py --stage validation --public-dir /task --output /app/solution/validation_ranking.parquet --prepared-cache /app/solution/cache_validation.npz --checkpoint-dir /app/solution/checkpoints_val --threads 8 --epochs 1 --loss-type full-softmax --target-weighting discovery-graded --fit-full-window
   7124  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 | awk 'NR==1 || /train_current_model|python/ {print}' | head -20; echo '---'; tail -20 /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.log
   7129  0.0  0.0       00:00 awk NR==1 || /train_current_model|python/ {print}
---
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250

=== Task call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/cache_validation.npz \
  --checkpoint-dir /app/solution/checkpoints_val \
  --threads 8 \
  --epochs 1 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window
Status: running
Duration: 617.18s
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
epoch=1/1 batches=250 targets=1,106,512 batch_bce=8.984297 running_bce=9.375724 elapsed=501.9s
saved epoch checkpoint: /app/solution/checkpoints_val/epoch-01.pt
epoch=1/1 train_loss=9.36769 validation_loss=9.03344 pair_accuracy=0.779 train_targets=1,183,056 validation_targets=130,103 train_seconds=537.5 validation_seconds=33.3
restored best validation-BCE checkpoint: epoch=1 loss=9.033440


Waited the requested 480s. 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-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/cache_validation.npz \
  --checkpoint-dir /app/solution/checkpoints_val \
  --threads 8 \
  --epochs 1 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window
Status: completed
Duration: 669.23s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-b13544b0-2a7f-4d6b-b15a-0be305a0a121-52.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
epoch=1/1 batches=250 targets=1,106,512 batch_bce=8.984297 running_bce=9.375724 elapsed=501.9s
saved epoch checkpoint: /app/solution/checkpoints_val/epoch-01.pt
epoch=1/1 train_loss=9.36769 validation_loss=9.03344 pair_accuracy=0.779 train_targets=1,183,056 validation_targets=130,103 train_seconds=537.5 validation_seconds=33.3
restored best validation-BCE checkpoint: epoch=1 loss=9.033440
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/validation_ranking.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 664.984,
  "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": "full-catalog-softmax-with-false-negative-mask",
    "loss": "full-softmax",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "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": 16,
    "unigram_negatives": 32,
    "matched_negatives": 16,
    "inbatch_negatives": 24,
    "dislike_negatives": 8,
    "shared_unigram_negatives": 0,
    "unigram_power": 0.75,
    "matched_rank_window": 250,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "loss_type": "full-softmax",
    "softmax_temperature": 1.0,
    "logq_scale": 0.5,
    "dislike_aux_weight": 0.2,
    "fit_full_window": true,
    "target_weighting": "discovery-graded",
    "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": 9.367694686426294,
      "positive_logit": -2.287510429482269,
      "negative_logit": 0.0,
      "pair_accuracy": 0.7792352760562475,
      "targets": 1183056.0,
      "target_weight_sum": 390909.5,
      "batches": 267.0,
      "seconds": 537.455751553,
      "validation_loss": 9.0334398560558,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 33.32337289099996
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 2.560360908508301,
    "query_norm_std": 1.0978474617004395,
    "item_norm_mean": 4.118657112121582,
    "item_norm_std": 0.8920438885688782
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/validation_ranking.parquet",
  "scores": {
    "quality_ndcg@10": 0.023635,
    "quality_users": 4263
  }
}

exit: 1
top10 mean popularity_rank 1352.5338457899716 median 88.0
top10 describe
 count    84560.000000
mean      1352.533846
std       3466.701372
min          1.000000
25%         18.000000
50%         88.000000
75%        770.000000
max      29926.000000
Name: pop_rank, dtype: float64
rank1 mean pop 1079.8656575212867
overall catalog mean pop rank 15000.5
Traceback (most recent call last):
  File "<stdin>", line 16, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'

exit: 0
item_ids match catalog True
popularity baseline {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
current {'quality_ndcg@10': 0.023635, 'quality_users': 4263}
mean overlap top10 with pop top10 2.853476821192053
model gold in top10 (694, 37769) top100 (2801, 37769)
pop gold in top10 (368, 37769) top100 (1814, 37769)
model g3 top10 (79, 4417) g1 (615, 33352)
pop g3 top10 (34, 4417) g1 (334, 33352)

<task-id>call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60.log</output-file>
<status>running</status>
<summary>Command "Resume full-softmax training for a second epoch" is still running after 30s (block_until_ms) and has been moved to the background as task call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60.</summary>
Use get_command_or_subagent_output with task_ids=["call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60"] when you need the output.

=== Task call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking_e2.parquet \
  --prepared-cache /app/solution/cache_validation.npz \
  --checkpoint-dir /app/solution/checkpoints_val \
  --resume-checkpoint /app/solution/checkpoints_val/epoch-01.pt \
  --threads 8 \
  --epochs 2 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window
Status: running
Duration: 645.04s
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
restored optimizer state from /app/solution/checkpoints_val/epoch-01.pt
resumed validation-BCE training: epoch=1 loss=9.033440
epoch=2/2 batches=250 targets=1,107,439 batch_bce=8.770271 running_bce=8.902698 elapsed=499.5s
saved epoch checkpoint: /app/solution/checkpoints_val/epoch-02.pt
epoch=2/2 train_loss=8.88988 validation_loss=8.51920 pair_accuracy=0.840 train_targets=1,183,056 validation_targets=130,103 train_seconds=534.7 validation_seconds=34.8
restored best validation-BCE checkpoint: epoch=2 loss=8.519199


Waited the requested 600s. 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-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking_e2.parquet \
  --prepared-cache /app/solution/cache_validation.npz \
  --checkpoint-dir /app/solution/checkpoints_val \
  --resume-checkpoint /app/solution/checkpoints_val/epoch-01.pt \
  --threads 8 \
  --epochs 2 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window
Status: completed
Duration: 670.45s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-4cdd34dc-c6c1-4872-9fbc-19427eecfa56-60.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,183,056 holdout=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,564,879 bytes=34,256,250
restored optimizer state from /app/solution/checkpoints_val/epoch-01.pt
resumed validation-BCE training: epoch=1 loss=9.033440
epoch=2/2 batches=250 targets=1,107,439 batch_bce=8.770271 running_bce=8.902698 elapsed=499.5s
saved epoch checkpoint: /app/solution/checkpoints_val/epoch-02.pt
epoch=2/2 train_loss=8.88988 validation_loss=8.51920 pair_accuracy=0.840 train_targets=1,183,056 validation_targets=130,103 train_seconds=534.7 validation_seconds=34.8
restored best validation-BCE checkpoint: epoch=2 loss=8.519199
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/validation_ranking_e2.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 666.191,
  "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": "full-catalog-softmax-with-false-negative-mask",
    "loss": "full-softmax",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "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": 16,
    "unigram_negatives": 32,
    "matched_negatives": 16,
    "inbatch_negatives": 24,
    "dislike_negatives": 8,
    "shared_unigram_negatives": 0,
    "unigram_power": 0.75,
    "matched_rank_window": 250,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "loss_type": "full-softmax",
    "softmax_temperature": 1.0,
    "logq_scale": 0.5,
    "dislike_aux_weight": 0.2,
    "fit_full_window": true,
    "target_weighting": "discovery-graded",
    "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": 9.367694686426294,
      "positive_logit": -2.287510429482269,
      "negative_logit": 0.0,
      "pair_accuracy": 0.7792352760562475,
      "targets": 1183056.0,
      "target_weight_sum": 390909.5,
      "batches": 267.0,
      "seconds": 537.455751553,
      "validation_loss": 9.0334398560558,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 33.32337289099996
    },
    {
      "epoch": 2.0,
      "loss": 8.889880212053333,
      "positive_logit": -4.118913671355498,
      "negative_logit": 0.0,
      "pair_accuracy": 0.8398854587187757,
      "targets": 1183056.0,
      "target_weight_sum": 390909.5,
      "batches": 267.0,
      "seconds": 534.686061803,
      "validation_loss": 8.519199354813074,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 34.832712596999954
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 2.798676013946533,
    "query_norm_std": 0.9638814330101013,
    "item_norm_mean": 5.064150810241699,
    "item_norm_std": 0.9050956964492798
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/validation_ranking_e2.parquet",
  "scores": {
    "quality_ndcg@10": 0.018175,
    "quality_users": 4263
  }
}

<task-id>call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64.log</output-file>
<status>running</status>
<summary>Command "Sweep cosine and popularity blends on epoch-1 checkpoint" is still running after 30s (block_until_ms) and has been moved to the background as task call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64.</summary>
Use get_command_or_subagent_output with task_ids=["call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64"] when you need the output.

=== Task call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64 ===
Command: cd /app/solution && /opt/task-venv/bin/python - <<'PY'
import sys, time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.nn import functional as F

import train_current_model as t
from train_current_model import Config
sys.path.insert(0,'/task')
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

config = Config()
device = torch.device('cpu')
torch.set_num_threads(8)
prepared, eligibility = t.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/cache_validation.npz'))
model = t.base.SequentialTwoTower(prepared.item_features, t.model_config(config)).to(device)
payload = torch.load('/app/solution/checkpoints_val/epoch-01.pt', map_location='cpu', weights_only=False)
model.load_state_dict(payload['model_state'])
model.eval()
t0=time.time()
with torch.inference_mode():
    queries = t.inference_queries(model, prepared, config, device)
    item_parts=[]
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('encoded', time.time()-t0, 'q', queries.shape, 'i', items.shape)

cat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
log_pop = torch.from_numpy(np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)))
log_org = torch.from_numpy(np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32)))
q_norm = F.normalize(queries, dim=1)
i_norm = F.normalize(items, dim=1)
item_n = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-6)

targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
elig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))
graded = validation_truth(elig, targets)

def evaluate(score_mat, name):
    rows=[]
    for start in range(0, len(eligibility.uids), 64):
        end=min(start+64, len(eligibility.uids))
        scores = score_mat[start:end].clone()
        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)
        selected = torch.topk(scores, k=100, dim=1).indices.numpy()
        for local, cols in enumerate(selected):
            uid=int(eligibility.uids[start+local])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r,c in enumerate(cols,1))
    frame=pd.DataFrame(rows, columns=['uid','item_id','rank'])
    ranked={int(uid): list(map(int, vals)) for uid, vals in frame.sort_values(['uid','rank']).groupby('uid').item_id}
    s=score_rankings(ranked, graded)
    print(name, s)
    return s, frame

# precompute dots in chunks to save memory? 8456 x 30000 x 4 = 1GB OK
dots = queries @ items.T
cos = q_norm @ i_norm.T
print('score mats ready')

results=[]
for name, mat in [
    ('dot', dots),
    ('cosine', cos),
    ('dot/norm0.5', dots / item_n.pow(0.5).unsqueeze(0)),
    ('dot+0.2logpop', dots + 0.2*log_pop),
    ('dot+0.5logpop', dots + 0.5*log_pop),
    ('dot-0.2logpop', dots - 0.2*log_pop),
    ('dot-0.5logpop', dots - 0.5*log_pop),
    ('cos+0.2logpop', cos + 0.05*log_pop),
    ('cos-0.05logpop', cos - 0.05*log_pop),
    ('dot+0.2logorg', dots + 0.2*log_org),
    ('dot-0.2logorg', dots - 0.2*log_org),
]:
    evaluate(mat, name)
PY
Status: failed
Duration: 88.79s
Exit Code: 137
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-d84a1c99-cc52-4dd2-9499-ec56317466d7-64.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
--: line 81:  8862 Killed                  /opt/task-venv/bin/python - <<'PY'
import sys, time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.nn import functional as F

import train_current_model as t
from train_current_model import Config
sys.path.insert(0,'/task')
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

config = Config()
device = torch.device('cpu')
torch.set_num_threads(8)
prepared, eligibility = t.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/cache_validation.npz'))
model = t.base.SequentialTwoTower(prepared.item_features, t.model_config(config)).to(device)
payload = torch.load('/app/solution/checkpoints_val/epoch-01.pt', map_location='cpu', weights_only=False)
model.load_state_dict(payload['model_state'])
model.eval()
t0=time.time()
with torch.inference_mode():
    queries = t.inference_queries(model, prepared, config, device)
    item_parts=[]
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('encoded', time.time()-t0, 'q', queries.shape, 'i', items.shape)

cat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
log_pop = torch.from_numpy(np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)))
log_org = torch.from_numpy(np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32)))
q_norm = F.normalize(queries, dim=1)
i_norm = F.normalize(items, dim=1)
item_n = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-6)

targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
elig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))
graded = validation_truth(elig, targets)

def evaluate(score_mat, name):
    rows=[]
    for start in range(0, len(eligibility.uids), 64):
        end=min(start+64, len(eligibility.uids))
        scores = score_mat[start:end].clone()
        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)
        selected = torch.topk(scores, k=100, dim=1).indices.numpy()
        for local, cols in enumerate(selected):
            uid=int(eligibility.uids[start+local])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r,c in enumerate(cols,1))
    frame=pd.DataFrame(rows, columns=['uid','item_id','rank'])
    ranked={int(uid): list(map(int, vals)) for uid, vals in frame.sort_values(['uid','rank']).groupby('uid').item_id}
    s=score_rankings(ranked, graded)
    print(name, s)
    return s, frame

# precompute dots in chunks to save memory? 8456 x 30000 x 4 = 1GB OK
dots = queries @ items.T
cos = q_norm @ i_norm.T
print('score mats ready')

results=[]
for name, mat in [
    ('dot', dots),
    ('cosine', cos),
    ('dot/norm0.5', dots / item_n.pow(0.5).unsqueeze(0)),
    ('dot+0.2logpop', dots + 0.2*log_pop),
    ('dot+0.5logpop', dots + 0.5*log_pop),
    ('dot-0.2logpop', dots - 0.2*log_pop),
    ('dot-0.5logpop', dots - 0.5*log_pop),
    ('cos+0.2logpop', cos + 0.05*log_pop),
    ('cos-0.05logpop', cos - 0.05*log_pop),
    ('dot+0.2logorg', dots + 0.2*log_org),
    ('dot-0.2logorg', dots - 0.2*log_org),
]:
    evaluate(mat, name)
PY

<task-id>call-76832796-8550-4808-8e44-98f05aab5da2-66</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-76832796-8550-4808-8e44-98f05aab5da2-66.log</output-file>
<status>running</status>
<summary>Command "Memory-safe scoring-variant sweep on epoch-1 model" is still running after 30s (block_until_ms) and has been moved to the background as task call-76832796-8550-4808-8e44-98f05aab5da2-66.</summary>
Use get_command_or_subagent_output with task_ids=["call-76832796-8550-4808-8e44-98f05aab5da2-66"] when you need the output.

=== Task call-76832796-8550-4808-8e44-98f05aab5da2-66 ===
Command: cd /app/solution && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np, pandas as pd, torch
from torch.nn import functional as F
import train_current_model as t
from train_current_model import Config
sys.path.insert(0,'/task')
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

config = Config()
device = torch.device('cpu')
torch.set_num_threads(8)
prepared, eligibility = t.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/cache_validation.npz'))
model = t.base.SequentialTwoTower(prepared.item_features, t.model_config(config)).to(device)
payload = torch.load('/app/solution/checkpoints_val/epoch-01.pt', map_location='cpu', weights_only=False)
model.load_state_dict(payload['model_state'])
model.eval()
with torch.inference_mode():
    queries = t.inference_queries(model, prepared, config, device)
    item_parts=[]
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('encoded', queries.shape, items.shape, 'mem ok')
cat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
log_pop = torch.from_numpy(np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)))
q_norm = F.normalize(queries, dim=1)
i_norm = F.normalize(items, dim=1)
item_n = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-6)

targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
graded = validation_truth(CandidateSet(Path('/task/data/validation_eligible_candidates.npz')), targets)

def eval_fn(make_scores, name):
    rows=[]
    for start in range(0, len(eligibility.uids), 128):
        end=min(start+128, len(eligibility.uids))
        scores = make_scores(start, end)
        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)
        selected = torch.topk(scores, k=100, dim=1).indices.numpy()
        for local, cols in enumerate(selected):
            uid=int(eligibility.uids[start+local])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r,c in enumerate(cols,1))
    frame=pd.DataFrame(rows, columns=['uid','item_id','rank'])
    ranked={int(uid): list(map(int, vals)) for uid, vals in frame.sort_values(['uid','rank']).groupby('uid').item_id}
    print(name, score_rankings(ranked, graded), flush=True)

eval_fn(lambda s,e: queries[s:e] @ items.T, 'dot')
eval_fn(lambda s,e: q_norm[s:e] @ i_norm.T, 'cosine')
eval_fn(lambda s,e: (queries[s:e] @ items.T) / item_n.pow(0.5), 'dot/n0.5')
eval_fn(lambda s,e: (queries[s:e] @ items.T) - 0.3*log_pop, 'dot-0.3pop')
eval_fn(lambda s,e: (queries[s:e] @ items.T) + 0.3*log_pop, 'dot+0.3pop')
eval_fn(lambda s,e: (q_norm[s:e] @ i_norm.T) - 0.02*log_pop, 'cos-0.02pop')
print('done')
PY
Status: completed
Duration: 105.67s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-76832796-8550-4808-8e44-98f05aab5da2-66.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
encoded torch.Size([8456, 192]) torch.Size([30000, 192]) mem ok
dot {'quality_ndcg@10': 0.023635, 'quality_users': 4263}
cosine {'quality_ndcg@10': 0.025454, 'quality_users': 4263}
dot/n0.5 {'quality_ndcg@10': 0.026063, 'quality_users': 4263}
dot-0.3pop {'quality_ndcg@10': 0.025853, 'quality_users': 4263}
dot+0.3pop {'quality_ndcg@10': 0.019503, 'quality_users': 4263}
cos-0.02pop {'quality_ndcg@10': 0.022987, 'quality_users': 4263}
done

<task-id>call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68.log</output-file>
<status>running</status>
<summary>Command "Sweep item-norm exponent and mild popularity penalty" is still running after 30s (block_until_ms) and has been moved to the background as task call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68.</summary>
Use get_command_or_subagent_output with task_ids=["call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68"] when you need the output.

=== Task call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68 ===
Command: cd /app/solution && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np, pandas as pd, torch
import train_current_model as t
from train_current_model import Config
sys.path.insert(0,'/task')
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

config = Config()
device = torch.device('cpu')
torch.set_num_threads(8)
prepared, eligibility = t.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/cache_validation.npz'))
model = t.base.SequentialTwoTower(prepared.item_features, t.model_config(config)).to(device)
payload = torch.load('/app/solution/checkpoints_val/epoch-01.pt', map_location='cpu', weights_only=False)
model.load_state_dict(payload['model_state'])
model.eval()
with torch.inference_mode():
    queries = t.inference_queries(model, prepared, config, device)
    item_parts=[]
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
cat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
log_pop = torch.from_numpy(np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)))
item_n = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-6)
graded = validation_truth(CandidateSet(Path('/task/data/validation_eligible_candidates.npz')),
                          set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)))

def eval_fn(make_scores, name):
    rows=[]
    for start in range(0, len(eligibility.uids), 256):
        end=min(start+256, len(eligibility.uids))
        scores = make_scores(start, end)
        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)
        selected = torch.topk(scores, k=100, dim=1).indices.numpy()
        for local, cols in enumerate(selected):
            uid=int(eligibility.uids[start+local])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r,c in enumerate(cols,1))
    frame=pd.DataFrame(rows, columns=['uid','item_id','rank'])
    ranked={int(uid): list(map(int, vals)) for uid, vals in frame.sort_values(['uid','rank']).groupby('uid').item_id}
    print(name, score_rankings(ranked, graded), flush=True)

for a in (0.25, 0.4, 0.5, 0.6, 0.75):
    eval_fn(lambda s,e, a=a: (queries[s:e] @ items.T) / item_n.pow(a), f'dot/n^{a}')
eval_fn(lambda s,e: (queries[s:e] @ items.T)/item_n.pow(0.5) - 0.15*log_pop, 'n0.5-0.15pop')
eval_fn(lambda s,e: (queries[s:e] @ items.T)/item_n.pow(0.5) - 0.3*log_pop, 'n0.5-0.3pop')
print('done')
PY
Status: completed
Duration: 102.10s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-1fbc9f43-5e68-4e6e-b152-387ecd446ddc-68.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
dot/n^0.25 {'quality_ndcg@10': 0.024089, 'quality_users': 4263}
dot/n^0.4 {'quality_ndcg@10': 0.025398, 'quality_users': 4263}
dot/n^0.5 {'quality_ndcg@10': 0.026063, 'quality_users': 4263}
dot/n^0.6 {'quality_ndcg@10': 0.02658, 'quality_users': 4263}
dot/n^0.75 {'quality_ndcg@10': 0.027132, 'quality_users': 4263}
n0.5-0.15pop {'quality_ndcg@10': 0.027909, 'quality_users': 4263}
n0.5-0.3pop {'quality_ndcg@10': 0.023213, 'quality_users': 4263}
done

<task-id>call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70.log</output-file>
<status>running</status>
<summary>Command "Fine-sweep item-norm power and popularity penalty" is still running after 30s (block_until_ms) and has been moved to the background as task call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70.</summary>
Use get_command_or_subagent_output with task_ids=["call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70"] when you need the output.

=== Task call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70 ===
Command: cd /app/solution && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np, pandas as pd, torch
import train_current_model as t
from train_current_model import Config
sys.path.insert(0,'/task')
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

config = Config()
device = torch.device('cpu')
torch.set_num_threads(8)
prepared, eligibility = t.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/cache_validation.npz'))
model = t.base.SequentialTwoTower(prepared.item_features, t.model_config(config)).to(device)
payload = torch.load('/app/solution/checkpoints_val/epoch-01.pt', map_location='cpu', weights_only=False)
model.load_state_dict(payload['model_state'])
model.eval()
with torch.inference_mode():
    queries = t.inference_queries(model, prepared, config, device)
    items = torch.cat([model.encode_items(torch.arange(s, min(s+2048, len(prepared.item_ids)))).cpu()
                       for s in range(0, len(prepared.item_ids), 2048)])
cat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
log_pop = torch.from_numpy(np.log1p(cat.positive_listens.to_numpy(dtype=np.float32)))
item_n = torch.linalg.vector_norm(items, dim=1).clamp_min(1e-6)
graded = validation_truth(CandidateSet(Path('/task/data/validation_eligible_candidates.npz')),
                          set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)))

def eval_fn(make_scores, name):
    rows=[]
    for start in range(0, len(eligibility.uids), 256):
        end=min(start+256, len(eligibility.uids))
        scores = make_scores(start, end)
        mask = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores.masked_fill_(~torch.from_numpy(mask), -torch.inf)
        selected = torch.topk(scores, k=100, dim=1).indices.numpy()
        for local, cols in enumerate(selected):
            uid=int(eligibility.uids[start+local])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r,c in enumerate(cols,1))
    frame=pd.DataFrame(rows, columns=['uid','item_id','rank'])
    ranked={int(uid): list(map(int, vals)) for uid, vals in frame.sort_values(['uid','rank']).groupby('uid').item_id}
    print(name, score_rankings(ranked, graded), flush=True)

for a,p in [(0.5,0.1),(0.5,0.12),(0.5,0.15),(0.5,0.18),(0.6,0.12),(0.6,0.15),(0.75,0.08),(0.75,0.12),(0.75,0.15),(1.0,0.0),(1.0,0.1)]:
    eval_fn(lambda s,e, a=a,p=p: (queries[s:e] @ items.T)/item_n.pow(a) - p*log_pop, f'n^{a}-p{p}')
print('done')
PY
Status: completed
Duration: 111.52s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-93ef2849-1b85-44ba-b0c0-965428e56fd8-70.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
n^0.5-p0.1 {'quality_ndcg@10': 0.027973, 'quality_users': 4263}
n^0.5-p0.12 {'quality_ndcg@10': 0.028025, 'quality_users': 4263}
n^0.5-p0.15 {'quality_ndcg@10': 0.027909, 'quality_users': 4263}
n^0.5-p0.18 {'quality_ndcg@10': 0.026903, 'quality_users': 4263}
n^0.6-p0.12 {'quality_ndcg@10': 0.027313, 'quality_users': 4263}
n^0.6-p0.15 {'quality_ndcg@10': 0.026808, 'quality_users': 4263}
n^0.75-p0.08 {'quality_ndcg@10': 0.026591, 'quality_users': 4263}
n^0.75-p0.12 {'quality_ndcg@10': 0.024931, 'quality_users': 4263}
n^0.75-p0.15 {'quality_ndcg@10': 0.023282, 'quality_users': 4263}
n^1.0-p0.0 {'quality_ndcg@10': 0.025454, 'quality_users': 4263}
n^1.0-p0.1 {'quality_ndcg@10': 0.020487, 'quality_users': 4263}
done

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.

exit: 0
OK

<task-id>call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81.log</output-file>
<status>running</status>
<summary>Command "Refit improved model on train+val and write submission" is still running after 30s (block_until_ms) and has been moved to the background as task call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81.</summary>
Use get_command_or_subagent_output with task_ids=["call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81"] when you need the output.

The file /app/solution/README.md has been created.

=== Task call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81 ===
Command: cd /app/solution && /opt/task-venv/bin/python train_current_model.py \
  --stage submission \
  --public-dir /task \
  --output /app/solution/submission.parquet \
  --prepared-cache /app/solution/cache_submission.npz \
  --checkpoint-dir /app/solution/checkpoints_sub \
  --threads 8 \
  --epochs 1 \
  --loss-type full-softmax \
  --target-weighting discovery-graded \
  --fit-full-window \
  --item-norm-power 0.5 \
  --popularity-penalty 0.12
Status: running
Duration: 215.73s
Output File: /home/agent/.grok/sessions/%2Fapp/5e8dea72-19fb-47d2-9e13-9e422343b32d/terminal/call-62cc0b53-ed47-41fd-8cfa-f03b47027431-81.log

=== Output ===
loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.4s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.8s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.4s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.5s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.4s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=37.1s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.7s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.7s
target users without qualifying catalog sequence=6
prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322
saved public-derived full-sequence cache: /app/solution/cache_submission.npz
fit_full_window: training on the entire retained chronological suffix, including the internal holdout
chronological window: train=1,199,603 holdout=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818 loss=full-softmax
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=314,586 users=7,613
grade-3 first-encounter targets=47,773 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,153 unique_user_items=5,724,310 bytes=34,323,750


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.