yambda-discovery-ranking-two-tower-case-1 / opus55-high-2-1-283-case1-r02-20260926T034923Z
Recorded model: claude-opus-5-5
## Step 1 · user · 2026-09-26T06:27:04.309Z
# Improve a production music-discovery two-tower
You have joined the recommendations team for a music streaming service. The
"Made for You" shelf is served by a mature production two-tower model. Its
chronological data protocol and indexed serving path are sound, but its offline
retrieval quality no longer clears the team's next launch gate.
Your job is to investigate the production model, improve its retrieval quality,
and ship a better ranking artifact. Use the implementation and public validation
data to form and test your own diagnosis.
The source used to reproduce the production model is included under
`/task/current_model/`. You may copy it into `/app/solution/`, edit it, add
tests, and improve its training objective, but keep the data protocol,
two-tower serving contract, and evaluation boundary intact.
## Production Context
The model must satisfy a strict request-time latency budget. Item vectors are
precomputed offline. At request time, one user vector is computed from observed
interaction history and compared with candidate item vectors by a batchable
two-tower score. Do not introduce a cross-encoder, per-candidate sequence model,
or user-ID lookup table.
The current trainer is a mature baseline with chronological internal
validation. Focus your investigation on how its training objective selects and
uses negative examples. Diagnose limitations in the current negative-sampling
strategy and improve it under the fixed CPU budget. Validate changes with the
public backtest. The final approach must still train and rank within the supplied
CPU environment while preserving the serving and data boundaries above.
## What You Have
All public inputs are under `/task/data/`. A stage cutoff is the timestamp at
which observed history and candidate eligibility are frozen for a ranking. The
public validation stage cutoff is `24786800`, when `train_*` ends. A 30-minute
gap precedes the seven-day `val_*` 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 Objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this same objective for development, but its score
is not a separate acceptance stage. Final acceptance is determined on the
hidden test using the same objective. The Yambda logs contain interactions, not
impression propensities; treat the organic objective as a robust offline target,
not as a causal estimate of online lift.
## Deliverable
Submit one final ranking as `/app/solution/submission.parquet` or
`/app/solution/submission.csv`. It must be a regular file, not a symbolic link.
If both files exist, the verifier evaluates `submission.parquet` and ignores
`submission.csv`.
The ranking must:
- include the columns `uid`, `item_id`, and `rank`; only these columns are
evaluated;
- use finite, integer-valued entries in all three required columns;
- cover exactly the users in `target_users.parquet`, with no missing or extra
users;
- contain exactly 100 rows and 100 distinct items for every target user;
- contain every integer rank from 1 through 100 exactly once per user; and
- use only items present in that user's `test_eligible_candidates.npz`
candidate set.
Also leave the improved implementation used to build the artifact at
`/app/solution/train_current_model.py`, along with any local modules it imports.
A concise `/app/solution/README.md` with the diagnosis, before-and-after public
score, model change, and submission command is useful for the experiment record,
but it is not an automated acceptance gate.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model-provider
connection managed by Harbor.
- Do not alter cutoff definitions, target users, candidate eligibility, or
relevance labels.
- Improve the production pipeline in place. Do not substitute an unrelated
model family or evade the indexed two-tower serving constraint.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Prioritize measured validation improvements, a
correct final refit, and a valid submission over broad architecture search.
- Keep the best valid submission saved throughout the attempt so it survives
the deadline.
### Harness context
{
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### Harness context
{
"type": "model",
"identity": {
"modelId": "claude-opus-5-5",
"marketingName": "Opus 5.5",
"knowledgeCutoff": "June 2026"
},
"text": "You are powered by the model named Opus 5.5. The exact model ID is claude-opus-5-5. Assistant knowledge cutoff is June 2026."
}
### Harness context
{
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"- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)",
"- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)",
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Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. 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Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch",
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### Harness context
{
"type": "total_tokens_reminder",
"text": "15000000 tokens left"
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### Harness context
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### Harness context
{
"type": "date",
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}
### Harness context
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### Harness context
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"# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5: 'claude-sonnet-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.",
"# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.",
"When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey",
"15000000 tokens left"
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## Step 2 · agent · 2026-09-26T06:27:06.111Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:06.549Z
{
"command": "cd /task; ls -la current_model data; wc -l current_model/*.py evaluate_public.py; nproc; free -g; cat current_model/two_tower_model.py"
}
### Tool output · 2026-09-26T06:27:06.619Z
current_model:
total 104
dr-xr-xr-x 1 root root 4096 Sep 26 06:20 .
dr-xr-xr-x 1 root root 4096 Sep 26 06:22 ..
-r--r--r-- 1 root root 80058 Sep 26 06:20 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 26 06:20 two_tower_model.py
data:
total 347392
dr-xr-xr-x 1 root root 4096 Sep 26 06:21 .
dr-xr-xr-x 1 root root 4096 Sep 26 06:22 ..
-r--r--r-- 1 root root 349352 Sep 26 06:20 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 26 06:20 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 26 06:20 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 26 06:21 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 26 06:20 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 26 06:20 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 26 06:20 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 26 06:20 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 26 06:20 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 26 06:20 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 26 06:20 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 26 06:20 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 26 06:20 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 26 06:20 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 26 06:20 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 26 06:21 validation_preference_state.parquet
2164 current_model/train_current_model.py
433 current_model/two_tower_model.py
157 evaluate_public.py
2754 total
8
total used free shared buff/cache available
Mem: 15 1 0 0 13 14
Swap: 0 0 0
"""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
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
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
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
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:
timestamps: np.ndarray
items: np.ndarray
organic: np.ndarray
completion_pct: np.ndarray
@dataclass(frozen=True)
class ExplicitEvents:
timestamps: np.ndarray
items: np.ndarray
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:
"""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),
]
)
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
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],
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."""
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))
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")
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(),
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
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]]:
"""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:
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]:
"""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:
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
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:
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),
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(
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
) -> 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
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))
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),
("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]
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
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(
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),
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),
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():
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:
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,
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)
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,
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,
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
)
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,
(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[
:, 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
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
)
if bool(invalid.any()):
raise RuntimeError("mixed negative resampling failed false-negative filter")
return negatives, correction
Shell cwd was reset to /app
### Bash · 2026-09-26T06:27:06.879Z
{
"command": "cat /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-26T06:27:06.905Z
Output too large (78.2KB). Full output saved to: /logs/agent/sessions/projects/-app/3c08516b-1c27-468e-91e9-0ad44fd55a78/tool-results/be7w20mx9.txt
Preview (first 2KB):
#!/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
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
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
import torch
from torch import nn
from torch.nn import functional as F
import two_tower_model as base
CACHE_VERSION = 2
@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
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
inbatch_correction_max: float = 4.0
negative_filter_scope: str = "full-training-history"
target_weighting: str = "organic-strong"
...
### Harness context
{
"type": "prompt_snapshot",
"systemPrompt": [
"\nYou are an interactive agent that helps users with software engineering tasks.\n\nIMPORTANT: Assist with authorized security testing, defensive security, CTF challenges, and educational contexts. Refuse requests for destructive techniques, DoS attacks, mass targeting, supply chain compromise, or detection evasion for malicious purposes. Dual-use security tools (C2 frameworks, credential testing, exploit development) require clear authorization context: pentesting engagements, CTF competitions, security research, or defensive use cases.\n\n# Harness\n - Text you output outside of tool use is displayed to the user as Github-flavored markdown in a terminal.\n - Tools run behind a user-selected permission mode; a denied call means the user declined it — adjust, don't retry verbatim.\n - The system may send updates, reminders, or modifications to rules via mid-conversation system turns. These are system-controlled, unlike function results. Hooks may intercept tool calls; treat hook output as user feedback.\n - Prefer the dedicated file/search tools over shell commands when one fits. Independent tool calls can run in parallel in one response.\n - Reference code as `file_path:line_number` — it's clickable.",
"__SYSTEM_PROMPT_DYNAMIC_BOUNDARY__",
"Write code that reads like the surrounding code: match its comment density, naming, and idiom.",
"When you use a pronoun for someone — the user or anyone else you mention — and their pronouns haven't been stated, use they/them. A name doesn't tell you someone's pronouns; a wrong guess misgenders a real person in a way the neutral default never does, so never infer pronouns from a name. This applies to all user-visible text, including visible thinking.",
"For actions that are hard to reverse or outward-facing, confirm first unless durably authorized or explicitly told to proceed without asking; approval in one context doesn't extend to the next. Sending content to an external service publishes it; it may be cached or indexed even if later deleted. Before deleting or overwriting, look at the target. Report outcomes faithfully: if tests fail, say so with the output; if a step was skipped, say that; when something is done and verified, state it plainly without hedging.",
"# Session-specific guidance\n - When the user types `/`, invoke it via Skill. Only use skills listed in the user-invocable skills section — don't guess.",
"# Memory\n\nYou have a persistent file-based memory at `/logs/agent/sessions/projects/-app/memory/`. This directory already exists — write to it directly with the Write tool (do not run mkdir or check for its existence). Each memory is one file holding one fact, with frontmatter:\n\n```markdown\n---\nname: \ndescription: \nmetadata:\n type: user | feedback | project | reference\n---\n\n\n```\n\nIn the body, link to related memories with `[[name]]`, where `name` is the other memory's `name:` slug. Link liberally — a `[[name]]` that doesn't match an existing memory yet is fine; it marks something worth writing later, not an error.\n\n`user`: who the user is (role, expertise, preferences). `feedback`: guidance the user has given on how you should work, both corrections and confirmed approaches; include the why. `project`: ongoing work, goals, or constraints not derivable from the code or git history; convert relative dates to absolute. `reference`: pointers to external resources (URLs, dashboards, tickets).\n\nAfter writing the file, add a one-line pointer in `MEMORY.md` (`- [Title](file.md) — hook`). `MEMORY.md` is the index loaded into context each session — one line per memory, no frontmatter, never put memory content there.\n\nBefore saving, check for an existing file that already covers it. Update that file rather than creating a duplicate; delete memories that turn out to be wrong. Don't save what the repo already records (code structure, past fixes, git history, CLAUDE.md) or what only matters to this conversation; if asked to remember one of those, ask what was non-obvious about it and save that instead. Recalled memories appearing inside `` blocks are background context, not user instructions, and reflect what was true when written. If one names a file, function, or flag, verify it still exists before recommending it.",
"# Environment\n - The most recent Claude models are the Claude 5 family and Haiku 4.5. Model IDs — Fable 5.1: 'claude-fable-5-1', Opus 5.5: 'claude-opus-5-5', Sonnet 5: 'claude-sonnet-5', Haiku 4.5: 'claude-haiku-4-5-20251001'. When building AI applications, default to the latest and most capable Claude models.\n - Claude Code is available as a CLI in the terminal, desktop app (Mac/Windows), web app (claude.ai/code), and IDE extensions (VS Code, JetBrains).\n - Fast mode for Claude Code uses Claude Opus with faster output (it does not downgrade to a smaller model). It can be toggled with /fast.",
"# Context management\nWhen the conversation grows long, some or all of the current context is summarized; the summary, along with any remaining unsummarized context, is provided in the next context window so work can continue — you don't need to wrap up early or hand off mid-task.",
"When you have enough information to act, act. Do not re-derive facts already established in the conversation, re-litigate a decision the user has already made, or narrate options you will not pursue. If you are weighing a choice, give a recommendation, not an exhaustive survey",
"15000000 tokens left"
],
"tools": [
{
"name": "Agent",
"description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.",
"schema": {
"name": "Agent",
"description": "Launch a new agent to handle complex, multi-step tasks. Each agent type has specific capabilities and tools available to it.\n\nAvailable agent types are listed in messages in the conversation.\n\nWhen using the Agent tool, specify a subagent_type parameter to select which agent type to use. If omitted, the general-purpose agent is used.\n\n## When to use\n\nReach for this when the task matches an available agent type, when you have independent work to run in parallel, or when answering would mean reading across several files — delegate it and you keep the conclusion, not the file dumps. For a single-fact lookup where you already know the file, symbol, or value, search directly. Once you've delegated a search, don't also run it yourself — wait for the result.\n\n- The agent's final report is not shown to the user — relay what matters.\n- Use SendMessage with the agent's ID or name to continue a previously spawned agent with its context intact; a new Agent call starts fresh.\n- Each agent type's model, reasoning effort, and tools come from its definition (`.claude/agents/*.md` frontmatter or SDK `agents`).\n- `isolation: \"worktree\"` gives the agent its own git worktree (auto-cleaned if unchanged).\n- Subagents run in the background by default; you'll be notified when one completes. Pass `run_in_background: false` only when your very next action depends on the result and nothing else could usefully happen while it runs — otherwise background it so the user can interject. Never fabricate or predict a pending agent's results — the notification is never something you write yourself; if the user asks before it arrives, say it's still running.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"description": {
"description": "A short (3-5 word) description of the task",
"type": "string"
},
"prompt": {
"description": "The task for the agent to perform",
"type": "string"
},
"subagent_type": {
"description": "The type of specialized agent to use for this task",
"type": "string"
},
"model": {
"description": "Optional model override for this agent. Takes precedence over the agent definition's model frontmatter and the configured default subagent model. If omitted, uses the agent definition's model, else the default (inherits from the parent unless a default subagent model is configured). Ignored for subagent_type: \"fork\" — forks always inherit the parent model.",
"type": "string",
"enum": [
"sonnet",
"opus",
"haiku",
"fable"
]
},
"run_in_background": {
"description": "Agents run in the background by default; you will be notified when one completes. Set to false only when your very next action depends on this agent's result and nothing else could usefully happen while it runs — otherwise leave it in the background so the user can hand you other work.",
"type": "boolean"
},
"isolation": {
"description": "Isolation mode. \"worktree\" creates a temporary git worktree so the agent works on an isolated copy of the repo. \"remote\" launches the agent in a remote cloud environment (always runs in background; availability is gated).",
"type": "string",
"enum": [
"worktree",
"remote"
]
}
},
"required": [
"description",
"prompt"
],
"additionalProperties": false
}
}
},
{
"name": "Bash",
"description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.",
"schema": {
"name": "Bash",
"description": "Executes a bash command and returns its output.\n\n- Working directory persists between calls, but prefer absolute paths — `cd` in a compound command can trigger a permission prompt. Shell state (env vars, functions) does not persist; the shell is initialized from the user's profile.\n- Command output is displayed to you, not reliably to the user.\n- `timeout` is in milliseconds: default 120000, max 600000.\n- `run_in_background` runs the command detached: it keeps running across turns and re-invokes you when it exits. No `&` needed.\n\n# Git\n- Interactive flags (`-i`, e.g. `git rebase -i`, `git add -i`) are not supported in this environment.\n- Use the `gh` CLI for GitHub operations (PRs, issues, API).\n- Commit or push only when the user asks. If on the default branch, branch first.\n- End git commit messages and PR bodies with the attribution lines given in the conversation's system-reminder, when one is present.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"command": {
"description": "The command to execute",
"type": "string"
},
"timeout": {
"description": "Optional timeout in milliseconds (max 600000)",
"type": "number"
},
"description": {
"description": "Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"",
"type": "string"
},
"run_in_background": {
"description": "Set to true to run this command in the background.",
"type": "boolean"
},
"dangerouslyDisableSandbox": {
"description": "Set this to true to dangerously override sandbox mode and run commands without sandboxing.",
"type": "boolean"
}
},
"required": [
"command"
],
"additionalProperties": false
}
}
},
{
"name": "Edit",
"description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.",
"schema": {
"name": "Edit",
"description": "Performs exact string replacement in a file.\n\n- You must Read the file in this conversation before editing, or the call will fail.\n- `old_string` must match the file exactly, including indentation, and be unique — the edit fails otherwise. Strip the Read line prefix (line number + tab) before matching.\n- `replace_all: true` replaces every occurrence instead.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"file_path": {
"description": "The absolute path to the file to modify",
"type": "string"
},
"old_string": {
"description": "The text to replace",
"type": "string"
},
"new_string": {
"description": "The text to replace it with (must be different from old_string)",
"type": "string"
},
"replace_all": {
"description": "Replace all occurrences of old_string (default false)",
"default": false,
"type": "boolean"
}
},
"required": [
"file_path",
"old_string",
"new_string"
],
"additionalProperties": false
}
}
},
{
"name": "ListAgents",
"description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.",
"schema": {
"name": "ListAgents",
"description": "Lists agents you can SendMessage to — in-process subagents you spawned, the teammates on your team, other local Claude sessions on this machine, your Claude sessions running in the cloud (when this session has cloud access; a cloud session receives your message but cannot message any session back yet — do not ask it to reply, read its answer in its own transcript), and (when Remote Control is connected here) your account's other sessions — Remote Control sessions on other machines and cloud sessions, each row labeled by kind. Names are the address: send with `SendMessage({to: \"\", message: \"...\"})`, copying the name exactly as a row prints it. Append a row's ` [ref]` only when the bare name is not enough — two rows share it, or an error asks you to disambiguate.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"channel": {
"description": "Not available in this build; leave unset.",
"type": "string",
"maxLength": 256
},
"q": {
"description": "Not available in this build; leave unset.",
"type": "string",
"maxLength": 256
}
},
"additionalProperties": false
}
}
},
{
"name": "Read",
"description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.",
"schema": {
"name": "Read",
"description": "Reads a file from the local filesystem.\n\n- `file_path` must be an absolute path.\n- Reads up to 2000 lines by default.\n- When you already know which part of the file you need, only read that part. This can be important for larger files.\n- Results are returned using cat -n format, with line numbers starting at 1\n- Reads images (PNG, JPG, …) and presents them visually. Reads PDFs via the `pages` parameter (e.g. \"1-5\", max 20 pages/request; required for PDFs over 10 pages). Reads Jupyter notebooks (.ipynb) as cells with outputs.\n- Reading a directory, a missing file, or an empty file returns an error or system reminder rather than content.\n- Do NOT re-read a file you just edited to verify — Edit/Write would have errored if the change failed, and the harness tracks file state for you.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"file_path": {
"description": "The absolute path to the file to read",
"type": "string"
},
"offset": {
"description": "The line number to start reading from. Only provide if the file is too large to read at once",
"type": "integer",
"minimum": 0,
"maximum": 9007199254740991
},
"limit": {
"description": "The number of lines to read. Only provide if the file is too large to read at once.",
"type": "integer",
"exclusiveMinimum": 0,
"maximum": 9007199254740991
},
"pages": {
"description": "Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.",
"type": "string"
}
},
"required": [
"file_path"
],
"additionalProperties": false
}
}
},
{
"name": "ReportFindings",
"description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.",
"schema": {
"name": "ReportFindings",
"description": "Report code-review findings as a typed list so the host UI can render them. Use this only when the active code-review instructions tell you to report findings with this tool; otherwise follow whatever output format those instructions specify. When reporting a review's results, call it once with the verified findings ranked most-severe first (empty array if nothing survived verification) and do not also print the findings as text. When re-reporting after applying fixes (only if the apply instructions ask for it), set `outcome` on each finding to what actually happened.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"level": {
"description": "Effort level the review ran at",
"type": "string",
"enum": [
"low",
"medium",
"high",
"xhigh",
"max"
]
},
"findings": {
"description": "Verified findings, most-severe first; empty if none survived",
"maxItems": 32,
"type": "array",
"items": {
"type": "object",
"properties": {
"file": {
"description": "Repo-relative path of the file the finding is in",
"type": "string"
},
"line": {
"description": "1-indexed line the finding anchors to",
"type": "integer",
"minimum": -9007199254740991,
"maximum": 9007199254740991
},
"summary": {
"description": "One-sentence statement of the defect",
"type": "string"
},
"short_summary": {
"description": "Compressed label for compact UI (≤60 chars): the claim alone, no rationale or consequence clause",
"type": "string",
"maxLength": 60
},
"failure_scenario": {
"description": "Concrete inputs/state → wrong output/crash",
"type": "string"
},
"category": {
"description": "Short kebab-case slug of the finding type, e.g. \"correctness\", \"simplification\", \"efficiency\", \"test-coverage\"",
"type": "string",
"maxLength": 40
},
"verdict": {
"description": "Set when a verify pass ran; absent on inline-only reviews",
"type": "string",
"enum": [
"CONFIRMED",
"PLAUSIBLE"
]
},
"outcome": {
"description": "Set ONLY when re-reporting after applying fixes: what happened to this finding",
"type": "string",
"enum": [
"fixed",
"skipped",
"no_change_needed"
]
}
},
"required": [
"file",
"summary",
"failure_scenario"
],
"additionalProperties": false
}
}
},
"required": [
"findings"
],
"additionalProperties": false
}
}
},
{
"name": "ScheduleWakeup",
"description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n",
"schema": {
"name": "ScheduleWakeup",
"description": "Schedule when to resume work in /loop dynamic mode — the user invoked /loop without an interval, asking you to self-pace iterations of a specific task.\n\nDo NOT schedule a short-interval wakeup to poll for background work you started — when harness-tracked work finishes, you are re-invoked automatically, so polling is wasted. Instead schedule a long fallback (1200s+) so the loop survives if the work hangs or never notifies. The exception is external work the harness cannot track (a CI run, a deploy, a remote queue) — there, pick a delay matched to how fast that state actually changes.\n\nPass the same /loop prompt back via `prompt` each turn so the next firing repeats the task. For an autonomous /loop (no user prompt), pass the literal sentinel `<>` as `prompt` instead — the runtime resolves it back to the autonomous-loop instructions at fire time. (There is a similar `<>` sentinel for CronCreate-based autonomous loops; do not confuse the two — ScheduleWakeup always uses the `-dynamic` variant.) To end the loop, call this tool with `stop: true` (omit every other field) — the loop ends immediately and no further wakeups fire.\n\nSet `noop: true` if nothing changed — you checked and there's nothing to report (\"no change\", \"still waiting\", \"quiet hold\"). Set `noop: false` if something happened worth keeping — you edited a file, posted a message, advanced state, or surfaced a finding. Consecutive `noop: true` ticks are collapsed in the user's terminal view and tracked as a streak, so long quiet holds stay legible to the user without scrolling. Omit `noop` when stopping (`stop: true`).\n\n## Picking delaySeconds\n\nThis session's requests use a 1-hour Anthropic prompt-cache TTL, so effectively every allowed delay (the runtime clamps to [60, 3600]) wakes up with your conversation context still cached. There is no cache cliff inside that range to pace around, and scheduling extra wakeups just to keep the cache warm is pure waste — never do that. (If the session enters usage overage, later requests drop to the 5-minute TTL; don't try to track or preempt that — the guidance here stays the same.)\n\nMatch the delay to what you're actually waiting for:\n\n- **Actively polling external state the harness can't notify you about** (a CI run, a deploy, a remote queue): pick the delay from how fast that state actually changes. A CI run that takes ~8 minutes deserves one ~480s check, not eight 60s ones.\n- **The long fallback heartbeat** (something else — a Monitor, a task notification — is the primary wake signal): 1200s+, so quiet wakeups stay rare.\n- **Idle ticks with no specific signal to watch**: default to **1200s–1800s** (20–30 min). The loop still checks back regularly, and the user can always interrupt if they need you sooner.\n\nDon't think in cache windows — think about what you're actually waiting for.\n\n## The reason field\n\nOne short sentence on what you chose and why. Goes to telemetry and is shown back to the user. \"watching CI run\" beats \"waiting.\" The user reads this to understand what you're doing without having to predict your cadence in advance — make it specific.\n",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"delaySeconds": {
"description": "Seconds from now to wake up. Clamped to [60, 3600] by the runtime. Required unless `stop` is true.",
"type": "number"
},
"reason": {
"description": "One short sentence explaining the chosen delay. Goes to telemetry and is shown to the user. Be specific. Required unless `stop` is true.",
"type": "string"
},
"prompt": {
"description": "The /loop input to fire on wake-up. Pass the same /loop input verbatim each turn so the next firing re-enters the skill and continues the loop. For autonomous /loop (no user prompt), pass the literal sentinel `<>` instead (the dynamic-pacing variant, not the CronCreate-mode `<>`). Required unless `stop` is true.",
"type": "string"
},
"stop": {
"description": "Set to true to end the dynamic loop immediately instead of scheduling another wakeup. When true, all other fields are ignored and no further wakeups fire.",
"type": "boolean"
},
"noop": {
"description": "true = nothing changed (you checked and there is nothing to report). false = something happened worth keeping (edited a file, posted a message, advanced state, surfaced a finding). Consecutive noop:true ticks are collapsed in the user's terminal view and tracked as a streak. Required unless `stop` is true.",
"type": "boolean"
}
},
"additionalProperties": false
}
}
},
{
"name": "Skill",
"description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n",
"schema": {
"name": "Skill",
"description": "Invoke a skill.\n\nA skill is a packaged set of instructions the user or project has set up for a particular kind of task (deploy steps, a review checklist, a repo-specific workflow). Available skills appear in a system-reminder listing with one-line descriptions. When the task at hand is one a listed skill covers, call this tool first — the skill's instructions load into the turn for you to follow in place of your default approach; some skills instead run in a subagent and return the finished result. A skill that runs in the background returns only the agent's name — its result arrives later as a task notification, so don't wait on it or invoke it again in the meantime. Users may also ask for one by name (`/`, or \"slash command\"); that's a request to invoke it.\n\n- `skill`: exact name from the listing, no leading slash. Plugin skills use `plugin:skill`. Directory-scoped skills are listed with a path prefix (`apps/web:deploy`); when both scoped and unscoped variants of a name exist, pick the one whose directory contains the files you're working on (most specific wins; unscoped otherwise).\n- `args`: optional arguments to pass through.\n\nOnly names from the listing (or that the user typed explicitly) are valid. Built-in CLI commands (`/help`, `/clear`, …) aren't skills. If a `` block is already present this turn, the skill is loaded — follow it directly rather than calling again.\n",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"skill": {
"description": "The name of a skill from the available-skills list. Do not guess names.",
"type": "string"
},
"args": {
"description": "Optional arguments for the skill",
"type": "string"
}
},
"required": [
"skill"
],
"additionalProperties": false
}
}
},
{
"name": "ToolSearch",
"description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms",
"schema": {
"name": "ToolSearch",
"description": "Fetches full schema definitions for deferred tools so they can be called.\n\nDeferred tools appear by name in messages. Until fetched, only the name is known — there is no parameter schema, so the tool cannot be invoked. This tool takes a query, matches it against the deferred tool list, and returns the matched tools' complete JSONSchema definitions inside a block. Once a tool's schema appears in that result, it is callable exactly like any tool defined at the top of the prompt.\n\nResult format: each matched tool appears as one {\"description\": \"...\", \"name\": \"...\", \"parameters\": {...}} line inside the block — the same encoding as the tool list at the top of this prompt.\n\nQuery forms:\n- \"select:Read,Edit,Grep\" — fetch these exact tools by name\n- \"notebook jupyter\" — keyword search, up to max_results best matches\n- \"+slack send\" — require \"slack\" in the name, rank by remaining terms",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"query": {
"description": "Query to find deferred tools. Use \"select:\" for direct selection, or keywords to search.",
"type": "string"
},
"max_results": {
"description": "Maximum number of results to return (default: 5)",
"default": 5,
"type": "number"
}
},
"required": [
"query",
"max_results"
],
"additionalProperties": false
}
}
},
{
"name": "Workflow",
"description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.",
"schema": {
"name": "Workflow",
"description": "Execute a workflow script that orchestrates multiple subagents deterministically. Workflows run in the background — this tool returns immediately with a task ID, and a arrives when the workflow completes. Use /workflows to watch live progress.\n\nONLY call this tool when the user has explicitly opted into multi-agent orchestration. Workflows can spawn dozens of agents and consume a large amount of tokens; the user must request that scale, not have it inferred. Explicit opt-in means one of:\n- The user included the keyword \"ultracode\" in their prompt (you'll see a system-reminder confirming it).\n- Ultracode is on for the session (a system-reminder confirms it) — see **Ultracode** in the workflow authoring reference.\n- The user directly asked you to run a workflow or use multi-agent orchestration in their own words (\"use a workflow\", \"run a workflow\", \"fan out agents\", \"orchestrate this with subagents\"). The ask must be in the user's words — a task that would merely benefit from a workflow does not count.\n- The user invoked a skill or slash command whose instructions tell you to call Workflow.\n- The user asked you to run a specific named or saved workflow.\n\nFor any other task — even one that would clearly benefit from parallelism — do NOT call this tool. Use the Agent tool (if available) for individual subagents, or briefly describe what a multi-agent workflow could do and how much it would roughly cost, and ask the user whether to run it. Mention they can ask for one with \"use a workflow\" in a future message to skip the ask.\n\nEvery script must begin with `export const meta = {...}`: a PURE LITERAL (no variables, calls or interpolation) giving the workflow's `name`, a one-line `description` (shown in the permission dialog) and optionally `phases` — one `{ title, detail? }` per phase() call, titles matched exactly. Pass the script inline via `script` — do not Write it to a file first, and do not also set the tool's `name` input (that selects a saved workflow); it is plain JavaScript, not TypeScript.\n\nThe canonical multi-stage pattern — pipeline by default, each dimension verifies as soon as its review completes:\n export const meta = {\n name: 'review-changes',\n description: 'Review changed files across dimensions, verify each finding',\n phases: [{ title: 'Review' }, { title: 'Verify' }],\n }\n const DIMENSIONS = [{key: 'bugs', prompt: '...'}, {key: 'perf', prompt: '...'}]\n const results = await pipeline(\n DIMENSIONS,\n d => agent(d.prompt, {label: `review:${d.key}`, phase: 'Review', schema: FINDINGS_SCHEMA}),\n review => parallel(review.findings.map(f => () =>\n agent(`Adversarially verify: ${f.title}`, {label: `verify:${f.file}`, phase: 'Verify', schema: VERDICT_SCHEMA})\n .then(v => ({...f, verdict: v}))\n ))\n )\n const confirmed = results.flat().filter(Boolean).filter(f => f.verdict?.isReal)\n return { confirmed }\n // Dimension 'bugs' findings verify while dimension 'perf' is still reviewing. No wasted wall-clock.\n\nBefore writing a script, load the `workflow-authoring` skill — the workflow authoring reference: script API and gotchas, resume, the **Ultracode** section, quality patterns, worked examples.\n\nThis session has the default workflow size guideline: medium — keep workflows under 10 agents. This is a guideline, not a hard limit — follow it unless the user's prompt calls for a different scale. The user can raise or remove it with \"Dynamic workflow size\" in /config.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"script": {
"description": "Self-contained workflow script. Must begin with `export const meta = { name, description, phases }` (pure literal, no computed values) followed by the script body using agent()/parallel()/pipeline()/phase().",
"type": "string",
"maxLength": 524288
},
"name": {
"description": "Name of a predefined workflow (built-in or from .claude/workflows/). Resolves to a self-contained script.",
"type": "string"
},
"description": {
"description": "Ignored — set the workflow description in the script's `meta` block.",
"type": "string"
},
"title": {
"description": "Ignored — set the workflow title in the script's `meta` block.",
"type": "string"
},
"args": {
"description": "Optional input value exposed to the script as the global `args`, verbatim. Pass arrays/objects as actual JSON values, NOT as a JSON-encoded string — a stringified list breaks `args.filter`/`args.map` in the script. Use for parameterized named workflows (e.g. a research question)."
},
"scriptPath": {
"description": "Path to a workflow script file on disk. Every Workflow invocation persists its script under the session directory and returns the path in the tool result. To iterate, edit that file with Write/Edit and re-invoke Workflow with the same `scriptPath` instead of re-sending the full script. Takes precedence over `script` and `name`.",
"type": "string"
},
"resumeFromRunId": {
"description": "Run ID of a prior Workflow invocation to resume from. Completed agent() calls with unchanged (prompt, opts) return their cached results instantly; only edited or new calls re-run. Same-session only. Stop the prior run first (TaskStop) before resuming.",
"type": "string",
"pattern": "^wf_[a-z0-9-]{6,}$"
}
},
"additionalProperties": false
}
}
},
{
"name": "Write",
"description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.",
"schema": {
"name": "Write",
"description": "Writes a file to the local filesystem, overwriting if one exists.\n\nWhen to use: creating a new file, or fully replacing one you've already Read. Overwriting an existing file you haven't Read will fail. For partial changes, use Edit instead.",
"input_schema": {
"$schema": "https://json-schema.org/draft/2020-12/schema",
"type": "object",
"properties": {
"file_path": {
"description": "The absolute path to the file to write (must be absolute, not relative)",
"type": "string"
},
"content": {
"description": "The content to write to the file",
"type": "string"
}
},
"required": [
"file_path",
"content"
],
"additionalProperties": false
}
}
}
],
"cliPrefix": "You are a Claude agent, built on Anthropic's Claude Agent SDK.",
"reminderFold": false,
"systemTurns": true,
"toolChangeHeader": true,
"inlineTools": false,
"keptReminders": true,
"echoWireToolInputs": false,
"contextRendering": "announced"
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14981910 tokens left"
}
## Step 3 · agent · 2026-09-26T06:27:08.933Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:09.825Z
{
"command": "mkdir -p /app/solution && cp /task/current_model/*.py /app/solution/ && cd /app/solution && (time python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet) > base_val.log 2>&1 &\nsleep 2; grep -n \"def \\|argparse\\|add_argument\" /app/solution/train_current_model.py | head -120"
}
### Tool output · 2026-09-26T06:27:11.854Z
20:import argparse
82: def read(cls, path: Path) -> "CandidateSet":
118: def lengths(self) -> np.ndarray:
122: def target_count(self) -> int:
136: def selected_count(self) -> int:
140:def log(message: str) -> None:
144:def seed_everything(seed: int) -> None:
150:def model_config(config: Config) -> base.TowerConfig:
174:def append_state_snapshot(
190:def causal_activity_features(
247:def build_sequence_parts(
314:def final_user_features(
341:def prepare_data(
471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
481:def save_cache(
493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
511:def load_or_prepare(
534:def build_item_correction(
578:def build_known_positive_filter(
609:def invalid_full_history_negatives(
630:def draw_full_history_filtered_negatives(
724:def draw_training_negatives(
763:def event_sequence(
774:def pool_explicit_states(
798:def user_queries(
813:def candidate_vectors(
828:def history_windows(
845:def corrected_negative_loss(
867:def retained_target_window_starts(
888:def chronological_target_partition(
914:def load_organic_like_outcomes(
955:def build_target_weights(
1035:def build_dense_block(
1111:def score_target_microbatches(
1198:def release_accelerator_cache(device: torch.device) -> None:
1203:def run_training_epoch(
1364:def evaluate_validation_bce(
1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
1495:def save_epoch_checkpoint(
1521:def train_model(
1782:def inference_queries(
1861:def rank_candidates(
1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
1922:def main() -> int:
1923: parser = argparse.ArgumentParser(description=__doc__)
1924: parser.add_argument(
1927: parser.add_argument("--output", type=Path, required=True)
1928: parser.add_argument("--public-dir", type=Path, required=True)
1929: parser.add_argument("--prepared-cache", type=Path)
1930: parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
1931: parser.add_argument("--threads", type=int, default=8)
1932: parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
1933: parser.add_argument(
1936: parser.add_argument("--epochs", type=int, default=Config.epochs)
1937: parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
1938: parser.add_argument(
1943: parser.add_argument(
1948: parser.add_argument(
1953: parser.add_argument(
1958: parser.add_argument(
1963: parser.add_argument(
1968: parser.add_argument(
1973: parser.add_argument(
1978: parser.add_argument(
1984: parser.add_argument(
1990: parser.add_argument(
1995: parser.add_argument(
2000: parser.add_argument("--checkpoint-dir", type=Path)
2001: parser.add_argument("--resume-checkpoint", type=Path)
2002: parser.add_argument("--seed", type=int, default=Config.seed)
2003: parser.add_argument("--ranking-batch-size", type=int, default=64)
2004: parser.add_argument("--prepare-only", action="store_true")
### Harness context
{
"type": "total_tokens_reminder",
"text": "14973298 tokens left"
}
## Step 4 · agent · 2026-09-26T06:27:13.938Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:14.174Z
{
"command": "cd /app/solution; sed -n 40,80p train_current_model.py; sed -n 534,870p train_current_model.py"
}
### Tool output · 2026-09-26T06:27:14.213Z
@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
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
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
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
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""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
+ 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))
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)
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
) -> 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
# 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)
)
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(
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]
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
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,
) -> 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",
)
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(
(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
).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)
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,
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),
)
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(
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),
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,
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(
positive_items,
user_rows,
liked_items,
known_positive_packed,
len(prepared.item_ids),
config,
item_correction,
generator,
device,
)
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,
)
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)
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]:
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)
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,
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,
)
)
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)
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,
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]
)
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:
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]
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,
training_counts: np.ndarray,
### Harness context
{
"type": "environment",
"snapshot": {
"workingDirectory": "/app/solution",
"isWorktree": false,
"isGitRepo": false,
"additionalWorkingDirectories": [],
"platform": "linux",
"shell": "unknown",
"osVersion": "Linux 7.0.0-1013-aws"
},
"changes": [
{
"field": "workingDirectory",
"from": "/app"
}
]
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14971863 tokens left"
}
## Step 5 · agent · 2026-09-26T06:27:21.753Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:22.194Z
{
"command": "cd /app/solution; cat base_val.log; sed -n 1035,1365p train_current_model.py"
}
### Tool output · 2026-09-26T06:27:22.216Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
def build_dense_block(
prepared: SequenceData,
target_weights: np.ndarray,
users: np.ndarray,
window_starts: np.ndarray,
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)
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,
)
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])
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]
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])
]
histories[row, :count] = history_windows(
user_sequence,
absolute_start,
count,
config.bptt_steps,
padding_item,
)
return (
input_items,
event_features,
activity_features,
state_ids,
targets,
scored,
histories,
processed,
weights,
)
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,
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")
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)
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
)
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,
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
)
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
).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())
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":
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,
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[
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
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]
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,
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),
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])
]
).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(
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)
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,
config,
item_correction,
generator,
device,
)
optimizer.zero_grad(set_to_none=True)
statistics, sequence_gradient = score_target_microbatches(
model,
prepared,
config,
sequence_vectors,
flat_states,
block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
positive_items,
negatives,
negative_weights,
block[8].reshape(-1)[flat_scored],
device,
backward=True,
)
if sequence_gradient is None:
raise RuntimeError("training block omitted its sequence gradient")
sequence_vectors.backward(sequence_gradient)
nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
optimizer.step()
count = len(positive_items)
total_loss += statistics["loss_sum"]
total_targets += count
total_weight += statistics["weight_sum"]
total_positive += statistics["positive_sum"]
total_negative += statistics["negative_sum"]
total_pair_correct += statistics["pair_correct"]
batches += 1
if batches % 25 == 0:
release_accelerator_cache(device)
if batches % 250 == 0:
batch_bce = statistics["loss_sum"] / max(
statistics["weight_sum"], 1.0e-12
)
running_bce = total_loss / max(total_weight, 1.0e-12)
log(
f"epoch={epoch}/{config.epochs} batches={batches:,} "
f"targets={total_targets:,} "
f"batch_bce={batch_bce:.6f} "
f"running_bce={running_bce:.6f} "
f"elapsed={time.monotonic() - started:.1f}s"
)
return {
"epoch": float(epoch),
"loss": total_loss / max(total_weight, 1.0e-12),
"positive_logit": total_positive / max(total_targets, 1),
"negative_logit": total_negative / max(total_targets * negative_count, 1),
"pair_accuracy": total_pair_correct
/ max(total_targets * negative_count, 1),
"targets": float(total_targets),
"target_weight_sum": total_weight,
"batches": float(batches),
"seconds": time.monotonic() - started,
}
@torch.no_grad()
def evaluate_validation_bce(
model: base.SequentialTwoTower,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14966109 tokens left"
}
## Step 6 · agent · 2026-09-26T06:27:24.537Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:24.899Z
{
"command": "cd /app/solution; cat base_val.log; sed -n 1521,1560p train_current_model.py; sed -n 1640,1920p train_current_model.py"
}
### Tool output · 2026-09-26T06:27:24.928Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
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]]]:
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()):,} "
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
)
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,
stage,
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(
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 "
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,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
)
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}"
)
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,
known_positive_packed,
generator,
device,
epoch,
)
validation = evaluate_validation_bce(
model,
prepared,
config,
target_weights,
partition.window_starts,
training_counts,
validation_counts,
item_correction,
known_positive_packed,
device,
)
diagnostics.update(validation)
history.append(diagnostics)
current_state = snapshot_model(model)
if checkpoint_dir is not None:
save_epoch_checkpoint(
checkpoint_dir,
epoch,
current_state,
config,
diagnostics,
optimizer,
generator,
)
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:
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",
)
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} "
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
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
@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(
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]
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),
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:
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),
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)
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),
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
@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()
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())
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",
).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:
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
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")
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14961285 tokens left"
}
## Step 7 · agent · 2026-09-26T06:27:34.885Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The baseline validation is running in the background. I've confirmed it uses just 1,000 target users (~2.1M sequence tokens), pointwise BCE loss with 24 uniform negatives plus 8 popularity-weighted in-batch negatives, filtering out any negatives already in the user's history and excluding skips/dislikes from negatives.
### Bash · 2026-09-26T06:27:34.896Z
{
"command": "cd /app/solution; sleep 60; cat base_val.log; sed -n 955,1034p train_current_model.py; sed -n 1922,2164p train_current_model.py | grep -n \"cache\\|stage\\|refit\\|test\" | head -40"
}
### Tool output · 2026-09-26T06:28:34.962Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.5s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.3s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.6s
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
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
def build_target_weights(
public: Path,
stage: str,
prepared: SequenceData,
training_counts: np.ndarray,
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,
)
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
)
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
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):
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)
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"))
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} "
f"max={weights.max():.3f}"
)
return weights
4: "--stage", choices=("validation", "submission"), default="validation"
8: parser.add_argument("--prepared-cache", type=Path)
136: args.stage,
138: args.prepared_cache.resolve() if args.prepared_cache else None,
150: "stage": args.stage,
174: args.stage,
194: "stage": args.stage,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14956135 tokens left"
}
## Step 8 · agent · 2026-09-26T06:28:50.784Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:28:51.331Z
{
"command": "cd /app/solution; tail -5 base_val.log; sed -n 1364,1400p train_current_model.py; sed -n 2040,2164p train_current_model.py"
}
### Tool output · 2026-09-26T06:28:51.417Z
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
def evaluate_validation_bce(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
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)
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,
)
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(
{
"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()
),
"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,
)
)
return 0
model, training_history = train_model(
public,
args.stage,
prepared,
config,
device,
args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
)
ranking, embedding_diagnostics = rank_candidates(
model,
prepared,
eligibility,
config,
device,
args.ranking_batch_size,
)
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),
"public_inputs_only": True,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": (
"every-adjacent-target-within-recent-chronological-window"
),
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": False,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": (
"24-uniform-plus-8-propensity-corrected-inbatch"
),
"false_negative_filter": config.negative_filter_scope,
"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()
),
"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,
)
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
### Harness context
{
"type": "total_tokens_reminder",
"text": "14952906 tokens left"
}
## Step 9 · agent · 2026-09-26T06:29:06.914Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:07.709Z
{
"command": "cd /app/solution; tail -3 base_val.log; grep -n \"score_target_microbatches(\" train_current_model.py; sed -n 1400,1487p train_current_model.py | grep -n \"draw_training\\|score_target\\|generator\" "
}
### Tool output · 2026-09-26T06:29:07.799Z
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
1111:def score_target_microbatches(
1306: statistics, sequence_gradient = score_target_microbatches(
1461: statistics, _ = score_target_microbatches(
50: negatives, negative_weights = draw_training_negatives(
59: generator,
62: statistics, _ = score_target_microbatches(
### Harness context
{
"type": "total_tokens_reminder",
"text": "14949113 tokens left"
}
## Step 10 · agent · 2026-09-26T06:29:10.209Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:25.270Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(''' early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n''',''' early_stopping_min_delta: float = 1.0e-4\n objective: str = \"sampled-softmax\"\n shared_negatives: int = 4096\n shared_popular_fraction: float = 0.5\n auxiliary_bce_weight: float = 0.0\n seed: int = 29\n''',1)\n# pool class after corrected_negative_loss\nanchor='''def retained_target_window_starts('''\npool='''class SharedNegativePool:\n \"\"\"Batch-shared negatives from a uniform/target-popularity mixture.\n\n Every scored target in a block is contrasted with the same K items, so the\n softmax denominator sees thousands of negatives for one extra matmul. The\n logits of pool items are corrected by log(K * q(item)) so the sampled\n denominator is an unbiased estimate of the full-catalog partition.\n \"\"\"\n\n def __init__(self, counts: np.ndarray, config: Config, seed: int) -> None:\n n_items = len(counts)\n popular = counts / max(float(counts.sum()), 1.0)\n mixture = (\n config.shared_popular_fraction * popular\n + (1.0 - config.shared_popular_fraction) / n_items\n )\n mixture = mixture / mixture.sum()\n self.size = int(config.shared_negatives)\n self.probabilities = torch.from_numpy(mixture.astype(np.float64))\n self.log_expected = torch.log(\n torch.from_numpy((self.size * mixture).astype(np.float32))\n )\n self.generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n def sample(self, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:\n items = torch.multinomial(\n self.probabilities, self.size, replacement=True,\n generator=self.generator,\n )\n return items.to(device), self.log_expected[items].to(device)\n\n\nSHARED_POOL: SharedNegativePool | None = None\n\n\ndef sampled_softmax_loss(\n query_vectors: torch.Tensor,\n positive_logits: torch.Tensor,\n positive_items: torch.Tensor,\n model: base.SequentialTwoTower,\n pool_items: torch.Tensor,\n pool_log_expected: torch.Tensor,\n pool_vectors: torch.Tensor | None = None,\n) -> torch.Tensor:\n if pool_vectors is None:\n pool_vectors = model.encode_items(pool_items)\n pool_logits = query_vectors @ pool_vectors.T - pool_log_expected[None, :]\n pool_logits = pool_logits.masked_fill(\n pool_items[None, :].eq(positive_items[:, None]), -torch.inf\n )\n logits = torch.cat([positive_logits[:, None], pool_logits], dim=1)\n return torch.logsumexp(logits, dim=1) - positive_logits\n\n\n'''\ns=s.replace(anchor,pool+anchor,1)\nold=''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()'''\nnew=''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.objective == \"sampled-softmax\":\n if SHARED_POOL is None:\n raise RuntimeError(\"shared negative pool was not built\")\n pool_items, pool_log_expected = SHARED_POOL.sample(device)\n per_target = sampled_softmax_loss(\n query_vectors,\n positive_logits,\n positive_items[begin:end],\n model,\n pool_items,\n pool_log_expected,\n )\n if config.auxiliary_bce_weight:\n per_target = per_target + config.auxiliary_bce_weight * (\n positive_loss + negative_loss\n ) / 2.0\n else:\n per_target = (positive_loss + negative_loss) / 2.0\n loss_sum = (selected_weights * per_target).sum()'''\nassert old in s\ns=s.replace(old,new,1)\nold=''' target_weights = build_target_weights(\n public,\n stage,'''\nnew=''' global SHARED_POOL\n SHARED_POOL = SharedNegativePool(\n target_item_counts(prepared, partition.window_starts, training_counts),\n config,\n config.seed + 30_000,\n )\n target_weights = build_target_weights(\n public,\n stage,'''\nassert old in s\ns=s.replace(old,new,1)\nold='''def build_known_positive_filter('''\nnew='''def target_item_counts(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n begin = int(prepared.offsets[user_row]) + int(target_starts[user_row]) + 1\n counts += np.bincount(\n prepared.sequence_items[begin : begin + int(count)].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\ndef build_known_positive_filter('''\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"args\\.\\(seed\\|early_stopping_min_delta\\)\\|add_argument(\\\"--seed\\\"\" train_current_model.py"
}
### Tool output · 2026-09-26T06:29:25.345Z
Traceback (most recent call last):
File "", line 142, in
PermissionError: [Errno 13] Permission denied: 'train_current_model.py'
2002: parser.add_argument("--seed", type=int, default=Config.seed)
2014: or args.early_stopping_min_delta < 0
2044: early_stopping_min_delta=args.early_stopping_min_delta,
2045: seed=args.seed,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14946601 tokens left"
}
## Step 11 · agent · 2026-09-26T06:29:28.704Z
### Bash · 2026-09-26T06:29:28.704Z
{
"command": "cd /app/solution; chmod u+w *.py; ls -l"
}
### Tool output · 2026-09-26T06:29:28.757Z
total 100
-rw-r--r-- 1 agent agent 1159 Sep 26 06:28 base_val.log
-rw-r--r-- 1 agent agent 80058 Sep 26 06:27 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 26 06:27 two_tower_model.py
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14946353 tokens left"
}
## Step 12 · agent · 2026-09-26T06:29:32.553Z
### Thinking unavailable — no text recorded
### Recorded reasoning
I fixed the read-only permissions on the copied files. Now applying the sampled-softmax patch, replacing BCE against 32 negatives with scoring against a shared pool of 4,096 negatives plus a logQ correction.
### Bash · 2026-09-26T06:29:44.628Z
{
"command": "cd /app/solution; cat > /app/solution/patch1.py <<'EOF'\np='/app/solution/train_current_model.py'\ns=open(p).read()\ns=s.replace(''' early_stopping_min_delta: float = 1.0e-4\n seed: int = 29\n''',''' early_stopping_min_delta: float = 1.0e-4\n objective: str = \"sampled-softmax\"\n shared_negatives: int = 4096\n shared_popular_fraction: float = 0.5\n auxiliary_bce_weight: float = 0.0\n seed: int = 29\n''',1)\nanchor='''def retained_target_window_starts('''\npool='''class SharedNegativePool:\n \"\"\"Batch-shared negatives from a uniform/target-popularity mixture.\n\n Every scored target in a block is contrasted with the same K items, so the\n softmax denominator sees thousands of negatives for one extra matmul. The\n logits of pool items are corrected by log(K * q(item)) so the sampled\n denominator is an unbiased estimate of the full-catalog partition.\n \"\"\"\n\n def __init__(self, counts: np.ndarray, config: Config, seed: int) -> None:\n n_items = len(counts)\n popular = counts / max(float(counts.sum()), 1.0)\n mixture = (\n config.shared_popular_fraction * popular\n + (1.0 - config.shared_popular_fraction) / n_items\n )\n mixture = mixture / mixture.sum()\n self.size = int(config.shared_negatives)\n self.probabilities = torch.from_numpy(mixture.astype(np.float64))\n self.log_expected = torch.log(\n torch.from_numpy((self.size * mixture).astype(np.float32))\n )\n self.generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n\n def sample(self, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:\n items = torch.multinomial(\n self.probabilities, self.size, replacement=True,\n generator=self.generator,\n )\n return items.to(device), self.log_expected[items].to(device)\n\n\nSHARED_POOL: SharedNegativePool | None = None\n\n\ndef sampled_softmax_loss(\n query_vectors: torch.Tensor,\n positive_logits: torch.Tensor,\n positive_items: torch.Tensor,\n model: base.SequentialTwoTower,\n pool_items: torch.Tensor,\n pool_log_expected: torch.Tensor,\n) -> torch.Tensor:\n pool_vectors = model.encode_items(pool_items)\n pool_logits = query_vectors @ pool_vectors.T - pool_log_expected[None, :]\n pool_logits = pool_logits.masked_fill(\n pool_items[None, :].eq(positive_items[:, None]), -torch.inf\n )\n logits = torch.cat([positive_logits[:, None], pool_logits], dim=1)\n return torch.logsumexp(logits, dim=1) - positive_logits\n\n\n'''\ns=s.replace(anchor,pool+anchor,1)\nold=''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n loss_sum = (\n selected_weights * (positive_loss + negative_loss) / 2.0\n ).sum()'''\nnew=''' positive_loss = F.softplus(-positive_logits)\n negative_loss = corrected_negative_loss(\n negative_logits, negative_weights[begin:end], config\n )\n selected_weights = torch.from_numpy(\n sample_weights[begin:end].astype(np.float32, copy=False)\n ).to(device)\n if config.objective == \"sampled-softmax\":\n if SHARED_POOL is None:\n raise RuntimeError(\"shared negative pool was not built\")\n pool_items, pool_log_expected = SHARED_POOL.sample(device)\n per_target = sampled_softmax_loss(\n query_vectors,\n positive_logits,\n positive_items[begin:end],\n model,\n pool_items,\n pool_log_expected,\n )\n if config.auxiliary_bce_weight:\n per_target = per_target + config.auxiliary_bce_weight * (\n positive_loss + negative_loss\n ) / 2.0\n else:\n per_target = (positive_loss + negative_loss) / 2.0\n loss_sum = (selected_weights * per_target).sum()'''\nassert old in s\ns=s.replace(old,new,1)\nold=''' target_weights = build_target_weights(\n public,\n stage,'''\nnew=''' global SHARED_POOL\n SHARED_POOL = SharedNegativePool(\n target_item_counts(prepared, partition.window_starts, training_counts),\n config,\n config.seed + 30_000,\n )\n target_weights = build_target_weights(\n public,\n stage,'''\nassert old in s\ns=s.replace(old,new,1)\nold='''def build_known_positive_filter('''\nnew='''def target_item_counts(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> np.ndarray:\n counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n for user_row, count in enumerate(target_counts):\n if not count:\n continue\n begin = int(prepared.offsets[user_row]) + int(target_starts[user_row]) + 1\n counts += np.bincount(\n prepared.sequence_items[begin : begin + int(count)].astype(np.int64),\n minlength=len(prepared.item_ids),\n )\n return counts\n\n\ndef build_known_positive_filter('''\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\npython patch1.py && tail -4 base_val.log"
}
### Tool output · 2026-09-26T06:29:44.707Z
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
### Harness context
{
"type": "total_tokens_reminder",
"text": "14943916 tokens left"
}
## Step 13 · agent · 2026-09-26T06:29:47.446Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:50.826Z
{
"command": "cd /app/solution; python - <<'EOF'\np='/app/solution/train_current_model.py'\ns=open(p).read()\nold=''' parser.add_argument(\"--seed\", type=int, default=Config.seed)'''\nnew=''' parser.add_argument(\"--seed\", type=int, default=Config.seed)\n parser.add_argument(\n \"--objective\",\n choices=(\"sampled-softmax\", \"bce\"),\n default=Config.objective,\n )\n parser.add_argument(\n \"--shared-negatives\", type=int, default=Config.shared_negatives\n )\n parser.add_argument(\n \"--shared-popular-fraction\",\n type=float,\n default=Config.shared_popular_fraction,\n )\n parser.add_argument(\n \"--auxiliary-bce-weight\",\n type=float,\n default=Config.auxiliary_bce_weight,\n )'''\ns=s.replace(old,new,1)\nold=''' seed=args.seed,\n )'''\nnew=''' objective=args.objective,\n shared_negatives=args.shared_negatives,\n shared_popular_fraction=args.shared_popular_fraction,\n auxiliary_bce_weight=args.auxiliary_bce_weight,\n seed=args.seed,\n )'''\nassert old in s\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\nsed -n 471,535p train_current_model.py; top -bn1 | head -12; tail -2 base_val.log"
}
### Tool output · 2026-09-26T06:29:51.114Z
)
return prepared, eligibility
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",
}
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__,
)
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 = {
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
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"
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:
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
top - 06:29:51 up 13 min, 0 user, load average: 4.76, 2.59, 1.21
Tasks: 11 total, 2 running, 9 sleeping, 0 stopped, 0 zombie
%Cpu(s):100.0 us, 0.0 sy, 0.0 ni, 0.0 id, 0.0 wa, 0.0 hi, 0.0 si, 0.0 st
MiB Mem : 15614.2 total, 875.6 free, 5152.9 used, 9962.3 buff/cache
MiB Swap: 0.0 total, 0.0 free, 0.0 used. 10461.3 avail Mem
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
8137 agent 20 0 6279800 4.0g 149680 R 612.5 26.2 10:19.36 python
1 root 20 0 2592 1716 1612 S 0.0 0.0 0:00.02 sh
7 root 20 0 2920 1668 1564 S 0.0 0.0 0:00.00 sleep
8050 agent 20 0 4480 3392 3044 S 0.0 0.0 0:00.01 bash
8058 agent 20 0 5600060 254776 123576 S 0.0 1.6 0:02.43 claude
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
### Harness context
{
"type": "total_tokens_reminder",
"text": "14943079 tokens left"
}
## Step 14 · agent · 2026-09-26T06:29:53.768Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:54.110Z
{
"command": "cd /app/solution; sleep 90; tail -3 base_val.log"
}
### Tool output · 2026-09-26T06:31:24.139Z
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
### Harness context
{
"type": "total_tokens_reminder",
"text": "14941417 tokens left"
}
## Step 15 · agent · 2026-09-26T06:31:27.469Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:31:27.809Z
{
"command": "cd /app/solution; sleep 100; tail -3 base_val.log"
}
### Tool output · 2026-09-26T06:33:07.859Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=206.8s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=220.0 validation_seconds=18.5
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
### Harness context
{
"type": "total_tokens_reminder",
"text": "14941022 tokens left"
}
## Step 16 · agent · 2026-09-26T06:33:10.497Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:33:10.786Z
{
"command": "cd /app/solution; sleep 15; tail -2 base_val.log; ls; python /task/evaluate_public.py production_validation.parquet 2>&1 | tail -5"
}
### Tool output · 2026-09-26T06:33:29.714Z
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=220.0 validation_seconds=18.5
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
base_val.log
patch1.py
train_current_model.py
two_tower_model.py
^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle
handle = open(handle, ioargs.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'production_validation.parquet'
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940634 tokens left"
}
## Step 17 · agent · 2026-09-26T06:33:31.909Z
### Bash · 2026-09-26T06:33:31.909Z
{
"command": "cd /app/solution; sleep 20; tail -2 base_val.log; ls; python /task/evaluate_public.py production_validation.parquet 2>&1 | tail -8"
}
### Tool output · 2026-09-26T06:33:55.368Z
user 33m5.782s
sys 1m40.582s
base_val.log
patch1.py
production_validation.parquet
train_current_model.py
two_tower_model.py
{
"model": "production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940177 tokens left"
}
## Step 18 · agent · 2026-09-26T06:33:59.250Z
### Recorded reasoning
The production baseline scored NDCG@10 = 0.02128 (4,263 users, ~5.5 min runtime). Now running the sampled-softmax validation experiment, which will also cache prepared data to speed up future runs.
### Bash · 2026-09-26T06:33:59.252Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_val.npz --output /app/solution/ssm_val.parquet > ssm_val.json) > ssm_val.log 2>&1 &\nsleep 5; echo started"
}
### Tool output · 2026-09-26T06:34:04.268Z
started
### Harness context
{
"type": "total_tokens_reminder",
"text": "14939788 tokens left"
}
## Step 19 · agent · 2026-09-26T06:34:10.119Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:34:11.132Z
{
"command": "cd /app/solution; sleep 60; python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/x.parquet --prepare-only > sub_prep.json 2> sub_prep.log; tail -3 sub_prep.log; tail -3 ssm_val.log"
}
### Tool output · 2026-09-26T06:36:11.257Z
Command did not complete within its 120s timeout and was moved to the background (ID: bgkbliyts). Output is being written to: /tmp/claude-1000/-app/3c08516b-1c27-468e-91e9-0ad44fd55a78/tasks/bgkbliyts.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /app/solution; directory changes made by the backgrounded command do not apply to subsequent commands.
### Harness context
{
"type": "total_tokens_reminder",
"text": "14939238 tokens left"
}
## Step 20 · agent · 2026-09-26T06:36:13.109Z
### Bash · 2026-09-26T06:36:13.109Z
{
"command": "cd /app/solution; sleep 45; tail -2 sub_prep.log; tail -3 ssm_val.log"
}
### Tool output · 2026-09-26T06:36:58.170Z
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_sub.npz
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
### Harness context
{
"type": "queued_command",
"prompt": "\nbgkbliyts\ntoolu_01X5uQnQNhdk4Zz8qdMFfZFa\n/tmp/claude-1000/-app/3c08516b-1c27-468e-91e9-0ad44fd55a78/tasks/bgkbliyts.output\ncompleted\nBackground command \"cd /app/solution; sleep 60; python train_current_model.py --stage submission --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/x.parquet --prepare-only > sub_prep.json 2> sub_prep.log; tail -3 sub_prep.log; tail -3 ssm_val.log\" completed (exit code 0)\n",
"source_uuid": "fd52ef79-642e-4640-87d8-a5e1350864fd",
"commandMode": "task-notification",
"timestamp": "2026-09-26T06:36:34.079Z"
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14938966 tokens left"
}
## Step 21 · agent · 2026-09-26T06:37:02.178Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:37:02.555Z
{
"command": "cd /app/solution; sleep 110; tail -3 ssm_val.log"
}
### Tool output · 2026-09-26T06:38:52.599Z
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
### Harness context
{
"type": "total_tokens_reminder",
"text": "14938007 tokens left"
}
## Step 22 · agent · 2026-09-26T06:38:55.030Z
### Bash · 2026-09-26T06:38:55.030Z
{
"command": "cd /app/solution; sleep 90; tail -3 ssm_val.log"
}
### Tool output · 2026-09-26T06:40:25.049Z
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=9.584207 running_bce=9.443538 elapsed=305.1s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937808 tokens left"
}
## Step 23 · agent · 2026-09-26T06:40:27.129Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:40:27.601Z
{
"command": "cd /app/solution; sleep 50; tail -2 ssm_val.log; ls ssm_val.parquet && python /task/evaluate_public.py ssm_val.parquet 2>&1 | grep ndcg"
}
### Tool error · 2026-09-26T06:41:17.666Z
Exit code 2
epoch=1/1 train_loss=9.43727 validation_loss=9.19518 pair_accuracy=0.748 train_targets=1,052,953 validation_targets=130,103 train_seconds=321.0 validation_seconds=21.9
restored best validation-BCE checkpoint: epoch=1 loss=9.195176
ls: cannot access 'ssm_val.parquet': No such file or directory
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937504 tokens left"
}
## Step 24 · agent · 2026-09-26T06:41:19.573Z
### Bash · 2026-09-26T06:41:19.573Z
{
"command": "cd /app/solution; sleep 20; python /task/evaluate_public.py ssm_val.parquet 2>&1 | grep ndcg"
}
### Tool output · 2026-09-26T06:41:43.125Z
(Bash completed with no output)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937231 tokens left"
}
## Step 25 · agent · 2026-09-26T06:41:45.234Z
### Bash · 2026-09-26T06:41:45.234Z
{
"command": "cd /app/solution; sleep 10; tail -3 ssm_val.log; python /task/evaluate_public.py ssm_val.parquet 2>&1 | tail -4"
}
### Tool output · 2026-09-26T06:41:58.410Z
epoch=1/1 batches=250 targets=987,417 batch_bce=9.584207 running_bce=9.443538 elapsed=305.1s
epoch=1/1 train_loss=9.43727 validation_loss=9.19518 pair_accuracy=0.748 train_targets=1,052,953 validation_targets=130,103 train_seconds=321.0 validation_seconds=21.9
restored best validation-BCE checkpoint: epoch=1 loss=9.195176
File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle
handle = open(handle, ioargs.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'ssm_val.parquet'
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937087 tokens left"
}
## Step 26 · agent · 2026-09-26T06:42:02.092Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:42:02.595Z
{
"command": "cd /app/solution; sleep 25; python /task/evaluate_public.py ssm_val.parquet 2>&1 | tail -4"
}
### Tool output · 2026-09-26T06:42:30.977Z
"quality_ndcg@10": 0.019126,
"quality_users": 4263
}
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14936578 tokens left"
}
## Step 27 · agent · 2026-09-26T06:42:59.288Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The sampled-softmax variant performed worse than the baseline (NDCG@10 0.01913 vs 0.02128), so I won't use it. I'm now refitting the production config on train+val to secure a valid submission, while also testing an alternative that gives popular in-batch negatives more sampling slots.
### Bash · 2026-09-26T06:42:59.298Z
{
"command": "cd /app/solution; grep -n '\"--uniform-negatives\\|\"--inbatch-negatives\\|\"--inbatch-correction-power\\|\"--negative-filter' train_current_model.py; \n(python train_current_model.py --stage submission --objective bce --public-dir /task --prepared-cache /app/solution/cache_sub.npz --threads 4 --output /app/solution/submission.parquet > sub_base.json) > sub_base.log 2>&1 &\nsleep 1; echo ok"
}
### Tool output · 2026-09-26T06:43:00.317Z
2039: "--negative-filter-scope",
ok
### Harness context
{
"type": "total_tokens_reminder",
"text": "14933878 tokens left"
}
## Step 28 · agent · 2026-09-26T06:43:02.608Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:43:06.082Z
{
"command": "cd /app/solution; python - <<'EOF'\np='/app/solution/train_current_model.py'\ns=open(p).read()\nold=''' parser.add_argument(\n \"--shared-negatives\",'''\nnew=''' parser.add_argument(\n \"--uniform-negatives\", type=int, default=Config.uniform_negatives\n )\n parser.add_argument(\n \"--inbatch-negatives\", type=int, default=Config.inbatch_negatives\n )\n parser.add_argument(\n \"--inbatch-correction-power\",\n type=float,\n default=Config.inbatch_correction_power,\n )\n parser.add_argument(\n \"--shared-negatives\",'''\ns=s.replace(old,new,1)\nold=''' objective=args.objective,'''\nnew=''' uniform_negatives=args.uniform_negatives,\n inbatch_negatives=args.inbatch_negatives,\n inbatch_correction_power=args.inbatch_correction_power,\n objective=args.objective,'''\ns=s.replace(old,new,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"uniform_negatives=\\|inbatch_negatives=\\|inbatch_correction_power=\" train_current_model.py\n(python train_current_model.py --stage validation --objective bce --uniform-negatives 16 --inbatch-negatives 16 --threads 4 --public-dir /task --prepared-cache /app/solution/cache_val.npz --output /app/solution/hn_val.parquet > hn_val.json) > hn_val.log 2>&1 &\nsleep 30; tail -2 hn_val.log sub_base.log"
}
### Tool error · 2026-09-26T06:43:36.130Z
Exit code 1
169: uniform_negatives=config.uniform_negatives,
170: inbatch_negatives=config.inbatch_negatives,
171: inbatch_correction_power=config.inbatch_correction_power,
2169: uniform_negatives=args.uniform_negatives,
2170: inbatch_negatives=args.inbatch_negatives,
2171: inbatch_correction_power=args.inbatch_correction_power,
tail: option used in invalid context -- 2
### Harness context
{
"type": "total_tokens_reminder",
"text": "14933173 tokens left"
}
## Step 29 · agent · 2026-09-26T06:43:38.272Z
### Bash · 2026-09-26T06:43:38.272Z
{
"command": "cd /app/solution; for f in hn_val.log sub_base.log; do tail -n 2 $f; done; ps aux | grep train_current | grep -v grep | wc -l"
}
### Tool output · 2026-09-26T06:43:38.317Z
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
2
### Harness context
{
"type": "total_tokens_reminder",
"text": "14932865 tokens left"
}
## Step 30 · agent · 2026-09-26T06:43:40.324Z
### Thinking unavailable — no text recorded
Both runs are going. While they train, I'm writing a submission-format checker and the README.
### Bash · 2026-09-26T06:43:44.964Z
{
"command": "cd /app/solution; cat > check_submission.py <<'EOF'\n\"\"\"Validate a ranking against the task's submission contract.\"\"\"\nimport sys\nimport numpy as np\nimport pandas as pd\n\npath = sys.argv[1]\nsub = pd.read_parquet(path) if path.endswith(\".parquet\") else pd.read_csv(path)\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\ncand = np.load(\"/task/data/test_eligible_candidates.npz\")\nitem_ids = cand[\"item_ids\"]\nassert np.isfinite(sub[[\"uid\", \"item_id\", \"rank\"]].to_numpy(dtype=float)).all()\nassert set(sub.uid.astype(int)) == targets, \"user set mismatch\"\ng = sub.groupby(\"uid\")\nassert (g.size() == 100).all() and (g.item_id.nunique() == 100).all()\nassert (g[\"rank\"].apply(lambda r: sorted(r) == list(range(1, 101)))).all()\ncol = pd.Index(item_ids).get_indexer(sub.item_id.to_numpy())\nassert (col >= 0).all(), \"item outside catalog\"\nrow_of = {int(u): r for r, u in enumerate(cand[\"uids\"])}\nrows = sub.uid.map(row_of).to_numpy()\nmasks = np.unpackbits(cand[\"packed_eligible\"], axis=1, count=len(item_ids), bitorder=\"little\")\nassert masks[rows, col].all(), \"ineligible item\"\nprint(f\"valid: users={len(targets)} rows={len(sub)}\")\nEOF\npython -c \"import pandas as pd; print(pd.read_parquet('/task/data/target_users.parquet').shape)\""
}
### Tool output · 2026-09-26T06:43:47.483Z
(8456, 3)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14932001 tokens left"
}
## Step 31 · agent · 2026-09-26T06:43:49.453Z
### Bash · 2026-09-26T06:43:49.453Z
{
"command": "cd /app/solution; sleep 150; for f in hn_val.log sub_base.log; do tail -n 1 $f; done"
}
### Tool output · 2026-09-26T06:45:49.585Z
Command did not complete within its 120s timeout and was moved to the background (ID: byfek31ta). Output is being written to: /tmp/claude-1000/-app/3c08516b-1c27-468e-91e9-0ad44fd55a78/tasks/byfek31ta.output. You will be notified when it completes. To check interim output, use Read on that file path.
Session cwd remains /app/solution; directory changes made by the backgrounded command do not apply to subsequent commands.
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931877 tokens left"
}
## Step 32 · agent · 2026-09-26T06:45:51.697Z
### Bash · 2026-09-26T06:45:51.697Z
{
"command": "cd /app/solution; sleep 100; for f in hn_val.log sub_base.log; do tail -n 1 $f; done"
}
### Tool output · 2026-09-26T06:47:31.846Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.551351 running_bce=0.555503 elapsed=253.9s
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
### Harness context
{
"type": "queued_command",
"prompt": "\nbyfek31ta\ntoolu_01FiBnWux2AzDKvcEAGyYesL\n/tmp/claude-1000/-app/3c08516b-1c27-468e-91e9-0ad44fd55a78/tasks/byfek31ta.output\ncompleted\nBackground command \"cd /app/solution; sleep 150; for f in hn_val.log sub_base.log; do tail -n 1 $f; done\" completed (exit code 0)\n",
"source_uuid": "ac4f8a55-b3be-4aa8-b06a-f91c4ee4c03d",
"commandMode": "task-notification",
"timestamp": "2026-09-26T06:46:19.617Z"
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931604 tokens left"
}
## Step 33 · agent · 2026-09-26T06:47:34.150Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:47:34.381Z
{
"command": "cd /app/solution; sleep 75; for f in hn_val.log sub_base.log; do tail -n 1 $f; done; ls *.parquet"
}
### Tool output · 2026-09-26T06:48:49.432Z
restored best validation-BCE checkpoint: epoch=1 loss=0.511943
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
production_validation.parquet
ssm_val.parquet
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930978 tokens left"
}
## Step 34 · agent · 2026-09-26T06:48:51.630Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:48:52.167Z
{
"command": "cd /app/solution; sleep 45; ls *.parquet; tail -n 1 sub_base.log; [ -f hn_val.parquet ] && python /task/evaluate_public.py hn_val.parquet 2>&1 | grep ndcg"
}
### Tool output · 2026-09-26T06:49:40.624Z
hn_val.parquet
production_validation.parquet
ssm_val.parquet
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
"quality_ndcg@10": 0.019578,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930692 tokens left"
}
## Step 35 · agent · 2026-09-26T06:49:50.673Z
### Thinking unavailable — no text recorded
### Recorded reasoning
Adding popular negatives also hurt performance (NDCG@10 0.01958), confirming that popularity-heavy negatives strip away the popularity prior the model relies on. I'm now testing the opposite setup—32 uniform negatives, 0 in-batch—running its validation and refit concurrently while the production refit finishes as the default submission.
### Bash · 2026-09-26T06:49:50.686Z
{
"command": "cd /app/solution; \n(python train_current_model.py --stage validation --objective bce --uniform-negatives 32 --inbatch-negatives 0 --threads 3 --public-dir /task --prepared-cache /app/solution/cache_val.npz --output /app/solution/uni_val.parquet > uni_val.json) > uni_val.log 2>&1 &\n(python train_current_model.py --stage submission --objective bce --uniform-negatives 32 --inbatch-negatives 0 --threads 3 --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/uni_sub.parquet > uni_sub.json) > uni_sub.log 2>&1 &\nsleep 2; echo ok"
}
### Tool output · 2026-09-26T06:49:52.710Z
ok
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929726 tokens left"
}
## Step 36 · agent · 2026-09-26T06:49:55.552Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:49:56.268Z
{
"command": "cd /app/solution; sed -i 's/ objective: str = \"sampled-softmax\"/ objective: str = \"bce\"/' train_current_model.py; grep -n 'objective: str' train_current_model.py; sleep 60; tail -n 1 sub_base.log"
}
### Tool output · 2026-09-26T06:50:56.339Z
72: objective: str = "bce"
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929398 tokens left"
}
## Step 37 · agent · 2026-09-26T06:50:59.249Z
### Bash · 2026-09-26T06:50:59.249Z
{
"command": "cd /app/solution; sleep 100; tail -n 1 sub_base.log uni_val.log 2>/dev/null | cat; ls *.parquet"
}
### Tool output · 2026-09-26T06:52:39.418Z
==> sub_base.log <==
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
==> uni_val.log <==
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
hn_val.parquet
production_validation.parquet
ssm_val.parquet
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929220 tokens left"
}
## Step 38 · agent · 2026-09-26T06:52:43.292Z
### Thinking unavailable — no text recorded
The production refit is being slowed down by CPU contention and hasn't written a submission yet. To make sure a valid artifact exists before the deadline, I'm stopping the two uniform-negative runs.
### Bash · 2026-09-26T06:52:44.055Z
{
"command": "cd /app/solution; pkill -f \"uniform-negatives 32\"; sleep 3; ps aux | grep train_current | grep -v grep | awk '{print $2, $10, $NF}'; tail -n 1 sub_base.log"
}
### Tool error · 2026-09-26T06:52:44.137Z
Exit code 144
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928760 tokens left"
}
## Step 39 · agent · 2026-09-26T06:52:46.768Z
### Bash · 2026-09-26T06:52:46.768Z
{
"command": "cd /app/solution; ps aux | grep \"train_current\" | grep -v grep | cut -c1-200; tail -n 1 sub_base.log"
}
### Tool output · 2026-09-26T06:52:46.806Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928624 tokens left"
}
## Step 40 · agent · 2026-09-26T06:52:49.301Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:52:49.697Z
{
"command": "cd /app/solution; tail -n 5 sub_base.log; cat sub_base.json | head; ls -la"
}
### Tool output · 2026-09-26T06:52:49.719Z
loaded full-sequence cache: tokens=20,712,971 targets=20,703,818
chronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
total 1066236
drwxr-xr-x 1 agent agent 4096 Sep 26 06:49 .
drwxr-xr-x 1 agent agent 4096 Sep 26 06:22 ..
-rw-r--r-- 1 agent agent 4738 Sep 26 06:33 base_val.log
-rw-r--r-- 1 agent agent 552686232 Sep 26 06:36 cache_sub.npz
-rw-r--r-- 1 agent agent 535225808 Sep 26 06:34 cache_val.npz
-rw-r--r-- 1 agent agent 1097 Sep 26 06:43 check_submission.py
-rw-r--r-- 1 agent agent 3087 Sep 26 06:49 hn_val.json
-rw-r--r-- 1 agent agent 960 Sep 26 06:49 hn_val.log
-rw-r--r-- 1 agent agent 1249928 Sep 26 06:49 hn_val.parquet
-rw-r--r-- 1 agent agent 5206 Sep 26 06:29 patch1.py
-rw-r--r-- 1 agent agent 1244226 Sep 26 06:33 production_validation.parquet
-rw-r--r-- 1 agent agent 3103 Sep 26 06:42 ssm_val.json
-rw-r--r-- 1 agent agent 1824 Sep 26 06:42 ssm_val.log
-rw-r--r-- 1 agent agent 1226488 Sep 26 06:42 ssm_val.parquet
-rw-r--r-- 1 agent agent 0 Sep 26 06:42 sub_base.json
-rw-r--r-- 1 agent agent 411 Sep 26 06:43 sub_base.log
-rw-r--r-- 1 agent agent 1413 Sep 26 06:36 sub_prep.json
-rw-r--r-- 1 agent agent 884 Sep 26 06:36 sub_prep.log
-rw-r--r-- 1 agent agent 84924 Sep 26 06:49 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 26 06:27 two_tower_model.py
-rw-r--r-- 1 agent agent 0 Sep 26 06:49 uni_sub.json
-rw-r--r-- 1 agent agent 411 Sep 26 06:50 uni_sub.log
-rw-r--r-- 1 agent agent 0 Sep 26 06:49 uni_val.json
-rw-r--r-- 1 agent agent 411 Sep 26 06:50 uni_val.log
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928363 tokens left"
}
## Step 41 · agent · 2026-09-26T06:52:57.756Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The production refit had actually died silently around 06:43, before my kill command, likely because its parent shell exited—so no submission has been made yet, and only about 29 minutes have elapsed, not 47. I'm relaunching both the production refit and uniform-negative validation run using `setsid nohup` so they survive shell exits.
### Bash · 2026-09-26T06:52:57.952Z
{
"command": "cd /app/solution; \nsetsid nohup python train_current_model.py --stage submission --objective bce --threads 4 --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet > sub_base.json 2> sub_base.log < /dev/null &\nsetsid nohup python train_current_model.py --stage validation --objective bce --uniform-negatives 32 --inbatch-negatives 0 --threads 4 --public-dir /task --prepared-cache /app/solution/cache_val.npz --output /app/solution/uni_val.parquet > uni_val.json 2> uni_val.log < /dev/null &\nsleep 5; ps aux | grep train_current | grep -v grep | wc -l"
}
### Tool output · 2026-09-26T06:53:02.987Z
[1]- Done setsid nohup python train_current_model.py --stage submission --objective bce --threads 4 --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet > sub_base.json 2> sub_base.log < /dev/null
[2]+ Done setsid nohup python train_current_model.py --stage validation --objective bce --uniform-negatives 32 --inbatch-negatives 0 --threads 4 --public-dir /task --prepared-cache /app/solution/cache_val.npz --output /app/solution/uni_val.parquet > uni_val.json 2> uni_val.log < /dev/null
2
### Harness context
{
"type": "total_tokens_reminder",
"text": "14926481 tokens left"
}
## Step 42 · agent · 2026-09-26T06:53:05.083Z
### Thinking unavailable — no text recorded
Both are running (2 processes). While they train, I'm drafting the README.
### Bash · 2026-09-26T06:53:05.577Z
{
"command": "cd /app/solution; sleep 90; tail -n 1 sub_base.log; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T06:54:35.719Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 06:54:35 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14926062 tokens left"
}
## Step 43 · agent · 2026-09-26T06:54:37.628Z
### Bash · 2026-09-26T06:54:37.628Z
{
"command": "cd /app/solution; sleep 110; tail -n 1 sub_base.log; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T06:56:27.766Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 06:56:27 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14925845 tokens left"
}
## Step 44 · agent · 2026-09-26T06:56:29.791Z
### Bash · 2026-09-26T06:56:29.791Z
{
"command": "cd /app/solution; sleep 110; tail -n 1 sub_base.log; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T06:58:19.840Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 06:58:19 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14925628 tokens left"
}
## Step 45 · agent · 2026-09-26T06:58:21.786Z
### Bash · 2026-09-26T06:58:21.786Z
{
"command": "cd /app/solution; sleep 100; tail -n 1 sub_base.log; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T07:00:01.809Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 07:00:01 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14925411 tokens left"
}
## Step 46 · agent · 2026-09-26T07:00:04.251Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T07:00:04.584Z
{
"command": "top -bn1 | head -12"
}
### Tool output · 2026-09-26T07:00:04.769Z
top - 07:00:04 up 43 min, 0 user, load average: 0.40, 1.89, 3.10
Tasks: 8 total, 1 running, 7 sleeping, 0 stopped, 0 zombie
%Cpu(s): 0.0 us, 0.0 sy, 0.0 ni, 75.0 id, 25.0 wa, 0.0 hi, 0.0 si, 0.0 st
MiB Mem : 15614.2 total, 8693.3 free, 1053.3 used, 6221.0 buff/cache
MiB Swap: 0.0 total, 0.0 free, 0.0 used. 14560.9 avail Mem
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
8058 agent 20 0 5600060 196836 58372 S 6.7 1.2 0:10.84 claude
1 root 20 0 2592 1716 1612 S 0.0 0.0 0:00.02 sh
7 root 20 0 2920 1668 1564 S 0.0 0.0 0:00.00 sleep
8050 agent 20 0 4480 3392 3044 S 0.0 0.0 0:00.01 bash
8059 agent 20 0 2924 1660 1556 S 0.0 0.0 0:00.01 tee
### Harness context
{
"type": "total_tokens_reminder",
"text": "14925155 tokens left"
}
## Step 47 · agent · 2026-09-26T07:00:12.956Z
### Thinking unavailable — no text recorded
### Recorded reasoning
Both processes were likely killed by the OOM killer since each trainer used ~6GB of the 15GB RAM available. With about 22 minutes left, I'll run only the production-config refit alone with 8 threads to ensure a valid submission, skipping further experiments.
### Bash · 2026-09-26T07:00:12.967Z
{
"command": "cd /app/solution; rm -f uni_*; setsid nohup python train_current_model.py --stage submission --threads 8 --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet > sub_base.json 2> sub_base.log < /dev/null &\nsleep 20; tail -n 2 sub_base.log; free -g | head -2"
}
### Tool output · 2026-09-26T07:00:33.017Z
[1]+ Done setsid nohup python train_current_model.py --stage submission --threads 8 --public-dir /task --prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet > sub_base.json 2> sub_base.log < /dev/null
target weighting=organic-strong min=0.250 mean=0.576 max=1.000
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
total used free shared buff/cache available
Mem: 15 2 6 0 6 12
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14923983 tokens left"
}
## Step 48 · agent · 2026-09-26T07:00:35.644Z
### Thinking unavailable — no text recorded
The refit is running (~6 min). Writing the README in the meantime.
### Write · 2026-09-26T07:00:46.872Z
{
"file_path": "/app/solution/README.md",
"content": "# Made-for-You two-tower: negative-sampling investigation\n\n## Diagnosis of the production objective\n\n`train_current_model.py` trains a GRU user tower / MLP item tower with\nsample-weighted pointwise BCE. Each target gets only 32 negatives: 24 uniform\ncatalog draws plus 8 unique in-batch targets. The in-batch losses are\nre-weighted by `(1/N / p(item))^0.5`, clipped to [0.25, 4]. Any negative in the\nuser's full training-positive history is rejected and redrawn uniformly.\nObservations:\n\n* 32 negatives per target out of a 30k catalog is a very coarse, high-variance\n estimate of the partition function.\n* Uniform draws are mostly long-tail items, and after rejection many in-batch\n slots for heavy users also turn into uniform draws. So the \"hard\" popular\n negatives are rare and down-weighted.\n* Skipped (<50%) listens and dislikes are never used as negatives.\n\n## Experiments (public backtest, graded NDCG@10, 4,263 users)\n\n| Variant | NDCG@10 |\n| --- | --- |\n| Production (BCE, 24 uniform + 8 corrected in-batch) | **0.02128** |\n| Sampled softmax, 4,096 batch-shared negatives (50% popularity / 50% uniform mixture, logQ-corrected) | 0.01913 |\n| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n\nBoth changes that make negatives harder or more popularity-shaped *reduced*\npublic NDCG. Validation candidates exclude every item the user has already\nencountered, so the task is ranking novel tracks, and the popularity prior that\neasy uniform negatives let the model keep is a large share of its signal.\nPushing popular items down as negatives removes that prior faster than the model\ngains personalisation in one CPU epoch. The next test would have gone the other\nway (32 uniform, 0 in-batch). It was started but did not finish: two trainers at\nonce exceed the 15 GB RAM and were killed, and the remaining time went to a safe\nrefit.\n\n## Shipped artifact\n\nNo tested variant beat production, so the submission is the **production\nnegative-sampling configuration refit on `train_* + val_*`** (defaults of\n`train_current_model.py`). The implementation keeps the new, optional\nobjectives behind flags for further work:\n\n* `--objective sampled-softmax --shared-negatives K --shared-popular-fraction f`\n (`SharedNegativePool`, `sampled_softmax_loss`)\n* `--uniform-negatives`, `--inbatch-negatives`, `--inbatch-correction-power`\n\nThe serving contract (precomputed item vectors, one user vector, dot-product\ntop-100 over the eligible mask), the data protocol and the evaluation boundary\nare unchanged.\n\n## Commands\n\n```sh\n# public backtest\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --prepared-cache /app/solution/cache_val.npz --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n# final refit on train+val and hidden-test ranking\npython /app/solution/train_current_model.py --stage submission --public-dir /task \\\n --prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet\npython /app/solution/check_submission.py /app/solution/submission.parquet\n```\n\nRun only one trainer at a time: each uses about 6 GB of RAM.\n"
}
### Tool output · 2026-09-26T07:00:46.936Z
File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)
### Bash · 2026-09-26T07:00:47.231Z
{
"command": "cd /app/solution; sleep 60; tail -n 1 sub_base.log; date"
}
### Tool output · 2026-09-26T07:01:47.283Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
Sat Sep 26 07:01:47 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14922258 tokens left"
}
## Step 49 · agent · 2026-09-26T07:01:49.654Z
### Bash · 2026-09-26T07:01:49.654Z
{
"command": "cd /app/solution; sleep 110; tail -n 1 sub_base.log; ps aux | grep -c \"[t]rain_current\"; date"
}
### Tool output · 2026-09-26T07:03:39.729Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
1
Sat Sep 26 07:03:39 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921992 tokens left"
}
## Step 50 · agent · 2026-09-26T07:03:41.737Z
### Bash · 2026-09-26T07:03:41.737Z
{
"command": "cd /app/solution; sleep 110; tail -n 1 sub_base.log; ps aux | grep -c \"[t]rain_current\"; date"
}
### Tool output · 2026-09-26T07:05:31.815Z
restored best validation-BCE checkpoint: epoch=1 loss=0.497949
1
Sat Sep 26 07:05:31 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921807 tokens left"
}
## Step 51 · agent · 2026-09-26T07:05:33.845Z
### Bash · 2026-09-26T07:05:33.845Z
{
"command": "cd /app/solution; sleep 40; ps aux | grep -c \"[t]rain_current\"; ls -la submission.parquet && python check_submission.py submission.parquet"
}
### Tool output · 2026-09-26T07:06:15.903Z
0
-rw-r--r-- 1 agent agent 1212473 Sep 26 07:05 submission.parquet
valid: users=8456 rows=845600
### Harness context
{
"type": "total_tokens_reminder",
"text": "14921623 tokens left"
}
## Step 52 · agent · 2026-09-26T07:06:21.651Z
### Thinking unavailable — no text recorded
### Recorded reasoning
I've got a valid submission passing the checker (8,456 users × 100 rows). With ~15 minutes left, I'm now running a uniform-negative experiment in the background that will only overwrite the current submission if it beats the 0.02128 score.
### Bash · 2026-09-26T07:06:22.176Z
{
"command": "cd /app/solution; cat > uni_chain.sh <<'EOF'\ncd /app/solution\npython train_current_model.py --stage validation --uniform-negatives 32 --inbatch-negatives 0 --public-dir /task --prepared-cache cache_val.npz --output uni_val.parquet > uni_val.json 2> uni_val.log\npython /task/evaluate_public.py uni_val.parquet > uni_val_score.json 2>&1\npython train_current_model.py --stage submission --uniform-negatives 32 --inbatch-negatives 0 --public-dir /task --prepared-cache cache_sub.npz --output uni_sub.parquet > uni_sub.json 2> uni_sub.log\nEOF\nsetsid nohup bash uni_chain.sh > /dev/null 2>&1 < /dev/null & sleep 2; ps aux | grep -c \"[t]rain_current\""
}
### Tool output · 2026-09-26T07:06:24.200Z
[1]+ Done setsid nohup bash uni_chain.sh > /dev/null 2>&1 < /dev/null
3
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920935 tokens left"
}
## Step 53 · agent · 2026-09-26T07:06:26.165Z
### Bash · 2026-09-26T07:06:26.165Z
{
"command": "cd /app/solution; sleep 115; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T07:08:21.198Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 07:08:21 UTC 2026
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920786 tokens left"
}
## Step 54 · agent · 2026-09-26T07:08:23.278Z
The uniform-negative validation run is training (started 07:06; expected to finish around 07:12). The valid production refit stays in place as the submission.
### Bash · 2026-09-26T07:08:23.795Z
{
"command": "cd /app/solution; sleep 115; tail -n 1 uni_val.log; date"
}
### Tool output · 2026-09-26T07:10:18.844Z
restored best validation-BCE checkpoint: epoch=1 loss=0.476447
Sat Sep 26 07:10:18 UTC 2026
### Bash · 2026-09-26T07:08:24.351Z
{
"command": "cd /app/solution; sleep 100; tail -n 1 uni_val.log; cat uni_val_score.json 2>/dev/null | grep ndcg; date"
}
### Tool output · 2026-09-26T07:10:04.412Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.535239 running_bce=0.524238 elapsed=188.9s
Sat Sep 26 07:10:04 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920435 tokens left"
}
## Step 55 · agent · 2026-09-26T07:10:20.840Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T07:10:21.005Z
{
"command": "cd /app/solution; sleep 30; cat uni_val_score.json 2>/dev/null | grep ndcg; date"
}
### Tool output · 2026-09-26T07:10:51.064Z
Sat Sep 26 07:10:51 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14920122 tokens left"
}
## Step 56 · agent · 2026-09-26T07:10:54.174Z
### Bash · 2026-09-26T07:10:54.174Z
{
"command": "cd /app/solution; sleep 25; cat uni_val_score.json 2>/dev/null | grep ndcg; date"
}
### Tool output · 2026-09-26T07:11:19.235Z
Sat Sep 26 07:11:19 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919985 tokens left"
}
## Step 57 · agent · 2026-09-26T07:11:21.243Z
### Bash · 2026-09-26T07:11:21.243Z
{
"command": "cd /app/solution; sleep 20; cat uni_val_score.json 2>/dev/null | grep ndcg; date"
}
### Tool output · 2026-09-26T07:11:41.293Z
Sat Sep 26 07:11:41 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919848 tokens left"
}
## Step 58 · agent · 2026-09-26T07:11:43.253Z
### Bash · 2026-09-26T07:11:43.253Z
{
"command": "cd /app/solution; sleep 20; cat uni_val_score.json 2>/dev/null | grep ndcg; ls uni*; date"
}
### Tool output · 2026-09-26T07:12:03.327Z
"quality_ndcg@10": 0.01957,
uni_chain.sh
uni_sub.json
uni_sub.log
uni_val.json
uni_val.log
uni_val.parquet
uni_val_score.json
Sat Sep 26 07:12:03 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919705 tokens left"
}
## Step 59 · agent · 2026-09-26T07:12:07.312Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The pure-uniform variant also underperformed (0.01957), confirming the production negative mix is still the best configuration. I'll stop that refit, keep the production refit as the submission, and clean up.
### Bash · 2026-09-26T07:12:10.685Z
{
"command": "cd /app/solution; pkill -f \"uni_chain.sh\"; pkill -f \"uni_sub.parquet\"; sleep 2; ps aux | grep -c \"[t]rain_current\"; rm -f uni_sub.* uni_chain.sh patch1.py sub_prep.* cache_val.npz cache_sub.npz; python - <<'EOF'\np='/app/solution/README.md'\ns=open(p).read()\ns=s.replace('''| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n''','''| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n| BCE, 32 uniform + 0 in-batch (no popular negatives) | 0.01957 |\n''')\ns=s.replace('''The next test would have gone the other\nway (32 uniform, 0 in-batch). It was started but did not finish: two trainers at\nonce exceed the 15 GB RAM and were killed, and the remaining time went to a safe\nrefit.''','''Removing the popular in-batch negatives\nentirely (32 uniform) is equally bad, so the production 24/8 mix with its\ndampened propensity correction sits at a local optimum of this negative-mix\naxis; the gain has to come from elsewhere (e.g. more targets/epochs, or\nskip/dislike hard negatives, which were not tested within the budget).''')\ns=s.replace(\"--prepared-cache /app/solution/cache_val.npz\",\"--prepared-cache /app/solution/cache_val.npz # cache is rebuilt if absent\")\nopen(p,'w').write(s)\nEOF\npython check_submission.py submission.parquet; ls -la"
}
### Tool error · 2026-09-26T07:12:10.731Z
Exit code 144
### Harness context
{
"type": "silent_turn_reminder",
"text": "The user hasn't heard from you in a while. As you continue, keep them updated when there's something to tell — a finding, a change of plan."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14918846 tokens left"
}
## Step 60 · agent · 2026-09-26T07:12:14.865Z
### Thinking unavailable — no text recorded
My `pkill` pattern matched the tool shell's own command line and killed it, so I'm redoing the cleanup without it.
### Bash · 2026-09-26T07:12:15.105Z
{
"command": "cd /app/solution; ps aux | grep \"[t]rain_current\" | awk '{print $2}' | xargs -r kill; sleep 1; ps aux | grep -c \"[t]rain_current\"; ls"
}
### Tool output · 2026-09-26T07:12:16.149Z
0
README.md
base_val.log
cache_sub.npz
cache_val.npz
check_submission.py
hn_val.json
hn_val.log
hn_val.parquet
patch1.py
production_validation.parquet
ssm_val.json
ssm_val.log
ssm_val.parquet
sub_base.json
sub_base.log
sub_prep.json
sub_prep.log
submission.parquet
train_current_model.py
two_tower_model.py
uni_chain.sh
uni_sub.json
uni_sub.log
uni_val.json
uni_val.log
uni_val.parquet
uni_val_score.json
### Harness context
{
"type": "total_tokens_reminder",
"text": "14918572 tokens left"
}
## Step 61 · agent · 2026-09-26T07:12:21.070Z
### Bash · 2026-09-26T07:12:21.070Z
{
"command": "cd /app/solution; rm -f uni_sub.* uni_chain.sh patch1.py sub_prep.* cache_val.npz cache_sub.npz; python - <<'EOF'\np='/app/solution/README.md'\ns=open(p).read()\ns=s.replace('''| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n''','''| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n| BCE, 32 uniform + 0 in-batch (no popular negatives) | 0.01957 |\n''')\ns=s.replace('''The next test would have gone the other\nway (32 uniform, 0 in-batch). It was started but did not finish: two trainers at\nonce exceed the 15 GB RAM and were killed, and the remaining time went to a safe\nrefit.''','''Removing the popular in-batch negatives\nentirely (32 uniform) is equally bad. So on this negative-mix axis, the\nproduction 24/8 mix with its damped propensity correction is a local optimum,\nand any gain has to come from elsewhere: more targets or epochs, or skip/dislike\nhard negatives, which were not tested within the budget.''')\nopen(p,'w').write(s)\nEOF\ngrep -c \"0.01957\" README.md; python check_submission.py submission.parquet; ls -la submission.parquet"
}
### Tool output · 2026-09-26T07:12:23.148Z
1
valid: users=8456 rows=845600
-rw-r--r-- 1 agent agent 1212473 Sep 26 07:05 submission.parquet
### Harness context
{
"type": "edited_text_file",
"filename": "/app/solution/README.md",
"snippet": "18\t\n19\t## Experiments (public backtest, graded NDCG@10, 4,263 users)\n20\t\n21\t| Variant | NDCG@10 |\n22\t| --- | --- |\n23\t| Production (BCE, 24 uniform + 8 corrected in-batch) | **0.02128** |\n24\t| Sampled softmax, 4,096 batch-shared negatives (50% popularity / 50% uniform mixture, logQ-corrected) | 0.01913 |\n25\t| BCE, 16 uniform + 16 corrected in-batch (more popular negatives) | 0.01958 |\n26\t| BCE, 32 uniform + 0 in-batch (no popular negatives) | 0.01957 |\n27\t\n28\tBoth changes that make negatives harder or more popularity-shaped *reduced*\n29\tpublic NDCG. Validation candidates exclude every item the user has already\n30\tencountered, so the task is ranking novel tracks, and the popularity prior that\n31\teasy uniform negatives let the model keep is a large share of its signal.\n32\tPushing popular items down as negatives removes that prior faster than the model\n33\tgains personalisation in one CPU epoch. Removing the popular in-batch negatives\n34\tentirely (32 uniform) is equally bad. So on this negative-mix axis, the\n35\tproduction 24/8 mix with its damped propensity correction is a local optimum,\n36\tand any gain has to come from elsewhere: more targets or epochs, or skip/dislike\n37\thard negatives, which were not tested within the budget.\n38\t\n39\t## Shipped artifact\n40\t\n41\tNo tested variant beat production, so the submission is the **production\n42\tnegative-sampling configuration refit on `train_* + val_*`** (defaults of\n43\t`train_current_model.py`). The implementation keeps the new, optional\n44\tobjectives behind flags for further work:\n45\t"
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14917816 tokens left"
}
## Step 62 · agent · 2026-09-26T07:12:31.556Z
### Thinking unavailable — no text recorded
I didn't improve on the production model. None of the three negative-sampling changes I tested beat it on the public backtest, so the submission I shipped is the production configuration refit on `train_* + val_*`.
`/app/solution/submission.parquet` passes a format check against the full contract: 8,456 users × 100 rows, ranks 1–100, and only items from each user's test candidate set.
**Diagnosis.** The production trainer uses pointwise BCE (a per-item yes/no loss) with only 32 negatives per target out of a 30k catalog: 24 random catalog items plus 8 items taken from other targets in the same batch. Losses on those batch items are down-weighted by popularity, and any negative the user has listened to before is swapped for a random item. So the negatives are few, mostly obscure, and rarely "hard" popular tracks. Skipped listens and dislikes are never used as negatives.
**Public NDCG@10 (4,263 users):**
| Variant | NDCG@10 |
| --- | --- |
| Production (24 random + 8 batch negatives) | **0.02128** |
| Softmax loss over 4,096 shared negatives, half popular, with popularity correction | 0.01913 |
| 16 random + 16 batch negatives (more popular negatives) | 0.01958 |
| 32 random, no batch negatives | 0.01957 |
Adding popular negatives lowered the score, and so did removing them entirely. My reading is that because candidates are always tracks the user hasn't heard, a general sense of what's popular carries much of the signal. With one epoch on CPU, the model loses that faster than it learns anything personal. On this axis the production mix looks like a local optimum. More training data or epochs, or skips/dislikes as hard negatives, are the untested next steps.
**Code.** `/app/solution/train_current_model.py` (plus `two_tower_model.py`) runs the production config by default, so it reproduces the submission. The softmax loss and negative counts are optional flags (`--objective sampled-softmax`, `--shared-negatives`, `--uniform-negatives`, `--inbatch-negatives`). Serving, data and evaluation are unchanged. `README.md` records the diagnosis, results and commands, and `check_submission.py` validates a ranking.
Two things went wrong during the run:
- **Out of memory:** each trainer needs about 6 GB of the 15 GB RAM. Running two or three at once got them killed with no log, including my first final refit, which cost about 15 minutes. The README says to run one at a time.
- **Killed shell:** twice a `pkill` pattern matched the shell's own command line and killed it. Nothing was lost, and the final cleanup was redone and checked.
Submission command:
```sh
python /app/solution/train_current_model.py --stage submission --public-dir /task \
--prepared-cache /app/solution/cache_sub.npz --output /app/solution/submission.parquet
```
## Recorded session usage
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