yambda-discovery-ranking-two-tower-case-1 / opus55-high-2-1-283-case1-r03-20260926T034923Z
Recorded model: claude-opus-5-5
## Step 1 · user · 2026-09-26T06:26:59.243Z
# 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
{
"type": "deferred_tools_delta",
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"- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ArtifactComments, ArtifactData, ArtifactCheck, ExitPlanMode, Edit, Write, NotebookEdit)",
"- 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
{
"type": "session_context",
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### Harness context
{
"type": "date",
"date": "2026-09-26"
}
### Harness context
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"pr": "🤖 Generated with [Claude Code](https://claude.com/claude-code)",
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### Harness context
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"\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.",
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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 · 2026-09-26T06:27:00.801Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:01.955Z
{
"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 · 2026-09-26T06:27:02.038Z
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 · 2026-09-26T06:27:01.983Z
{
"command": "cat /task/current_model/train_current_model.py"
}
### Tool output · 2026-09-26T06:27:02.031Z
Output too large (78.2KB). Full output saved to: /logs/agent/sessions/projects/-app/9aa27726-e2fb-4f7d-88f2-a0bb79cb0456/tool-results/bxvqe4dku.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": "14981911 tokens left"
}
## Step 3 · agent · 2026-09-26T06:27:03.600Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:04.303Z
{
"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_run.log 2>&1 &\nsleep 2; grep -n \"def \\|add_argument\" train_current_model.py | head -150"
}
### Tool output · 2026-09-26T06:27:06.333Z
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:
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": "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": "14973304 tokens left"
}
## Step 4 · agent · 2026-09-26T06:27:07.536Z
### Bash · 2026-09-26T06:27:07.536Z
{
"command": "cd /app/solution; sed -n 40,80p train_current_model.py; sed -n 530,870p train_current_model.py"
}
### Tool output · 2026-09-26T06:27:07.563Z
@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
save_cache(cache, prepared, stage, config)
return prepared, built_eligibility
def build_item_correction(
prepared: SequenceData,
config: Config,
target_starts: np.ndarray,
target_counts: np.ndarray,
) -> torch.Tensor:
"""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": "total_tokens_reminder",
"text": "14971919 tokens left"
}
## Step 5 · agent · 2026-09-26T06:27:14.808Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:15.192Z
{
"command": "cd /app/solution; sed -n 1035,1365p train_current_model.py; cat base_run.log"
}
### Tool output · 2026-09-26T06:27:15.218Z
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,
loaded explicit events: rows=720,270 users=8,220
### Harness context
{
"type": "total_tokens_reminder",
"text": "14966148 tokens left"
}
## Step 6 · agent · 2026-09-26T06:27:25.635Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:26.326Z
{
"command": "cd /app/solution; sed -n 955,1034p train_current_model.py; sed -n 1521,1560p train_current_model.py; sed -n 1782,1922p train_current_model.py; cat base_run.log"
}
### Tool output · 2026-09-26T06:27:26.359Z
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
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,
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")
def main() -> int:
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=5.0s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=10.2s
### 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": "14960561 tokens left"
}
## Step 7 · agent · 2026-09-26T06:27:40.183Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:27:40.667Z
{
"command": "sleep 45; cd /app/solution; cat base_run.log; sed -n 1560,1780p train_current_model.py"
}
### Tool output · 2026-09-26T06:28:25.700Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=5.0s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=10.2s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.5s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=21.1s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=26.0s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.8s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=36.4s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.6s
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
stage,
prepared,
partition.history_training_counts,
config,
)
known_positive_packed = (
build_known_positive_filter(prepared, partition.history_training_counts)
if config.negative_filter_scope == "full-training-history"
else None
)
history: list[dict[str, float]] = []
best_loss = float("inf")
best_epoch = 0
best_state: dict[str, torch.Tensor] | None = None
epochs_without_improvement = 0
start_epoch = 0
if resume_checkpoint is not None:
payload = torch.load(
resume_checkpoint, map_location=device, weights_only=False
)
stored_config = payload.get("config", {})
architecture_fields = (
"bptt_steps",
"embedding_dim",
"hidden_dim",
"user_hidden_1",
"user_hidden_2",
"item_hidden_1",
"item_hidden_2",
)
mismatches = {
name: (stored_config.get(name), getattr(config, name))
for name in architecture_fields
if stored_config.get(name) != getattr(config, name)
}
if mismatches:
raise RuntimeError(
f"resume checkpoint architecture differs: {mismatches}"
)
stored_filter_scope = stored_config.get(
"negative_filter_scope", "last-100"
)
stored_target_weighting = stored_config.get(
"target_weighting", "uniform"
)
stored_weight_values = (
stored_config.get("ordinary_target_weight", 1.0),
stored_config.get("organic_strong_target_weight", 1.0),
stored_config.get("organic_like_target_weight", 3.0),
stored_config.get("discovery_horizon_days", 7),
)
current_weight_values = (
config.ordinary_target_weight,
config.organic_strong_target_weight,
config.organic_like_target_weight,
config.discovery_horizon_days,
)
stored_window_values = (
stored_config.get("validation_fraction", 0.10),
stored_config.get("max_train_targets_per_user", 0),
stored_config.get("max_validation_targets_per_user", 0),
)
current_window_values = (
config.validation_fraction,
config.max_train_targets_per_user,
config.max_validation_targets_per_user,
)
objective_changed = (
stored_filter_scope != config.negative_filter_scope
or stored_target_weighting != config.target_weighting
or stored_weight_values != current_weight_values
or stored_window_values != current_window_values
)
model.load_state_dict(payload["model_state"])
if "optimizer_state" in payload and not objective_changed:
optimizer.load_state_dict(payload["optimizer_state"])
for group in optimizer.param_groups:
group["lr"] = config.learning_rate
group["weight_decay"] = config.weight_decay
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
### Harness context
{
"type": "total_tokens_reminder",
"text": "14955354 tokens left"
}
## Step 8 · agent · 2026-09-26T06:28:36.287Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:28:36.758Z
{
"command": "sleep 20; cd /app/solution; tail -5 base_run.log; sed -n 1922,2164p train_current_model.py | grep -v \"^\\s*$\" | head -250"
}
### Tool output · 2026-09-26T06:28:56.835Z
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 main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--stage", choices=("validation", "submission"), default="validation"
)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--public-dir", type=Path, required=True)
parser.add_argument("--prepared-cache", type=Path)
parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
parser.add_argument("--threads", type=int, default=8)
parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
parser.add_argument(
"--user-batch-size", type=int, default=Config.user_batch_size
)
parser.add_argument("--epochs", type=int, default=Config.epochs)
parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
parser.add_argument(
"--loss-microbatch-size",
type=int,
default=Config.loss_microbatch_size,
)
parser.add_argument(
"--negative-filter-scope",
choices=("full-training-history", "last-100"),
default=Config.negative_filter_scope,
)
parser.add_argument(
"--target-weighting",
choices=("discovery-graded", "organic-strong", "uniform"),
default=Config.target_weighting,
)
parser.add_argument(
"--ordinary-target-weight",
type=float,
default=Config.ordinary_target_weight,
)
parser.add_argument(
"--organic-strong-target-weight",
type=float,
default=Config.organic_strong_target_weight,
)
parser.add_argument(
"--organic-like-target-weight",
type=float,
default=Config.organic_like_target_weight,
)
parser.add_argument(
"--discovery-horizon-days",
type=int,
default=Config.discovery_horizon_days,
)
parser.add_argument(
"--validation-fraction",
type=float,
default=Config.validation_fraction,
)
parser.add_argument(
"--max-train-targets-per-user",
type=int,
default=Config.max_train_targets_per_user,
help="retain this many recent pre-validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--max-validation-targets-per-user",
type=int,
default=Config.max_validation_targets_per_user,
help="retain this many final validation targets per user; 0 is uncapped",
)
parser.add_argument(
"--early-stopping-patience",
type=int,
default=Config.early_stopping_patience,
)
parser.add_argument(
"--early-stopping-min-delta",
type=float,
default=Config.early_stopping_min_delta,
)
parser.add_argument("--checkpoint-dir", type=Path)
parser.add_argument("--resume-checkpoint", type=Path)
parser.add_argument("--seed", type=int, default=Config.seed)
parser.add_argument("--ranking-batch-size", type=int, default=64)
parser.add_argument("--prepare-only", action="store_true")
args = parser.parse_args()
if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
parser.error("threads, BPTT steps, and user batch size must be positive")
if (
args.epochs < 1
or args.learning_rate <= 0
or args.loss_microbatch_size < 1
or args.early_stopping_patience < 1
or args.early_stopping_min_delta < 0
or args.ordinary_target_weight <= 0
or args.organic_strong_target_weight <= 0
or args.organic_like_target_weight <= 0
or args.discovery_horizon_days < 1
or args.max_train_targets_per_user < 0
or args.max_validation_targets_per_user < 0
):
parser.error("training counts, learning rate, and patience are invalid")
if not 0.0 < args.validation_fraction < 1.0:
parser.error("validation fraction must be strictly between zero and one")
public = args.public_dir.resolve()
if not (public / "data" / "candidate_catalog.parquet").is_file():
parser.error(f"public task data not found under {public}")
config = Config(
bptt_steps=args.bptt_steps,
user_batch_size=args.user_batch_size,
epochs=args.epochs,
learning_rate=args.learning_rate,
loss_microbatch_size=args.loss_microbatch_size,
negative_filter_scope=args.negative_filter_scope,
target_weighting=args.target_weighting,
ordinary_target_weight=args.ordinary_target_weight,
organic_strong_target_weight=args.organic_strong_target_weight,
organic_like_target_weight=args.organic_like_target_weight,
discovery_horizon_days=args.discovery_horizon_days,
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": "14951053 tokens left"
}
## Step 9 · agent · 2026-09-26T06:29:04.372Z
### Thinking unavailable — no text recorded
### Recorded reasoning
While the baseline trains (~1.05M targets, ~286 blocks), I've diagnosed that the current per-row BCE loss only uses 32 weak negatives with an averaged negative term, underweighting them relative to positives. I'm now implementing a block-shared sampled softmax with thousands of shared negatives, logQ correction, and the same false-negative filtering.
### Bash · 2026-09-26T06:29:17.387Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(''' target_weighting: str = \"organic-strong\"\n''',''' target_weighting: str = \"organic-strong\"\n negative_objective: str = \"shared-softmax\"\n shared_uniform_negatives: int = 4096\n logq_alpha: float = 1.0\n''',1)\n# softmax scorer inserted before release_accelerator_cache\nfunc='''\ndef build_target_log_probabilities(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Smoothed training-target frequency used by the in-batch logQ term.\"\"\"\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 probabilities = (counts + 1.0) / (counts.sum() + len(counts))\n return torch.from_numpy(np.log(probabilities))\n\n\ndef score_shared_softmax_block(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n target_log_probabilities: torch.Tensor,\n sample_weights: np.ndarray,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, dict[str, float]]:\n \"\"\"Sampled softmax over one negative pool shared by every block target.\n\n The pool is the block's unique targets (popularity-distributed in-batch\n negatives) plus uniform catalog draws. Every logit is corrected by\n ``logq_alpha * log`` of the item's expected pool inclusion, and pool items\n that are known training positives or active likes of a row's user are\n masked for that row (the row's own target is kept as the label).\n \"\"\"\n n_items = len(prepared.item_ids)\n target_count = len(positive_items)\n uniform = torch.randint(\n n_items, (config.shared_uniform_negatives,), generator=generator\n )\n pool, inverse = torch.unique(\n torch.cat([positive_items.cpu(), uniform]), return_inverse=True\n )\n labels = inverse[:target_count]\n target_probability = target_log_probabilities[pool].exp()\n inclusion = (\n -torch.expm1(target_count * torch.log1p(-target_probability))\n + config.shared_uniform_negatives / n_items\n ).clamp_max(1.0)\n log_inclusion = inclusion.log().float()\n\n invalid = np.zeros((target_count, len(pool)), dtype=bool)\n pool_np = pool.numpy()\n if known_positive_packed is not None:\n bytes_ = known_positive_packed[user_rows[:, None], (pool_np >> 3)[None, :]]\n invalid |= ((bytes_ >> (pool_np & 7).astype(np.uint8)[None, :]) & 1).astype(\n bool\n )\n liked = prepared.liked_states[flat_states].astype(np.int64)\n liked_columns = np.searchsorted(pool_np, np.minimum(liked, n_items - 1))\n liked_columns = np.minimum(liked_columns, len(pool_np) - 1)\n liked_hit = (pool_np[liked_columns] == liked) & (liked < n_items)\n rows_hit = np.broadcast_to(\n np.arange(target_count)[:, None], liked.shape\n )[liked_hit]\n invalid[rows_hit, liked_columns[liked_hit]] = True\n labels_np = labels.numpy()\n invalid[np.arange(target_count), labels_np] = False\n\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, flat_states, device\n )\n counts = prepared.state_counts[flat_states]\n numeric = np.zeros((target_count, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_vectors,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n pool_vectors = model.encode_items(pool.to(device))\n logits = query_vectors @ pool_vectors.T\n corrected = logits - config.logq_alpha * log_inclusion.to(device)[None, :]\n corrected = corrected.masked_fill(torch.from_numpy(invalid).to(device), -1.0e9)\n labels = labels.to(device)\n per_target = F.cross_entropy(corrected, labels, reduction=\"none\")\n weights = torch.from_numpy(sample_weights.astype(np.float32)).to(device)\n loss = (weights * per_target).sum() / weights.sum()\n if not bool(torch.isfinite(loss)):\n raise RuntimeError(\"shared softmax produced a non-finite loss\")\n with torch.no_grad():\n positive_logits = logits.gather(1, labels[:, None]).squeeze(1)\n valid = ~torch.from_numpy(invalid).to(device)\n valid[torch.arange(target_count), labels] = False\n beaten = ((logits > positive_logits[:, None]) & valid).sum(dim=1).float()\n statistics = {\n \"loss_sum\": float((weights * per_target).sum()),\n \"weight_sum\": float(weights.sum()),\n \"positive_sum\": float(positive_logits.sum()),\n \"negative_sum\": float((logits * valid).sum() / valid.sum().clamp_min(1)\n * target_count),\n \"pair_correct\": float(\n (1.0 - beaten / valid.sum(dim=1).clamp_min(1)).sum()\n ),\n \"pool\": float(len(pool)),\n }\n return loss, statistics\n\n\ndef release_accelerator_cache('''\ns=s.replace('\\ndef release_accelerator_cache(',func,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"def release_accelerator_cache\" train_current_model.py; tail -3 base_run.log"
}
### Tool output · 2026-09-26T06:29:17.466Z
Traceback (most recent call last):
File "", line 133, in
PermissionError: [Errno 13] Permission denied: 'train_current_model.py'
1198:def release_accelerator_cache(device: torch.device) -> None:
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": "14944763 tokens left"
}
## Step 10 · agent · 2026-09-26T06:29:18.657Z
Copied files kept read-only permissions; fixing and re-applying.
### Bash · 2026-09-26T06:29:19.007Z
{
"command": "cd /app/solution; chmod u+w *.py; ls -la"
}
### Tool output · 2026-09-26T06:29:19.062Z
total 112
drwxr-xr-x 1 agent agent 4096 Sep 26 06:27 .
drwxr-xr-x 1 agent agent 4096 Sep 26 06:22 ..
-rw-r--r-- 1 agent agent 1160 Sep 26 06:28 base_run.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": "total_tokens_reminder",
"text": "14944459 tokens left"
}
## Step 11 · agent · 2026-09-26T06:29:21.422Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:35.987Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(''' target_weighting: str = \"organic-strong\"\n''',''' target_weighting: str = \"organic-strong\"\n negative_objective: str = \"shared-softmax\"\n shared_uniform_negatives: int = 4096\n logq_alpha: float = 1.0\n''',1)\nfunc='''\ndef build_target_log_probabilities(\n prepared: SequenceData,\n target_starts: np.ndarray,\n target_counts: np.ndarray,\n) -> torch.Tensor:\n \"\"\"Smoothed training-target frequency used by the in-batch logQ term.\"\"\"\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 probabilities = (counts + 1.0) / (counts.sum() + len(counts))\n return torch.from_numpy(np.log(probabilities))\n\n\ndef score_shared_softmax_block(\n model: base.SequentialTwoTower,\n prepared: SequenceData,\n config: Config,\n sequence_vectors: torch.Tensor,\n flat_states: np.ndarray,\n flat_activity: np.ndarray,\n positive_items: torch.Tensor,\n user_rows: np.ndarray,\n known_positive_packed: np.ndarray | None,\n target_log_probabilities: torch.Tensor,\n sample_weights: np.ndarray,\n generator: torch.Generator,\n device: torch.device,\n) -> tuple[torch.Tensor, dict[str, float]]:\n \"\"\"Sampled softmax over one negative pool shared by every block target.\n\n The pool is the block's unique targets (popularity-distributed in-batch\n negatives) plus uniform catalog draws. Every logit is corrected by\n ``logq_alpha * log`` of the item's expected pool inclusion, and pool items\n that are known training positives or active likes of a row's user are\n masked for that row (the row's own target is kept as the label).\n \"\"\"\n n_items = len(prepared.item_ids)\n target_count = len(positive_items)\n uniform = torch.randint(\n n_items, (config.shared_uniform_negatives,), generator=generator\n )\n pool, inverse = torch.unique(\n torch.cat([positive_items.cpu(), uniform]), return_inverse=True\n )\n labels = inverse[:target_count]\n target_probability = target_log_probabilities[pool].exp()\n inclusion = (\n -torch.expm1(target_count * torch.log1p(-target_probability))\n + config.shared_uniform_negatives / n_items\n ).clamp_max(1.0)\n log_inclusion = inclusion.log().float()\n\n invalid = np.zeros((target_count, len(pool)), dtype=bool)\n pool_np = pool.numpy()\n if known_positive_packed is not None:\n bytes_ = known_positive_packed[user_rows[:, None], (pool_np >> 3)[None, :]]\n invalid |= ((bytes_ >> (pool_np & 7).astype(np.uint8)[None, :]) & 1).astype(\n bool\n )\n liked = prepared.liked_states[flat_states].astype(np.int64)\n liked_columns = np.searchsorted(pool_np, np.minimum(liked, n_items - 1))\n liked_columns = np.minimum(liked_columns, len(pool_np) - 1)\n liked_hit = (pool_np[liked_columns] == liked) & (liked < n_items)\n rows_hit = np.broadcast_to(\n np.arange(target_count)[:, None], liked.shape\n )[liked_hit]\n invalid[rows_hit, liked_columns[liked_hit]] = True\n labels_np = labels.numpy()\n invalid[np.arange(target_count), labels_np] = False\n\n liked_vectors, disliked_vectors, _ = pool_explicit_states(\n model, prepared, flat_states, device\n )\n counts = prepared.state_counts[flat_states]\n numeric = np.zeros((target_count, 6), dtype=np.float32)\n numeric[:, :4] = flat_activity\n numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n query_vectors = user_queries(\n model,\n sequence_vectors,\n liked_vectors,\n disliked_vectors,\n torch.from_numpy(numeric).to(device),\n )\n pool_vectors = model.encode_items(pool.to(device))\n logits = query_vectors @ pool_vectors.T\n corrected = logits - config.logq_alpha * log_inclusion.to(device)[None, :]\n corrected = corrected.masked_fill(torch.from_numpy(invalid).to(device), -1.0e9)\n labels = labels.to(device)\n per_target = F.cross_entropy(corrected, labels, reduction=\"none\")\n weights = torch.from_numpy(sample_weights.astype(np.float32)).to(device)\n loss = (weights * per_target).sum() / weights.sum()\n if not bool(torch.isfinite(loss)):\n raise RuntimeError(\"shared softmax produced a non-finite loss\")\n with torch.no_grad():\n positive_logits = logits.gather(1, labels[:, None]).squeeze(1)\n valid = ~torch.from_numpy(invalid).to(device)\n valid[torch.arange(target_count), labels] = False\n beaten = ((logits > positive_logits[:, None]) & valid).sum(dim=1).float()\n statistics = {\n \"loss_sum\": float((weights * per_target).sum()),\n \"weight_sum\": float(weights.sum()),\n \"positive_sum\": float(positive_logits.sum()),\n \"negative_sum\": float((logits * valid).sum() / valid.sum().clamp_min(1)\n * target_count),\n \"pair_correct\": float(\n (1.0 - beaten / valid.sum(dim=1).clamp_min(1)).sum()\n ),\n \"pool\": float(len(pool)),\n }\n return loss, statistics\n\n\ndef release_accelerator_cache('''\ns=s.replace('\\ndef release_accelerator_cache(',func,1)\nopen(p,'w').write(s)\nEOF\ngrep -n \"def release_accelerator_cache\" train_current_model.py; tail -3 base_run.log"
}
### Tool output · 2026-09-26T06:29:36.099Z
1321:def release_accelerator_cache(device: torch.device) -> None:
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": "14941765 tokens left"
}
## Step 12 · agent · 2026-09-26T06:29:38.686Z
### Thinking unavailable — no text recorded
Now wiring it into the training loop.
### Bash · 2026-09-26T06:29:47.267Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\nold=''' negatives, negative_weights = draw_training_negatives(\n positive_items,'''\nnew=''' if config.negative_objective == \"shared-softmax\":\n optimizer.zero_grad(set_to_none=True)\n loss, statistics = score_shared_softmax_block(\n model,\n prepared,\n config,\n sequence_vectors,\n flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items,\n flat_user_rows,\n known_positive_packed,\n target_log_probabilities,\n block[8].reshape(-1)[flat_scored],\n generator,\n device,\n )\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n else:\n statistics = run_bce_block(\n model, optimizer, prepared, config, sequence_vectors,\n flat_states, block, flat_scored, positive_items,\n known_history, liked_items, flat_user_rows,\n known_positive_packed, item_correction, generator, device,\n )\n\n count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]'''\ni=s.index(old); j=s.index(''' count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]''', i)\nbce_body=s[i:j]\ns=s[:i]+new+s[j+len(''' count = len(positive_items)\n total_loss += statistics[\"loss_sum\"]'''):]\n# build run_bce_block from body (dedent by 4)\nimport textwrap\nbody=textwrap.dedent(bce_body)\nfn='''def run_bce_block(\n model, optimizer, prepared, config, sequence_vectors, flat_states, block,\n flat_scored, positive_items, known_history, liked_items, flat_user_rows,\n known_positive_packed, item_correction, generator, device,\n) -> dict[str, float]:\n \"\"\"The production 24-uniform + 8-in-batch BCE step (kept for ablation).\"\"\"\n'''+textwrap.indent(body,' ')+''' statistics[\"pair_correct\"] /= (\n config.uniform_negatives + config.inbatch_negatives\n )\n return statistics\n\n\ndef run_training_epoch('''\ns=s.replace('def run_training_epoch(',fn,1)\ns=s.replace(''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n) -> dict[str, float]:''',''' known_positive_packed: np.ndarray | None,\n generator: torch.Generator,\n device: torch.device,\n epoch: int,\n target_log_probabilities: torch.Tensor | None = None,\n) -> dict[str, float]:''',1)\ns=s.replace(''' \"pair_accuracy\": total_pair_correct\n / max(total_targets * negative_count, 1),''',''' \"pair_accuracy\": total_pair_correct / max(total_targets, 1),''',1)\ns=s.replace(''' generator,\n device,\n epoch,\n )''',''' generator,\n device,\n epoch,\n target_log_probabilities,\n )''',1)\ns=s.replace(''' target_weights = build_target_weights(\n public,''',''' target_log_probabilities = build_target_log_probabilities(\n prepared, partition.window_starts, training_counts\n )\n target_weights = build_target_weights(\n public,''',1)\nopen(p,'w').write(s)\nEOF\nsed -n '/^def run_bce_block/,/^def run_training_epoch/p' train_current_model.py; tail -3 base_run.log"
}
### Tool output · 2026-09-26T06:29:47.434Z
def run_bce_block(
model, optimizer, prepared, config, sequence_vectors, flat_states, block,
flat_scored, positive_items, known_history, liked_items, flat_user_rows,
known_positive_packed, item_correction, generator, device,
) -> dict[str, float]:
"""The production 24-uniform + 8-in-batch BCE step (kept for ablation)."""
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()
statistics["pair_correct"] /= (
config.uniform_negatives + config.inbatch_negatives
)
return statistics
def run_training_epoch(
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": "14940103 tokens left"
}
## Step 13 · agent · 2026-09-26T06:29:50.482Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:53.638Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(''' parser.add_argument(\"--checkpoint-dir\", type=Path)''',''' parser.add_argument(\n \"--negative-objective\",\n choices=(\"shared-softmax\", \"bce\"),\n default=Config.negative_objective,\n )\n parser.add_argument(\n \"--shared-uniform-negatives\",\n type=int,\n default=Config.shared_uniform_negatives,\n )\n parser.add_argument(\"--logq-alpha\", type=float, default=Config.logq_alpha)\n parser.add_argument(\"--checkpoint-dir\", type=Path)''',1)\ns=s.replace(''' target_weighting=args.target_weighting,\n ordinary''',''' target_weighting=args.target_weighting,\n negative_objective=args.negative_objective,\n shared_uniform_negatives=args.shared_uniform_negatives,\n logq_alpha=args.logq_alpha,\n ordinary''',1)\nopen(p,'w').write(s)\nEOF\npython -c \"import ast;ast.parse(open('train_current_model.py').read())\"; grep -n \"cache\" train_current_model.py | sed -n 1,30p | grep -n \"def \\|path\\|Path\" ; date; tail -2 base_run.log; ps aux | grep train_current | head -2"
}
### Tool output · 2026-09-26T06:29:53.887Z
1:474:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
3:484:def save_cache(
5:493: log(f"saved public-derived full-sequence cache: {path}")
6:496:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
10:518: cache: Path | None,
16:1321:def release_accelerator_cache(device: torch.device) -> None:
21:2096: parser.add_argument("--prepared-cache", type=Path)
Sat Sep 26 06:29:53 UTC 2026
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
agent 8134 0.0 0.0 4492 1696 ? S 06:27 0:00 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790404022000-f4j4t0.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval '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_run.log 2>&1 & sleep 2; grep -n "def \|add_argument" train_current_model.py | head -150' < /dev/null && pwd -P >| /tmp/claude-4445-cwd
agent 8136 388 27.5 6513680 4407404 ? Rl 06:27 10:57 python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet
### Harness context
{
"type": "total_tokens_reminder",
"text": "14938759 tokens left"
}
## Step 14 · agent · 2026-09-26T06:29:58.467Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:29:59.095Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage validation --public-dir /task --prepared-cache /app/solution/cache_validation.npz --threads 4 --output /app/solution/softmax_validation.parquet) > sm_run.log 2>&1 &\nsleep 100; tail -3 sm_run.log; tail -2 base_run.log"
}
### Tool output · 2026-09-26T06:31:39.133Z
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
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": "14937608 tokens left"
}
## Step 15 · agent · 2026-09-26T06:31:41.127Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:31:41.167Z
{
"command": "cd /app/solution; ls -la; sed -n 1,40p /task/evaluate_public.py"
}
### Tool output · 2026-09-26T06:31:41.233Z
total 522812
drwxr-xr-x 1 agent agent 4096 Sep 26 06:31 .
drwxr-xr-x 1 agent agent 4096 Sep 26 06:22 ..
-rw-r--r-- 1 agent agent 1160 Sep 26 06:28 base_run.log
-rw-r--r-- 1 agent agent 535225808 Sep 26 06:31 cache_validation.npz
-rw-r--r-- 1 agent agent 1237 Sep 26 06:31 sm_run.log
-rw-r--r-- 1 agent agent 87422 Sep 26 06:29 train_current_model.py
-rw-r--r-- 1 agent agent 15985 Sep 26 06:27 two_tower_model.py
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14937252 tokens left"
}
## Step 16 · agent · 2026-09-26T06:31:45.653Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:31:45.875Z
{
"command": "sleep 120; cd /app/solution; tail -2 sm_run.log; tail -2 base_run.log; date"
}
### Tool error · 2026-09-26T06:33:46.030Z
Exit code 143
Command timed out after 2m 0s
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
user 21m40.464s
sys 1m7.425s
Sat Sep 26 06:33:45 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14936058 tokens left"
}
## Step 17 · agent · 2026-09-26T06:33:47.406Z
### Bash · 2026-09-26T06:33:47.406Z
{
"command": "cd /app/solution; tail -30 base_run.log | grep -v '^ \\|^{\\|^}' ; ls"
}
### Tool output · 2026-09-26T06:33:47.498Z
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=5.0s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=10.2s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.5s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=21.1s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=26.0s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.8s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=36.4s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=46.6s
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
/bin/bash: line 2: 8136 Killed python train_current_model.py --stage validation --public-dir /task --output /app/solution/production_validation.parquet
real 4m52.520s
user 21m40.464s
sys 1m7.425s
base_run.log
cache_validation.npz
sm_run.log
train_current_model.py
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": "14935810 tokens left"
}
## Step 18 · agent · 2026-09-26T06:33:48.909Z
### Thinking unavailable — no text recorded
The production baseline run was OOM-killed — running two trainings at once exceeded the 15 GB RAM. Checking whether my softmax run survived.
### Bash · 2026-09-26T06:33:49.414Z
{
"command": "ps aux | grep \"train_current\" | grep -v grep | awk '{print $2,$3,$4,$6,$10}'; free -m | head -2"
}
### Tool output · 2026-09-26T06:33:49.455Z
8275 0.0 0.0 1888 0:00
8277 222 22.8 3653556 8:31
total used free shared buff/cache available
Mem: 15614 4768 4835 2 6362 10845
### Harness context
{
"type": "total_tokens_reminder",
"text": "14934879 tokens left"
}
## Step 19 · agent · 2026-09-26T06:33:53.141Z
### Thinking unavailable — no text recorded
It survived (3.6 GB). I'll let it run alone and rerun the baseline afterward.
### Bash · 2026-09-26T06:33:53.520Z
{
"command": "sleep 110; cd /app/solution; tail -2 sm_run.log; date"
}
### Tool output · 2026-09-26T06:35:43.550Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=7.985775 running_bce=8.283997 elapsed=232.0s
Sat Sep 26 06:35:43 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14934430 tokens left"
}
## Step 20 · agent · 2026-09-26T06:35:45.614Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:35:45.761Z
{
"command": "sleep 60; cd /app/solution; grep -v '^ \\|^{\\|^}\\|\"' sm_run.log | tail -6; date"
}
### Tool output · 2026-09-26T06:36:45.792Z
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=7.985775 running_bce=8.283997 elapsed=232.0s
epoch=1/1 train_loss=8.27547 validation_loss=0.52134 pair_accuracy=0.719 train_targets=1,052,953 validation_targets=130,103 train_seconds=246.2 validation_seconds=17.4
restored best validation-BCE checkpoint: epoch=1 loss=0.521344
Sat Sep 26 06:36:45 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14934096 tokens left"
}
## Step 21 · agent · 2026-09-26T06:36:49.189Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:36:49.749Z
{
"command": "sleep 25; cd /app/solution; ls; grep -v '^ \\|^{\\|^}\\|\"' sm_run.log | tail -2; ls softmax_validation.parquet && python /task/evaluate_public.py softmax_validation.parquet"
}
### Tool output · 2026-09-26T06:37:18.309Z
base_run.log
cache_validation.npz
sm_run.log
softmax_validation.parquet
train_current_model.py
two_tower_model.py
user 16m48.441s
sys 3m26.720s
softmax_validation.parquet
{
"model": "softmax_validation.parquet",
"scores": {
"quality_ndcg@10": 0.017546,
"quality_users": 4263
}
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14933432 tokens left"
}
## Step 22 · agent · 2026-09-26T06:37:24.933Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The softmax variant scored NDCG@10 = 0.01755. Now running the production BCE baseline and a partial-logQ variant (α=0.5) off the shared prepared cache to conserve memory.
### Bash · 2026-09-26T06:37:24.942Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 4 --negative-objective bce --output production_validation.parquet) > base_run.log 2>&1 &\n(time python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 4 --logq-alpha 0.5 --output sm_a05_validation.parquet) > sm_a05.log 2>&1 &\nsleep 60; free -m|head -2; tail -1 base_run.log sm_a05.log"
}
### Tool error · 2026-09-26T06:38:24.979Z
Exit code 1
total used free shared buff/cache available
Mem: 15614 5891 3186 2 6889 9723
tail: option used in invalid context -- 1
### Harness context
{
"type": "total_tokens_reminder",
"text": "14932621 tokens left"
}
## Step 23 · agent · 2026-09-26T06:38:27.236Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:38:27.383Z
{
"command": "sleep 200; cd /app/solution; for f in base_run.log sm_a05.log; do tail -n1 $f; done; free -m | head -2"
}
### Tool error · 2026-09-26T06:40:27.420Z
Exit code 143
Command timed out after 2m 0s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14932332 tokens left"
}
## Step 24 · agent · 2026-09-26T06:40:28.710Z
### Bash · 2026-09-26T06:40:28.710Z
{
"command": "cd /app/solution; for f in base_run.log sm_a05.log; do tail -n1 $f; done; free -m | head -2; date"
}
### Tool output · 2026-09-26T06:40:28.748Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
total used free shared buff/cache available
Mem: 15614 7928 1201 2 6836 7685
Sat Sep 26 06:40:28 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": "14932187 tokens left"
}
## Step 25 · agent · 2026-09-26T06:40:31.526Z
### Thinking unavailable — no text recorded
Both runs are training (both about 4 GB, no memory pressure). They should finish around 06:45; after that I'll start the final refit with whichever softmax setting wins.
### Bash · 2026-09-26T06:40:32.014Z
{
"command": "sleep 115; cd /app/solution; for f in base_run.log sm_a05.log; do grep \"epoch=1/1\" $f | tail -n1; done"
}
### Tool output · 2026-09-26T06:42:27.202Z
(Bash completed with no output)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931720 tokens left"
}
## Step 26 · agent · 2026-09-26T06:42:28.590Z
### Bash · 2026-09-26T06:42:28.590Z
{
"command": "sleep 110; cd /app/solution; for f in base_run.log sm_a05.log; do grep \"epoch=1/1\" $f | tail -n1; done; date"
}
### Tool output · 2026-09-26T06:44:18.650Z
Sat Sep 26 06:44:18 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931581 tokens left"
}
## Step 27 · agent · 2026-09-26T06:44:22.207Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:44:22.545Z
{
"command": "sleep 90; cd /app/solution; for f in base_run.log sm_a05.log; do grep \"epoch=1/1\" $f | tail -n1; done; date"
}
### Tool output · 2026-09-26T06:45:52.568Z
Sat Sep 26 06:45:52 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931167 tokens left"
}
## Step 28 · agent · 2026-09-26T06:45:53.536Z
### Bash · 2026-09-26T06:45:53.536Z
{
"command": "top -bn1 | head -15"
}
### Tool output · 2026-09-26T06:45:53.740Z
top - 06:45:53 up 29 min, 0 user, load average: 0.18, 2.11, 2.51
Tasks: 8 total, 1 running, 7 sleeping, 0 stopped, 0 zombie
%Cpu(s): 0.0 us, 0.0 sy, 0.0 ni, 83.3 id, 16.7 wa, 0.0 hi, 0.0 si, 0.0 st
MiB Mem : 15614.2 total, 8548.4 free, 1058.7 used, 6359.8 buff/cache
MiB Swap: 0.0 total, 0.0 free, 0.0 used. 14555.6 avail Mem
PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND
1 root 20 0 2592 1556 1452 S 0.0 0.0 0:00.01 sh
7 root 20 0 2920 1692 1588 S 0.0 0.0 0:00.00 sleep
8051 agent 20 0 4480 3376 3028 S 0.0 0.0 0:00.01 bash
8058 agent 20 0 5599996 189960 59156 S 0.0 1.2 0:07.54 claude
8059 agent 20 0 2924 1668 1560 S 0.0 0.0 0:00.01 tee
8718 agent 20 0 4492 3320 2956 S 0.0 0.0 0:00.00 bash
8720 agent 20 0 9084 5128 3080 R 0.0 0.0 0:00.00 top
8721 agent 20 0 2928 1700 1592 S 0.0 0.0 0:00.00 head
### Harness context
{
"type": "total_tokens_reminder",
"text": "14931061 tokens left"
}
## Step 29 · agent · 2026-09-26T06:45:54.901Z
### Thinking unavailable — no text recorded
Both processes are gone. Checking the logs.
### Bash · 2026-09-26T06:45:55.086Z
{
"command": "cd /app/solution; tail -4 base_run.log; tail -4 sm_a05.log; ls"
}
### Tool output · 2026-09-26T06:45:55.108Z
real 3m58.491s
user 9m28.573s
sys 1m15.917s
real 3m58.491s
user 8m50.990s
sys 3m8.939s
base_run.log
cache_validation.npz
sm_a05.log
sm_run.log
softmax_validation.parquet
train_current_model.py
two_tower_model.py
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930334 tokens left"
}
## Step 30 · agent · 2026-09-26T06:45:56.300Z
### Bash · 2026-09-26T06:45:56.300Z
{
"command": "cd /app/solution; tail -12 base_run.log | head -8; dmesg 2>/dev/null | tail -3"
}
### Tool output · 2026-09-26T06:45:56.350Z
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
/bin/bash: line 3: 8489 Killed python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 4 --negative-objective bce --output production_validation.parquet
real 3m58.491s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14930106 tokens left"
}
## Step 31 · agent · 2026-09-26T06:46:00.900Z
### Thinking unavailable — no text recorded
Both were killed at the same instant (~06:41), when combined memory hit ~8 GB. I'm checking for a container memory limit.
### Bash · 2026-09-26T06:46:01.498Z
{
"command": "cat /sys/fs/cgroup/memory.max /sys/fs/cgroup/memory.peak 2>/dev/null; cat /sys/fs/cgroup/memory.events 2>/dev/null"
}
### Tool output · 2026-09-26T06:46:01.518Z
8589934592
8589934592
low 0
high 0
max 7711
oom 14
oom_kill 3
oom_group_kill 0
sock_throttled 0
### Harness context
{
"type": "total_tokens_reminder",
"text": "14929323 tokens left"
}
## Step 32 · agent · 2026-09-26T06:46:06.718Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The container's 8GB cgroup limit has caused 3 OOM kills, so only one training run can happen at a time. With ~35 minutes left, I'm prioritizing the final refit (shared-softmax, α=1.0, train+val, 8 threads) to secure a submission.
### Bash · 2026-09-26T06:46:06.725Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage submission --public-dir /task --prepared-cache cache_submission.npz --threads 8 --output submission.parquet) > submission_run.log 2>&1 &\nsleep 5; head -3 submission_run.log"
}
### Tool output · 2026-09-26T06:46:11.743Z
(Bash completed with no output)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928853 tokens left"
}
## Step 33 · agent · 2026-09-26T06:46:12.752Z
Meanwhile, drafting the README (I'll fill in the scores later).
### Bash · 2026-09-26T06:46:13.499Z
{
"command": "cd /app/solution; sed -n '/^def load_or_prepare/,/^def build_item_correction/p' train_current_model.py | head -30; grep -n \"VALIDATION_END\\|stage ==\" train_current_model.py | head"
}
### Tool output · 2026-09-26T06:46:13.529Z
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
def build_item_correction(
357: if stage == "validation":
364: history_end = base.VALIDATION_END
522: if stage == "validation"
921: prefixes = ("train",) if stage == "validation" else ("train", "val")
984: base.TRAIN_END if stage == "validation" else base.VALIDATION_END
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928665 tokens left"
}
## Step 34 · agent · 2026-09-26T06:46:15.443Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:46:15.647Z
{
"command": "sleep 100; cd /app/solution; tail -3 submission_run.log; cat /sys/fs/cgroup/memory.current"
}
### Tool output · 2026-09-26T06:47:55.665Z
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
4068872192
### Harness context
{
"type": "total_tokens_reminder",
"text": "14928036 tokens left"
}
## Step 35 · agent · 2026-09-26T06:47:56.769Z
### Bash · 2026-09-26T06:47:56.769Z
{
"command": "sleep 115; cd /app/solution; tail -2 submission_run.log; date"
}
### Tool output · 2026-09-26T06:49:51.819Z
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
Sat Sep 26 06:49:51 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14927831 tokens left"
}
## Step 36 · agent · 2026-09-26T06:49:52.939Z
### Bash · 2026-09-26T06:49:52.939Z
{
"command": "sleep 110; cd /app/solution; tail -2 submission_run.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T06:51:43.004Z
epoch=1/1 train_loss=8.27034 validation_loss=0.53122 pair_accuracy=0.719 train_targets=1,067,606 validation_targets=131,997 train_seconds=212.6 validation_seconds=16.5
restored best validation-BCE checkpoint: epoch=1 loss=0.531223
Sat Sep 26 06:51:42 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14927632 tokens left"
}
## Step 37 · agent · 2026-09-26T06:51:45.347Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T06:51:48.077Z
{
"command": "sleep 30; cd /app/solution; ls -la submission.parquet && python - <<'EOF'\nimport pandas as pd, numpy as np\ns=pd.read_parquet('/app/solution/submission.parquet')\nt=pd.read_parquet('/task/data/target_users.parquet')\nc=np.load('/task/data/test_eligible_candidates.npz')\nprint(s.shape, s.dtypes.to_dict())\nassert set(s.uid)==set(t.uid) and s.uid.nunique()==len(t)\ng=s.groupby('uid')\nassert (g.size()==100).all() and (g.item_id.nunique()==100).all() and (g['rank'].apply(lambda r: sorted(r)==list(range(1,101)))).all()\nrow={u:i for i,u in enumerate(c['uids'])}; col={it:j for j,it in enumerate(c['item_ids'])}\nm=np.unpackbits(c['packed_eligible'],axis=1,count=len(c['item_ids']),bitorder='little')\nok=m[s.uid.map(row).values, s.item_id.map(col).values]\nprint('all eligible', ok.all())\nEOF"
}
### Tool error · 2026-09-26T06:52:18.125Z
Exit code 2
ls: cannot access 'submission.parquet': No such file or directory
### Harness context
{
"type": "total_tokens_reminder",
"text": "14926895 tokens left"
}
## Step 38 · agent · 2026-09-26T06:52:19.313Z
### Bash · 2026-09-26T06:52:19.313Z
{
"command": "sleep 20; cd /app/solution; ls; tail -3 submission_run.log | cut -c1-200"
}
### Tool output · 2026-09-26T06:52:39.355Z
base_run.log
cache_submission.npz
cache_validation.npz
sm_a05.log
sm_run.log
softmax_validation.parquet
submission.parquet
submission_run.log
train_current_model.py
two_tower_model.py
real 6m21.130s
user 32m15.126s
sys 4m14.312s
### 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": "14926749 tokens left"
}
## Step 39 · agent · 2026-09-26T06:52:40.531Z
Submission written. Starting the baseline validation run immediately, then checking the submission's validity while it trains.
### Bash · 2026-09-26T06:52:43.540Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 8 --negative-objective bce --output production_validation.parquet) > base_run.log 2>&1 &\npython - <<'EOF'\nimport pandas as pd, numpy as np\ns=pd.read_parquet('/app/solution/submission.parquet')\nt=pd.read_parquet('/task/data/target_users.parquet')\nc=np.load('/task/data/test_eligible_candidates.npz')\nprint(s.shape, s.dtypes.to_dict())\nassert set(s.uid)==set(t.uid) and s.uid.nunique()==len(t)\ng=s.groupby('uid')\nassert (g.size()==100).all() and (g.item_id.nunique()==100).all() and (g['rank'].apply(lambda r: sorted(r)==list(range(1,101)))).all()\nrow={u:i for i,u in enumerate(c['uids'])}; col={it:j for j,it in enumerate(c['item_ids'])}\nm=np.unpackbits(c['packed_eligible'],axis=1,count=len(c['item_ids']),bitorder='little')\nok=m[s.uid.map(row).values, s.item_id.map(col).values]\nprint('all eligible', ok.all())\nEOF"
}
### Tool output · 2026-09-26T06:52:45.735Z
(845600, 3) {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}
all eligible True
### Harness context
{
"type": "total_tokens_reminder",
"text": "14926038 tokens left"
}
## Step 40 · agent · 2026-09-26T06:52:46.957Z
### Thinking unavailable — no text recorded
Submission is valid: 8,456 users × 100 ranks, and every item is eligible. Writing the README while the baseline trains.
### Write · 2026-09-26T06:52:58.331Z
{
"file_path": "/app/solution/README.md",
"content": "# Made for You two-tower: negative-sampling fix\n\n## Diagnosis\n\nThe production trainer (`--negative-objective bce`) scores each target against\nonly 32 row-private negatives, using sample-weighted BCE:\n\n* 24 negatives are drawn uniformly from the 30k catalog. Most are long-tail\n tracks that the model can separate from almost any target just by popularity\n features, so they add little signal about *which* popular or relevant\n unheard tracks a user prefers. That is the discrimination the discovery\n NDCG@10 actually tests.\n* 8 in-batch negatives carry inverse-propensity weights. The weights are\n renormalised within those 8 columns, so the correction only reshuffles weight\n among the in-batch items. It never corrects the logit for sampling bias\n (no logQ).\n* `corrected_negative_loss` averages over the negatives, so all 32 of them\n together get the same weight as the single positive. Each score is also an\n independent sigmoid, so nothing trains the model to put the target above the\n bulk of the catalog, which is what top-100 retrieval needs.\n\n## Change (`--negative-objective shared-softmax`, now the default)\n\n`score_shared_softmax_block` replaces the per-row BCE with a sampled softmax\nover one negative pool shared by the whole BPTT block (about 6.4k targets):\n\n* Pool = the block's unique targets (in-batch, popularity-distributed hard\n negatives) plus 4,096 uniform catalog draws. That gives about 7k negatives\n per target instead of 32.\n* logQ correction: each logit is reduced by `logq_alpha * log(expected pool\n inclusion)`. For an item that is `1-(1-p_j)^T + M/N`, where `p_j` is the\n smoothed training-target frequency.\n* False-negative filter kept: for each row, the pool masks every known\n training positive of that user (the same packed full-history filter as\n production) and every active like. The row's own target stays as the label.\n* The organic-strong sample weights are unchanged. The loss is weighted CE.\n\nThe data protocol, chronological internal split, GRU user tower, item tower,\nraw-dot-product serving and candidate masking are all unchanged. At request\ntime the model still computes one user vector and scores it against\nprecomputed item vectors by dot product. The old BCE path is still available\nvia `--negative-objective bce` for ablation.\n\n## Public validation (graded NDCG@10, 4,263 users with relevance)\n\n| model | NDCG@10 |\n| --- | --- |\n| production BCE (24 uniform + 8 in-batch) | BASELINE_SCORE |\n| shared softmax, logq_alpha=1.0 | 0.017546 |\n\n## Commands\n\n```sh\n# validation backtest\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --prepared-cache /app/solution/cache_validation.npz \\\n --output /app/solution/softmax_validation.parquet\npython /task/evaluate_public.py /app/solution/softmax_validation.parquet\n\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_submission.npz \\\n --output /app/solution/submission.parquet\n```\n\nNotes: the container has an 8 GB cgroup memory limit, so run one training at a\ntime. Each run takes about 6 minutes on 8 CPU threads.\n"
}
### Tool output · 2026-09-26T06:52:58.399Z
File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14924646 tokens left"
}
## Step 41 · agent · 2026-09-26T06:52:59.680Z
### Bash · 2026-09-26T06:52:59.680Z
{
"command": "sleep 100; cd /app/solution; tail -2 base_run.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T06:54:39.716Z
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
Sat Sep 26 06:54:39 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14924504 tokens left"
}
## Step 42 · agent · 2026-09-26T06:54:40.846Z
### Bash · 2026-09-26T06:54:40.846Z
{
"command": "sleep 115; cd /app/solution; tail -2 base_run.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T06:56:35.890Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=204.7s
Sat Sep 26 06:56:35 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14924306 tokens left"
}
## Step 43 · agent · 2026-09-26T06:56:37.030Z
### Bash · 2026-09-26T06:56:37.030Z
{
"command": "sleep 75; cd /app/solution; ls production_validation.parquet && python /task/evaluate_public.py production_validation.parquet; date"
}
### Tool output · 2026-09-26T06:57:52.095Z
ls: cannot access 'production_validation.parquet': No such file or directory
Sat Sep 26 06:57:52 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14924073 tokens left"
}
## Step 44 · agent · 2026-09-26T06:57:53.428Z
### Bash · 2026-09-26T06:57:53.428Z
{
"command": "sleep 25; cd /app/solution; ls production_validation.parquet && python /task/evaluate_public.py production_validation.parquet; date"
}
### Tool output · 2026-09-26T06:58:21.854Z
production_validation.parquet
{
"model": "production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Sat Sep 26 06:58:21 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14923905 tokens left"
}
## Step 45 · agent · 2026-09-26T06:58:52.813Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The baseline beats my full-logQ softmax model (0.02128 vs 0.01755), so with ~22 minutes left I'll try one more experiment: a hybrid combining production BCE with a 0.25-weighted shared-pool softmax for ranking signal.
### Bash · 2026-09-26T06:58:57.735Z
{
"command": "cd /app/solution; python - <<'EOF'\np='train_current_model.py'\ns=open(p).read()\ns=s.replace(''' logq_alpha: float = 1.0\n''',''' logq_alpha: float = 1.0\n softmax_weight: float = 0.25\n''',1)\nold=''' else:\n statistics = run_bce_block('''\nnew=''' elif config.negative_objective == \"hybrid\":\n # Production BCE plus a weighted shared-pool softmax term.\n negatives, negative_weights = draw_training_negatives(\n positive_items, known_history, liked_items, flat_user_rows,\n known_positive_packed, prepared, config, item_correction,\n generator, device,\n )\n optimizer.zero_grad(set_to_none=True)\n statistics, sequence_gradient = score_target_microbatches(\n model, prepared, config, sequence_vectors, flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items, negatives, negative_weights,\n block[8].reshape(-1)[flat_scored], device, backward=True,\n )\n softmax_loss, softmax_statistics = score_shared_softmax_block(\n model, prepared, config, sequence_vectors, flat_states,\n block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n positive_items, flat_user_rows, known_positive_packed,\n target_log_probabilities, block[8].reshape(-1)[flat_scored],\n generator, device,\n )\n torch.autograd.backward(\n [sequence_vectors, config.softmax_weight * softmax_loss],\n [sequence_gradient, None],\n )\n nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n optimizer.step()\n statistics[\"pair_correct\"] = softmax_statistics[\"pair_correct\"]\n else:\n statistics = run_bce_block('''\nassert old in s\ns=s.replace(old,new,1)\ns=s.replace('choices=(\"shared-softmax\", \"bce\"),','choices=(\"shared-softmax\", \"hybrid\", \"bce\"),',1)\ns=s.replace(''' parser.add_argument(\"--logq-alpha\", type=float, default=Config.logq_alpha)''',''' parser.add_argument(\"--logq-alpha\", type=float, default=Config.logq_alpha)\n parser.add_argument(\n \"--softmax-weight\", type=float, default=Config.softmax_weight\n )''',1)\ns=s.replace(''' logq_alpha=args.logq_alpha,\n''',''' logq_alpha=args.logq_alpha,\n softmax_weight=args.softmax_weight,\n''',1)\nopen(p,'w').write(s)\nEOF\n(time python train_current_model.py --stage validation --public-dir /task --prepared-cache cache_validation.npz --threads 8 --negative-objective hybrid --output hybrid_validation.parquet) > hybrid.log 2>&1 &\nsleep 75; tail -2 /app/solution/hybrid.log | cut -c1-200"
}
### Tool output · 2026-09-26T07:00:12.776Z
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": "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": "14920063 tokens left"
}
## Step 46 · agent · 2026-09-26T07:00:14.319Z
### Thinking unavailable — no text recorded
The hybrid validation run has started. I'm checking that it gets past the first training blocks without crashing.
### Bash · 2026-09-26T07:00:14.943Z
{
"command": "sleep 30; ps aux | grep \"hybrid\" | grep -v grep | wc -l; tail -1 /app/solution/hybrid.log | cut -c1-200; cat /sys/fs/cgroup/memory.current; date"
}
### Tool output · 2026-09-26T07:00:45.062Z
2
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
5533470720
Sat Sep 26 07:00:45 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919736 tokens left"
}
## Step 47 · agent · 2026-09-26T07:00:47.722Z
### Thinking unavailable — no text recorded
Running at 5.5 GB. It should finish around 07:05. If it beats 0.02128 I'll refit the hybrid; otherwise I'll refit the production BCE, so the submission is at least as good as production.
### Bash · 2026-09-26T07:00:48.016Z
{
"command": "sleep 118; tail -1 /app/solution/hybrid.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T07:02:46.058Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 07:02:46 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919414 tokens left"
}
## Step 48 · agent · 2026-09-26T07:02:47.219Z
### Bash · 2026-09-26T07:02:47.219Z
{
"command": "sleep 110; tail -1 /app/solution/hybrid.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T07:04:37.260Z
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
Sat Sep 26 07:04:37 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14919246 tokens left"
}
## Step 49 · agent · 2026-09-26T07:04:40.866Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T07:04:41.200Z
{
"command": "sleep 90; grep \"epoch=1\" /app/solution/hybrid.log | cut -c1-220; date"
}
### Tool output · 2026-09-26T07:06:11.277Z
epoch=1/1 batches=250 targets=987,417 batch_bce=0.543711 running_bce=0.542426 elapsed=379.0s
epoch=1/1 train_loss=0.54073 validation_loss=0.49740 pair_accuracy=0.724 train_targets=1,052,953 validation_targets=130,103 train_seconds=404.9 validation_seconds=16.0
restored best validation-BCE checkpoint: epoch=1 loss=0.497405
Sat Sep 26 07:06:11 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14918835 tokens left"
}
## Step 50 · agent · 2026-09-26T07:06:12.932Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-26T07:06:13.221Z
{
"command": "grep \"train_loss\" /app/solution/base_run.log | cut -c1-200; sleep 35; cd /app/solution; ls hybrid_validation.parquet && python /task/evaluate_public.py hybrid_validation.parquet"
}
### Tool error · 2026-09-26T07:06:48.303Z
Exit code 2
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=218.9 validation_seconds=15.5
ls: cannot access 'hybrid_validation.parquet': No such file or directory
### Harness context
{
"type": "total_tokens_reminder",
"text": "14918449 tokens left"
}
## Step 51 · agent · 2026-09-26T07:06:49.741Z
### Bash · 2026-09-26T07:06:49.741Z
{
"command": "sleep 15; cd /app/solution; ls hybrid_validation.parquet && python /task/evaluate_public.py hybrid_validation.parquet; date"
}
### Tool output · 2026-09-26T07:07:04.800Z
ls: cannot access 'hybrid_validation.parquet': No such file or directory
Sat Sep 26 07:07:04 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14918198 tokens left"
}
## Step 52 · agent · 2026-09-26T07:07:06.070Z
### Bash · 2026-09-26T07:07:06.070Z
{
"command": "sleep 20; cd /app/solution; ls hybrid_validation.parquet && python /task/evaluate_public.py hybrid_validation.parquet; date"
}
### Tool output · 2026-09-26T07:07:26.123Z
ls: cannot access 'hybrid_validation.parquet': No such file or directory
Sat Sep 26 07:07:26 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": "14918021 tokens left"
}
## Step 53 · agent · 2026-09-26T07:07:27.614Z
### Bash · 2026-09-26T07:07:27.614Z
{
"command": "sleep 15; cd /app/solution; tail -2 hybrid.log | cut -c1-100; ls hybrid_validation.parquet && python /task/evaluate_public.py hybrid_validation.parquet; date"
}
### Tool output · 2026-09-26T07:07:45.999Z
user 45m53.165s
sys 5m56.854s
hybrid_validation.parquet
{
"model": "hybrid_validation.parquet",
"scores": {
"quality_ndcg@10": 0.0177,
"quality_users": 4263
}
}
Sat Sep 26 07:07:45 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14917781 tokens left"
}
## Step 54 · agent · 2026-09-26T07:07:50.554Z
### Thinking unavailable — no text recorded
### Recorded reasoning
The hybrid scored 0.0177, still below production's 0.02128, so the BCE objective remains the best model. With time running low, I'm refitting it on train+val, writing to a temp file first to keep the current valid submission intact until the new one is ready.
### Bash · 2026-09-26T07:07:50.562Z
{
"command": "cd /app/solution; (time python train_current_model.py --stage submission --public-dir /task --prepared-cache cache_submission.npz --threads 8 --negative-objective bce --output submission_bce.parquet) > submission_bce.log 2>&1 &\nsleep 2; mv submission.parquet softmax_submission.parquet; cp softmax_submission.parquet submission.parquet; ls"
}
### Tool output · 2026-09-26T07:07:52.597Z
README.md
base_run.log
cache_submission.npz
cache_validation.npz
hybrid.log
hybrid_validation.parquet
production_validation.parquet
sm_a05.log
sm_run.log
softmax_submission.parquet
softmax_validation.parquet
submission.parquet
submission_bce.log
submission_run.log
train_current_model.py
two_tower_model.py
### Harness context
{
"type": "total_tokens_reminder",
"text": "14917258 tokens left"
}
## Step 55 · agent · 2026-09-26T07:07:54.389Z
### Thinking unavailable — no text recorded
While it runs, I'm setting the script's default objective back to `bce` so the saved implementation matches the artifact, and rewriting the README with the actual results.
### Bash · 2026-09-26T07:07:55.459Z
{
"command": "cd /app/solution; sed -i 's/ negative_objective: str = \"shared-softmax\"/ negative_objective: str = \"bce\"/' train_current_model.py; grep -n 'negative_objective: str' train_current_model.py; python -c \"import ast;ast.parse(open('train_current_model.py').read())\""
}
### Tool output · 2026-09-26T07:07:55.583Z
63: negative_objective: str = "bce"
### Write · 2026-09-26T07:08:06.936Z
{
"file_path": "/app/solution/README.md",
"content": "# Made for You two-tower: negative-sampling investigation\n\n## Outcome (honest summary)\n\nNeither negative-sampling change I tested beat production on the public\nbacktest in the time available. The final `submission.parquet` is the\nproduction BCE objective (`--negative-objective bce`, now the script's\ndefault again), refit on `train_* + val_*`. It is the best model measured.\nProduction does not clear the launch gate, and this attempt did not either.\n\n## Public validation (graded NDCG@10, 4,263 users with relevance)\n\n| objective | NDCG@10 |\n| --- | --- |\n| production BCE: 24 uniform + 8 propensity-weighted in-batch (**submitted**) | **0.02128** |\n| shared-pool sampled softmax, logQ alpha=1.0 | 0.01755 |\n| hybrid: production BCE + 0.25 × shared-pool softmax | 0.01770 |\n\nAll runs use 1 epoch, seed 29, 8 CPU threads and the same prepared cache.\n\n## Diagnosis of the production negative sampler\n\n* It uses 32 row-private negatives per target. 24 are uniform over the 30k\n catalog, so they are mostly long-tail tracks that popularity features alone\n separate. They are easy negatives.\n* The 8 in-batch negatives' inverse-propensity weights are renormalised within\n those 8 columns. The correction only reshuffles weight among in-batch items;\n it is not a logit (logQ) correction.\n* `corrected_negative_loss` averages the negatives, so the whole negative set\n gets the same weight as one positive in an independent-sigmoid loss.\n\n## What I tried (`score_shared_softmax_block`)\n\n* A sampled softmax over one pool shared by the whole BPTT block (about 6.4k\n targets). The pool is the block's unique targets plus 4,096 uniform draws,\n with a logQ correction on expected pool inclusion.\n* The same per-user full-history known-positive filter and active-like masks\n as production.\n* The same organic-strong target weights.\n* The serving contract is unchanged: one user vector, precomputed item\n vectors, raw dot product.\n\nSoftmax alone (`--negative-objective shared-softmax`) reached a training CE of\nonly 8.27, against ln(pool) ≈ 8.85, after one epoch. It under-learns at this\nbudget and appears to lose the popularity prior that this organic-discovery\nmetric rewards. Adding it as a 0.25-weighted auxiliary term to production BCE\n(`--negative-objective hybrid`) also hurt NDCG and nearly doubled training\ntime.\n\nNext steps I did not have time for:\n\n* Partial logQ (`--logq-alpha 0.5` / `0`). The run was OOM-killed: the\n container has an 8 GB cgroup limit, so only one training can run at a time.\n* Fewer uniform draws, so hard in-batch negatives dominate.\n* A smaller softmax weight.\n* Popularity-proportional (pop^0.5) negatives inside the BCE.\n\n## Commands\n\n```sh\n# public backtest (production objective)\npython /app/solution/train_current_model.py --stage validation --public-dir /task \\\n --prepared-cache /app/solution/cache_validation.npz --negative-objective bce \\\n --output /app/solution/production_validation.parquet\npython /task/evaluate_public.py /app/solution/production_validation.parquet\n\n# final refit on train + val (the submitted artifact)\npython /app/solution/train_current_model.py --stage submission --public-dir /task \\\n --prepared-cache /app/solution/cache_submission.npz --negative-objective bce \\\n --output /app/solution/submission.parquet\n```\n\nEach run takes about 6 minutes on 8 threads (the hybrid takes about 8).\n"
}
### Tool output · 2026-09-26T07:08:06.965Z
The file /app/solution/README.md has been updated successfully. (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14915497 tokens left"
}
## Step 56 · agent · 2026-09-26T07:08:08.160Z
### Bash · 2026-09-26T07:08:08.160Z
{
"command": "sleep 100; tail -1 /app/solution/submission_bce.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T07:09:48.212Z
full-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750
Sat Sep 26 07:09:48 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14915287 tokens left"
}
## Step 57 · agent · 2026-09-26T07:09:49.374Z
### Bash · 2026-09-26T07:09:49.374Z
{
"command": "sleep 115; grep \"train_loss\" /app/solution/submission_bce.log | cut -c1-200; date"
}
### Tool output · 2026-09-26T07:11:44.447Z
Sat Sep 26 07:11:44 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14915115 tokens left"
}
## Step 58 · agent · 2026-09-26T07:11:46.296Z
### Bash · 2026-09-26T07:11:46.296Z
{
"command": "sleep 90; grep \"train_loss\" /app/solution/submission_bce.log | cut -c1-200; ls /app/solution/submission_bce.parquet; date"
}
### Tool output · 2026-09-26T07:13:16.344Z
epoch=1/1 train_loss=0.53835 validation_loss=0.49795 pair_accuracy=0.755 train_targets=1,067,606 validation_targets=131,997 train_seconds=221.2 validation_seconds=15.6
ls: cannot access '/app/solution/submission_bce.parquet': No such file or directory
Sat Sep 26 07:13:16 UTC 2026
### Harness context
{
"type": "total_tokens_reminder",
"text": "14914964 tokens left"
}
## Step 59 · agent · 2026-09-26T07:13:17.704Z
### Bash · 2026-09-26T07:13:17.704Z
{
"command": "sleep 40; cd /app/solution; ls submission_bce.parquet && tail -3 submission_bce.log | head -1"
}
### Tool output · 2026-09-26T07:13:57.748Z
submission_bce.parquet
real 5m32.852s
### Harness context
{
"type": "total_tokens_reminder",
"text": "14914712 tokens left"
}
## Step 60 · agent · 2026-09-26T07:13:58.814Z
The refit is done. Validating it, then swapping it in as the submission.
### Bash · 2026-09-26T07:14:02.163Z
{
"command": "cd /app/solution; python - <<'EOF'\nimport pandas as pd, numpy as np\ns=pd.read_parquet('/app/solution/submission_bce.parquet')\nt=pd.read_parquet('/task/data/target_users.parquet')\nc=np.load('/task/data/test_eligible_candidates.npz')\nassert set(s.uid)==set(t.uid) and s.uid.nunique()==len(t) and len(s)==100*len(t)\ng=s.groupby('uid')\nassert (g.item_id.nunique()==100).all() and (g['rank'].min()==1).all() and (g['rank'].max()==100).all() and (g['rank'].nunique()==100).all()\nrow={u:i for i,u in enumerate(c['uids'])}; col={it:j for j,it in enumerate(c['item_ids'])}\nm=np.unpackbits(c['packed_eligible'],axis=1,count=len(c['item_ids']),bitorder='little')\nassert m[s.uid.map(row).values, s.item_id.map(col).values].all()\nassert np.isfinite(s[['uid','item_id','rank']].to_numpy()).all()\nprint('valid', s.shape, s.dtypes.to_dict())\nEOF\ncp submission_bce.parquet submission.parquet && rm -f cache_validation.npz.tmp; ls -la submission.parquet; test -L submission.parquet || echo \"regular file\""
}
### Tool output · 2026-09-26T07:14:04.111Z
valid (845600, 3) {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}
-rw-r--r-- 1 agent agent 1212474 Sep 26 07:14 submission.parquet
regular file
### Harness context
{
"type": "total_tokens_reminder",
"text": "14914117 tokens left"
}
## Step 61 · agent · 2026-09-26T07:14:11.262Z
### Thinking unavailable — no text recorded
I didn't manage to improve on the production model. The submission I shipped is the unchanged production training objective, refit on train + validation data. It's the best model I measured, but that means it still doesn't clear the launch gate.
**Public validation, graded NDCG@10 (4,263 users with a relevant item):**
| Training objective | NDCG@10 |
|---|---|
| Production: 24 random + 8 in-batch negatives per target (**submitted**) | **0.02128** |
| My change: many shared negatives per block with popularity correction | 0.01755 |
| Production plus my change as a secondary term (weight 0.25) | 0.01770 |
**Diagnosis of the current negative sampling:**
- Each target gets only 32 negatives, and 24 of them are drawn uniformly from the 30k catalog. Most are obscure tracks the model can rule out on popularity alone, so they teach it little about what a given user prefers.
- The 8 in-batch negatives get popularity-correction weights, but those weights are rescaled among just those 8. They only shift weight between them and never correct for how often popular items get sampled.
- The loss averages over the negatives, so all 32 together count the same as the single positive.
**What I tried:** instead of 32 negatives per target, every target in a training block shares one pool of about 7,000 negatives: the block's other targets plus 4,096 random tracks. I added a sampling-bias correction and kept the same filtering of each user's known positives and likes. The serving path is unchanged: one user vector, precomputed item vectors, dot-product score.
On its own this learned slowly in one epoch; its loss ended only slightly below random guessing. It also seems to lose the popularity signal this metric rewards. As a secondary term alongside the production loss it still scored worse, and training took almost twice as long.
**What limited the attempt:** the container has an 8 GB memory limit. Three runs were killed when I ran trainings in parallel, including a planned test with a weaker popularity correction, so everything after that ran one at a time. Next I'd test the weaker correction, fewer random negatives, a smaller weight on the new term, and popularity-weighted negatives inside the existing loss.
**Deliverables:**
- `/app/solution/submission.parquet` is a regular file and passes every format rule: 8,456 users × 100 unique items, ranks 1–100 once each, all items eligible.
- `/app/solution/train_current_model.py` defaults to the production objective (`--negative-objective bce`). My variants are still available as `shared-softmax` and `hybrid`.
- `/app/solution/README.md` has the diagnosis, the score table, and the exact commands.
- `softmax_submission.parquet` is a test ranking from the shared-negatives model (not submitted).
- Two cached datasets totalling about 1 GB (`cache_validation.npz`, `cache_submission.npz`) are also in `/app/solution/`. They make reruns faster and can be deleted.
## Recorded session usage
[
{
"type": "cost-state",
"sessionId": "9aa27726-e2fb-4f7d-88f2-a0bb79cb0456",
"totalCostUSD": 1.9773122,
"totalAPIDuration": 290420,
"totalAPIDurationWithoutRetries": 290304,
"totalToolDuration": 2544087,
"totalLinesAdded": 122,
"totalLinesRemoved": 48,
"totalDuration": 2835569,
"startTime": 1790404018879,
"modelUsage": {
"claude-opus-5-5": {
"inputTokens": 120,
"outputTokens": 29038,
"thinkingTokens": 10519,
"cacheReadInputTokens": 3998401,
"cacheCreationInputTokens": 74549,
"webSearchRequests": 0,
"costUSD": 1.9773122
}
},
"hasUnknownModelCost": false
}
]