yambda-discovery-ranking-two-tower-case-1 / gemini-3-8-flash-high-case1-fixed-r03-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:36:40.003Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───.keep
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
├───current_model/
│ ├───train_current_model.py
│ └───two_tower_model.py
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T05:36:40.048Z
# 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.
## Step 3 · agent · 2026-09-29T05:36:44.377Z
### Recorded reasoning summary
Analyzing Prompt Parameters
I'm currently dissecting the prompt's intricacies, focusing on the precise output format, word count limitations, and the imperative for first-person narration. My objective is to ensure strict adherence to all specified constraints for the user.
### update_topic · 2026-09-29T05:36:44.377Z
{
"strategic_intent": "Explore the workspace and codebase to understand the baseline model, data, and evaluation pipeline.",
"title": "Exploring Baseline and Environment",
"summary": "Starting investigation of the two-tower music discovery model. We will inspect the directory structure, scripts in /task/, examine train_current_model.py and two_tower_model.py, run the public baseline evaluation, and diagnose negative sampling limitations."
}
### Tool output · 2026-09-29T05:36:44.386Z
Current topic: "Exploring Baseline and Environment"
Topic summary: Starting investigation of the two-tower music discovery model. We will inspect the directory structure, scripts in /task/, examine train_current_model.py and two_tower_model.py, run the public baseline evaluation, and diagnose negative sampling limitations.
Strategic Intent: Explore the workspace and codebase to understand the baseline model, data, and evaluation pipeline.
## Step 4 · agent · 2026-09-29T05:36:46.620Z
### Recorded reasoning summary
Examining Task Directory
I'm currently reviewing the contents of the `/task/` directory. My focus is on the files related to public evaluations and ranking, specifically `evaluate_public.py` and `ranking_evaluation.py`. I'm also taking note of the training scripts for the current model.
### run_shell_command · 2026-09-29T05:36:46.620Z
{
"description": "List files in /task and /task/current_model",
"command": "ls -la /task /task/current_model"
}
### Tool output · 2026-09-29T05:36:46.647Z
Output: /task:
total 40
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 .
drwxr-xr-x 1 root root 4096 Sep 29 05:36 ..
-r--r--r-- 1 root root 0 Sep 29 05:31 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 data
-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt
/task/current_model:
total 104
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 .
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 ..
-r--r--r-- 1 root root 80058 Sep 29 05:31 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 05:31 two_tower_model.py
Process Group PGID: 1612
## Step 5 · agent · 2026-09-29T05:36:48.544Z
### run_shell_command · 2026-09-29T05:36:48.544Z
{
"description": "Check CPU and memory resources",
"command": "lscpu; free -h"
}
### Tool output · 2026-09-29T05:36:48.564Z
Output: Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7R13 Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 1
BogoMIPS: 5299.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 2 MiB (4 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Old microcode: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsa: Mitigation; Clear CPU buffers
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1621
## Step 6 · agent · 2026-09-29T05:36:50.344Z
### run_shell_command · 2026-09-29T05:36:50.344Z
{
"description": "Check available RAM",
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable\""
}
### Tool output · 2026-09-29T05:36:50.358Z
Output: MemTotal: 15988968 kB
MemAvailable: 14506048 kB
Process Group PGID: 1625
## Step 7 · agent · 2026-09-29T05:36:52.182Z
### run_shell_command · 2026-09-29T05:36:52.182Z
{
"command": "python -c \"import torch; print(torch.__version__, torch.get_num_threads())\"",
"description": "Check PyTorch version and thread count"
}
### Tool output · 2026-09-29T05:36:55.106Z
Output: 2.8.0+cpu 4
Process Group PGID: 1629
## Step 8 · agent · 2026-09-29T05:37:05.295Z
### read_file · 2026-09-29T05:37:05.295Z
{
"file_path": "/task/evaluate_public.py",
"start_line": 1
}
### Tool output · 2026-09-29T05:37:05.300Z
#!/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,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
## Step 9 · agent · 2026-09-29T05:37:06.763Z
### read_file · 2026-09-29T05:37:06.763Z
{
"end_line": 150,
"file_path": "/task/ranking_evaluation.py",
"start_line": 1
}
### Tool output · 2026-09-29T05:37:06.767Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
## Step 10 · agent · 2026-09-29T05:37:08.493Z
### read_file · 2026-09-29T05:37:08.493Z
{
"file_path": "/task/ranking_evaluation.py",
"start_line": 151,
"end_line": 232
}
### Tool output · 2026-09-29T05:37:08.496Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.
--- FILE CONTENT (truncated) ---
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
## Step 11 · agent · 2026-09-29T05:37:11.085Z
### Recorded reasoning summary
Calculating NDCG Metrics
I'm currently refining the NDCG calculation, focusing on the top 10 items and incorporating the gain function of `2^grade - 1`. The discount factor is being applied as `1 / log2(r + 1)`. Users without relevant items in validation are excluded from the average, and only those with at least one eligible relevant event in validation are included in the scoring.
### read_file · 2026-09-29T05:37:11.085Z
{
"file_path": "/task/current_model/two_tower_model.py",
"end_line": 150,
"start_line": 1
}
### Tool output · 2026-09-29T05:37:11.090Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""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
## Step 12 · agent · 2026-09-29T05:37:13.441Z
### read_file · 2026-09-29T05:37:13.441Z
{
"file_path": "/task/current_model/two_tower_model.py",
"end_line": 300,
"start_line": 151
}
### Tool output · 2026-09-29T05:37:13.445Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 151-300 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 301.
--- FILE CONTENT (truncated) ---
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,
## Step 13 · agent · 2026-09-29T05:37:15.230Z
### read_file · 2026-09-29T05:37:15.230Z
{
"end_line": 434,
"start_line": 301,
"file_path": "/task/current_model/two_tower_model.py"
}
### Tool output · 2026-09-29T05:37:15.234Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 301-434 of 434 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 435.
--- FILE CONTENT (truncated) ---
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
## Step 14 · agent · 2026-09-29T05:37:17.559Z
### grep_search · 2026-09-29T05:37:17.559Z
{
"pattern": "^(class|def) ",
"dir_path": "/task/current_model"
}
### Tool output · 2026-09-29T05:37:17.572Z
Found 58 matches for pattern "^(class|def) " in path "/task/current_model":
---
File: train_current_model.py
L41: class Config:
L76: class CandidateSet:
L99: class SequenceData:
L127: class TargetPartition:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
L27: class TowerConfig:
L49: class ListenEvents:
L57: class ExplicitEvents:
L65: def log(message: str) -> None:
L69: def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
L90: def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
L106: def iter_listen_users(
L157: def merge_listen_streams(
L175: def load_explicit_events(
L228: def empty_explicit_events() -> ExplicitEvents:
L238: def pack_active_items(
L251: def load_inference_state(
L284: class SequentialTwoTower(nn.Module):
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---
## Step 15 · agent · 2026-09-29T05:37:20.525Z
### Recorded reasoning summary
Reviewing Train Script
I'm currently examining the function names within `train_current_model.py`. Specifically, I'm looking at `Config`, `build_item_correction`, `build_known_positive_filter`, `invalid_full_history_negatives`, and `draw_full_history_filtered_negatives` to understand their roles in the training process.
### read_file · 2026-09-29T05:37:20.525Z
{
"end_line": 100,
"file_path": "/task/current_model/train_current_model.py",
"start_line": 41
}
### Tool output · 2026-09-29T05:37:20.529Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 41-100 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.
--- FILE CONTENT (truncated) ---
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
@classmethod
def read(cls, path: Path) -> "CandidateSet":
with np.load(path, allow_pickle=False) as artifact:
result = cls(
uids=artifact["uids"].astype(np.int64, copy=True),
item_ids=artifact["item_ids"].astype(np.int64, copy=True),
packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
)
expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
if result.packed.shape != expected:
raise ValueError(
f"invalid candidate artifact shape {result.packed.shape}; "
f"expected {expected}"
)
return result
@dataclass(frozen=True)
class SequenceData:
item_ids: np.ndarray
## Step 16 · agent · 2026-09-29T05:37:23.226Z
### Recorded reasoning summary
Testing Baseline Reproducibility
I'm starting by executing the baseline validation reproduction command provided. My immediate focus is to capture its execution time, memory consumption, and the resulting score. This initial step will establish a foundational understanding of the process.
### run_shell_command · 2026-09-29T05:37:23.226Z
{
"command": "mkdir -p /app/solution",
"description": "Ensure /app/solution exists"
}
### Tool output · 2026-09-29T05:37:23.237Z
Output: (empty)
Process Group PGID: 1641
## Step 17 · agent · 2026-09-29T05:37:25.655Z
### run_shell_command · 2026-09-29T05:37:25.655Z
{
"command": "time python /task/current_model/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --output /app/solution/production_validation.parquet",
"description": "Run baseline validation model training and inference"
}
### Tool output · 2026-09-29T05:43:30.836Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
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
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.1s
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=211.1 validation_seconds=15.6
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/production_validation.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 360.854,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "organic-strong"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.539838046260986,
"positive_logit": 0.7427799582740985,
"negative_logit": -0.6222247031655147,
"pair_accuracy": 0.7547733612041563,
"targets": 1052953.0,
"target_weight_sum": 632551.0,
"batches": 266.0,
"seconds": 211.101639062,
"validation_loss": 0.496636221768594,
"validation_targets": 130103.0,
"validation_target_weight_sum": 78769.25,
"validation_seconds": 15.603404805000082
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.603016972541809,
"query_norm_std": 0.7697497010231018,
"item_norm_mean": 1.7570281028747559,
"item_norm_std": 0.8279485702514648
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
real 6m5.168s
user 30m39.551s
sys 1m39.579s
Process Group PGID: 1644
## Step 18 · agent · 2026-09-29T05:43:36.957Z
### run_shell_command · 2026-09-29T05:43:36.957Z
{
"description": "Evaluate baseline validation ranking",
"command": "python /task/evaluate_public.py /app/solution/production_validation.parquet"
}
### Tool output · 2026-09-29T05:43:40.349Z
Output: {
"model": "/app/solution/production_validation.parquet",
"scores": {
"quality_ndcg@10": 0.02128,
"quality_users": 4263
}
}
Process Group PGID: 1705
## Step 19 · agent · 2026-09-29T05:43:45.611Z
### Recorded reasoning summary
Analyzing Initial Retrieval Quality
The current baseline retrieval quality is quite low, with a quality_ndcg@10 of 0.02128 across over 4000 users. My immediate focus is on understanding this performance and exploring avenues for significant improvement.
### Recorded reasoning summary
Investigating Negative Sampling
My current investigation centers on the training objective's handling of negative examples. I'm diagnosing limitations within the existing negative-sampling strategy, aiming to enhance it while adhering to the CPU budget and validating my findings using the public backtest.
### Recorded reasoning summary
Refining Candidate Generation
I'm now focusing on refining the candidate generation process, specifically exploring how to better leverage the provided preference state data. My aim is to enrich the candidate pool, moving beyond a simple top-k approach and ensuring better alignment with user preferences for the final ranking submission.
### update_topic · 2026-09-29T05:43:45.611Z
{
"summary": "Baseline reproduction completed with quality_ndcg@10 = 0.02128 in 6 minutes. Now we transition into a deep code audit of train_current_model.py and two_tower_model.py, specifically focusing on negative sampling, negative filtering, loss formulation, and candidate eligibility.",
"strategic_intent": "Analyze the training objective, negative sampling strategy, and loss formulation in train_current_model.py.",
"title": "Diagnosing Negative Sampling and Training Objective"
}
### Tool output · 2026-09-29T05:43:45.614Z
Current topic: "Diagnosing Negative Sampling and Training Objective"
Topic summary: Baseline reproduction completed with quality_ndcg@10 = 0.02128 in 6 minutes. Now we transition into a deep code audit of train_current_model.py and two_tower_model.py, specifically focusing on negative sampling, negative filtering, loss formulation, and candidate eligibility.
Strategic Intent: Analyze the training objective, negative sampling strategy, and loss formulation in train_current_model.py.
## Step 20 · agent · 2026-09-29T05:43:52.945Z
### Recorded reasoning summary
Reviewing model code
I'm examining the contents of the `/task/current_model/` directory. It appears to contain two Python files: `train_current_model.py` and `two_tower_model.py`. My next step is to copy these to `/app/solution/` for modification.
### run_shell_command · 2026-09-29T05:43:52.945Z
{
"description": "Copy baseline scripts to /app/solution/",
"command": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/"
}
### Tool output · 2026-09-29T05:43:52.965Z
Output: (empty)
Process Group PGID: 1725
## Step 21 · agent · 2026-09-29T05:43:58.114Z
### Recorded reasoning summary
Analyzing Training Logic
I'm currently dissecting `train_current_model.py`, specifically scrutinizing the components responsible for negative sampling and filtering. My focus is on `build_item_correction`, `build_known_positive_filter`, and `invalid_full_history_negatives` to understand their operational nuances.
### read_file · 2026-09-29T05:43:58.114Z
{
"file_path": "/app/solution/train_current_model.py",
"end_line": 760,
"start_line": 525
}
### Tool output · 2026-09-29T05:43:58.123Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 525-760 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 761.
--- FILE CONTENT (truncated) ---
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(
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,
)
## Step 22 · agent · 2026-09-29T05:44:00.370Z
### read_file · 2026-09-29T05:44:00.370Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 760,
"end_line": 950
}
### Tool output · 2026-09-29T05:44:00.376Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 760-950 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 951.
--- FILE CONTENT (truncated) ---
)
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,
) -> np.ndarray:
"""Return the discarded prefix before the retained chronological suffix."""
training_region_ends = total_targets - validation_counts
window_starts = training_region_ends - training_counts
if (
np.any(window_starts < 0)
or np.any(training_counts < 0)
or np.any(validation_counts < 0)
):
raise RuntimeError("chronological target partition produced negative counts")
if not np.array_equal(
window_starts + training_counts + validation_counts, total_targets
):
raise RuntimeError("chronological target window is not a final contiguous suffix")
return window_starts
def chronological_target_partition(
prepared: SequenceData,
validation_fraction: float,
max_train_targets_per_user: int,
max_validation_targets_per_user: int,
) -> TargetPartition:
"""Select a recent contiguous window with a final out-of-time BCE suffix."""
totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
validation = np.floor(totals * validation_fraction).astype(np.int64)
validation[(totals >= 2) & (validation == 0)] = 1
if max_validation_targets_per_user:
validation = np.minimum(validation, max_validation_targets_per_user)
history_training = totals - validation
training = history_training.copy()
if max_train_targets_per_user:
training = np.minimum(training, max_train_targets_per_user)
window_starts = retained_target_window_starts(totals, validation, training)
return TargetPartition(
window_starts=window_starts,
training_counts=training,
validation_counts=validation,
history_training_counts=history_training,
)
def load_organic_like_outcomes(
public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
"""Load mapped organic-like timestamps from public fitting history."""
prefixes = ("train",) if stage == "validation" else ("train", "val")
frames = [
pd.read_parquet(
public / "data" / f"{prefix}_likes.parquet",
columns=["uid", "timestamp", "item_id", "is_organic"],
)
for prefix in prefixes
]
likes = pd.concat(frames, ignore_index=True)
likes = likes[likes.is_organic == 1].copy()
mapped = pd.Index(item_ids).get_indexer(
likes.item_id.to_numpy(dtype=np.int64)
)
likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
nested: dict[int, dict[int, list[int]]] = {}
for raw_uid, raw_timestamp, raw_item in likes.itertuples(
index=False, name=None
):
nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
int(raw_timestamp)
)
result = {
uid: {
item: np.asarray(timestamps, dtype=np.int64)
for item, timestamps in items.items()
}
for uid, items in nested.items()
}
log(
f"loaded organic-like outcomes: rows={len(likes):,} "
f"users={len(result):,}"
## Step 23 · agent · 2026-09-29T05:44:02.208Z
### read_file · 2026-09-29T05:44:02.208Z
{
"start_line": 951,
"end_line": 1150,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:44:02.216Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 951-1150 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1151.
--- FILE CONTENT (truncated) ---
)
return result
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 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]
## Step 24 · agent · 2026-09-29T05:44:04.812Z
### read_file · 2026-09-29T05:44:04.812Z
{
"start_line": 1145,
"file_path": "/app/solution/train_current_model.py",
"end_line": 1350
}
### Tool output · 2026-09-29T05:44:04.818Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1145-1350 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1351.
--- FILE CONTENT (truncated) ---
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),
## Step 25 · agent · 2026-09-29T05:44:09.617Z
### read_file · 2026-09-29T05:44:09.617Z
{
"end_line": 1600,
"start_line": 1350,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:44:09.623Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1350-1600 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1601.
--- FILE CONTENT (truncated) ---
"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,
prepared: SequenceData,
config: Config,
target_weights: np.ndarray,
window_starts: np.ndarray,
training_counts: np.ndarray,
validation_counts: np.ndarray,
item_correction: torch.Tensor,
known_positive_packed: np.ndarray | None,
device: torch.device,
) -> dict[str, float]:
"""Measure deterministic BCE on each user's chronological target suffix."""
model.eval()
totals = training_counts + validation_counts
users_with_validation = np.flatnonzero(validation_counts > 0)
ordered = users_with_validation[
np.argsort(totals[users_with_validation], kind="stable")
]
groups = [
ordered[start : start + config.user_batch_size]
for start in range(0, len(ordered), config.user_batch_size)
]
generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
total_loss = 0.0
total_targets = 0
total_weight = 0.0
started = time.monotonic()
for group_number, users in enumerate(groups, start=1):
group_totals = totals[users]
group_starts = training_counts[users]
hidden = torch.zeros(
1, len(users), config.embedding_dim, device=device
)
max_targets = int(group_totals.max())
for start in range(0, max_targets, config.bptt_steps):
steps = min(config.bptt_steps, max_targets - start)
block = build_dense_block(
prepared,
target_weights,
users,
window_starts[users],
group_totals,
group_starts,
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)
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,
)
statistics, _ = 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=False,
)
total_loss += statistics["loss_sum"]
total_targets += len(positive_items)
total_weight += statistics["weight_sum"]
if group_number % 25 == 0:
release_accelerator_cache(device)
return {
"validation_loss": total_loss / max(total_weight, 1.0e-12),
"validation_targets": float(total_targets),
"validation_target_weight_sum": total_weight,
"validation_seconds": time.monotonic() - started,
}
def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
return {
name: value.detach().cpu().clone()
for name, value in model.state_dict().items()
}
def save_epoch_checkpoint(
checkpoint_dir: Path,
epoch: int,
model_state: dict[str, torch.Tensor],
config: Config,
diagnostics: dict[str, float],
optimizer: torch.optim.Optimizer,
generator: torch.Generator,
) -> Path:
checkpoint_dir.mkdir(parents=True, exist_ok=True)
path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
torch.save(
{
"epoch": epoch,
"config": asdict(config),
"diagnostics": diagnostics,
"model_state": model_state,
"optimizer_state": optimizer.state_dict(),
"generator_state": generator.get_state(),
},
path,
)
log(f"saved epoch checkpoint: {path}")
return path
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,
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(
## Step 26 · agent · 2026-09-29T05:44:11.492Z
### read_file · 2026-09-29T05:44:11.492Z
{
"end_line": 1950,
"start_line": 1780,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:44:11.499Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1780-1950 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1951.
--- FILE CONTENT (truncated) ---
@torch.inference_mode()
def inference_queries(
model: base.SequentialTwoTower,
prepared: SequenceData,
config: Config,
device: torch.device,
) -> torch.Tensor:
sequence_rows = prepared.inference_user_rows
lengths = np.where(
sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
)
ordered = np.argsort(lengths, kind="stable")
result = torch.zeros(
len(sequence_rows), config.embedding_dim, dtype=torch.float32
)
for group_start in range(0, len(ordered), config.user_batch_size):
output_rows = ordered[group_start : group_start + config.user_batch_size]
rows = sequence_rows[output_rows]
group_lengths = lengths[output_rows]
hidden = torch.zeros(
1, len(output_rows), config.embedding_dim, device=device
)
for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
steps = min(
config.bptt_steps,
int(group_lengths.max(initial=0)) - start,
)
items = np.full(
(len(output_rows), steps),
len(prepared.item_ids),
dtype=np.uint16,
)
features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
counts = np.zeros(len(output_rows), dtype=np.int32)
for local_row, sequence_row in enumerate(rows):
if sequence_row < 0:
continue
count = min(steps, max(int(group_lengths[local_row]) - start, 0))
if not count:
continue
offset = int(prepared.offsets[sequence_row])
selected = slice(offset + start, offset + start + count)
items[local_row, :count] = prepared.sequence_items[selected]
features[local_row, :count] = prepared.event_features[selected]
counts[local_row] = count
outputs, _ = event_sequence(
model,
torch.from_numpy(items.astype(np.int64)).to(device),
torch.from_numpy(features.astype(np.float32)).to(device),
hidden,
)
next_rows: list[torch.Tensor] = []
for local_row, count in enumerate(counts):
next_rows.append(
outputs[local_row, count - 1]
if count
else hidden[0, local_row]
)
hidden = torch.stack(next_rows).unsqueeze(0)
liked_items = torch.from_numpy(
prepared.inference_liked_items[output_rows].astype(np.int64)
).to(device)
disliked_items = torch.from_numpy(
prepared.inference_disliked_items[output_rows].astype(np.int64)
).to(device)
queries = user_queries(
model,
hidden.squeeze(0),
model._mean_context_channel(liked_items),
model._mean_context_channel(disliked_items),
torch.from_numpy(
prepared.inference_user_features[output_rows].astype(np.float32)
).to(device),
)
result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
return result
@torch.inference_mode()
def rank_candidates(
model: base.SequentialTwoTower,
prepared: SequenceData,
eligibility: CandidateSet,
config: Config,
device: torch.device,
ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
model.eval()
queries = inference_queries(model, prepared, config, device)
item_parts: list[torch.Tensor] = []
for start in range(0, len(prepared.item_ids), 2_048):
rows = torch.arange(
start,
min(start + 2_048, len(prepared.item_ids)),
dtype=torch.long,
device=device,
)
item_parts.append(model.encode_items(rows).cpu())
items = torch.cat(item_parts)
rows: list[tuple[int, int, int]] = []
for start in range(0, len(eligibility.uids), ranking_batch_size):
end = min(start + ranking_batch_size, len(eligibility.uids))
scores = queries[start:end] @ items.T
eligible = np.unpackbits(
eligibility.packed[start:end],
axis=1,
count=len(prepared.item_ids),
bitorder="little",
).astype(bool, copy=False)
scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
for local_row, columns in enumerate(selected):
uid = int(eligibility.uids[start + local_row])
rows.extend(
(uid, int(prepared.item_ids[column]), rank)
for rank, column in enumerate(columns, start=1)
)
if end == len(eligibility.uids) or end % 1_024 == 0:
log(f"ranked users={end:,}/{len(eligibility.uids):,}")
query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
diagnostics = {
"query_norm_mean": float(query_norm.mean()),
"query_norm_std": float(query_norm.std()),
"item_norm_mean": float(item_norm.mean()),
"item_norm_std": float(item_norm.std()),
}
return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
def write_ranking(frame: pd.DataFrame, output: Path) -> None:
output.parent.mkdir(parents=True, exist_ok=True)
if output.suffix == ".parquet":
frame.to_parquet(output, index=False, compression="zstd")
elif output.suffix == ".csv":
frame.to_csv(output, index=False)
else:
raise ValueError("--output must end in .parquet or .csv")
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"),
## Step 27 · agent · 2026-09-29T05:44:13.641Z
### read_file · 2026-09-29T05:44:13.641Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1950,
"end_line": 2050
}
### Tool output · 2026-09-29T05:44:13.648Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1950-2050 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2051.
--- FILE CONTENT (truncated) ---
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)
## Step 28 · agent · 2026-09-29T05:44:40.436Z
### Recorded reasoning summary
Analyzing Supported Arguments
I'm currently scrutinizing the available `args` and their associated options. My focus is on understanding precisely what the prompt dictates regarding request-time latency, precomputed item vectors, and the on-the-fly user vector computation from interaction history for comparison.
### Recorded reasoning summary
Improving Negative Sampling
My current focus is on understanding and enhancing the negative sampling strategy. I'm examining how the training objective selects and utilizes negative examples, aiming to diagnose limitations and implement improvements within the existing CPU budget before validating with the public backtest.
### Recorded reasoning summary
Investigating Negative Sampling Methods
I'm digging into how negative items are sampled, specifically examining the `draw_full_history_filtered_negatives` function. My current focus is on understanding the mechanics of how `uniform` and `inbatch` negatives are drawn to ensure a robust sampling strategy.
### Recorded reasoning summary
Clarifying Target Definitions
I'm currently investigating the `targets` variable within the negative sampling logic. My immediate focus is on determining its precise role and origin, tracing its usage back to `run_training_epoch` to ensure the sampling process is correctly initialized and operating on the intended data.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm currently focused on the `draw_full_history_filtered_negatives` function and how it handles targets. I've realized `targets` is actually `positive_items`, which can be quite large. My investigation now centers on the dimensions of `priorities` relative to `unique_targets` to ensure correct negative sampling.
### Recorded reasoning summary
Calculating Model Loss
I'm now examining the model's loss calculation, specifically the `positive_loss` and `negative_loss` components. My focus is on understanding the implications of `F.softplus(-positive_logits)` and how `corrected_negative_loss` is applied to the logits.
### Recorded reasoning summary
Understanding Loss Calculation
I'm now delving into the specifics of `corrected_negative_loss`. It appears `raw` is calculated using `F.softplus(logits)`, which equates to $\log(1 + e^s)$. This contrasts with the `positive_loss` calculation of `F.softplus(-positive_logits)`, or $\log(1 + e^{-s})$.
### Recorded reasoning summary
Revisiting Training Objective
I'm now conducting a comprehensive re-evaluation of the entire training objective. My current focus is on systematically analyzing the prompt's directives, the origin and sampling of negatives, the impact of the false-negative filter, and the specifics of in-batch negatives.
### Recorded reasoning summary
Exploring Loss Function Details
I'm currently investigating the specific loss function being employed. My focus is on distinguishing between Binary Cross-Entropy, sampled softmax, InfoNCE, and BPR, and understanding how explicit negative events, like dislikes, are incorporated.
### Recorded reasoning summary
Refining Negative Sampling
I'm currently examining the `draw_full_history_filtered_negatives` function, specifically how uniform and in-batch negatives are selected. My focus is on understanding the `item_correction` mechanism within this process.
### Recorded reasoning summary
Estimating In-Batch Propensities
I'm currently working on estimating in-batch propensities. My focus is on understanding the calculation of `counts` and `probabilities`, and how `inbatch_correction_power` influences the `raw` output for normalization.
### Recorded reasoning summary
Analyzing Loss Correction
I am now examining how the `correction` tensor, derived from in-batch propensities, is applied within the `corrected_negative_loss` function. My focus is on understanding the implications of using `weights` to scale the `raw` logits, specifically how this impacts the overall loss calculation.
### Recorded reasoning summary
Adjusting Loss Weighting
I'm now focused on the inverse relationship between item popularity and the weighting applied in `corrected_negative_loss`. High-frequency items, sampled more often as negatives, receive smaller weights, which seems counterintuitive for bias correction.
### run_shell_command · 2026-09-29T05:44:40.436Z
{
"command": "python -c \"\nimport pandas as pd\ndf = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(df.info())\nprint(df.head(10))\nprint(df.describe())\n\"",
"description": "Inspect candidate_catalog.parquet"
}
### Tool output · 2026-09-29T05:44:41.991Z
Output:
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 item_id 30000 non-null uint32
1 positive_listens 30000 non-null int64
2 organic_positive_listens 30000 non-null uint64
3 popularity_rank 30000 non-null int32
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
5 5463340 22746 17398 6
6 8213481 22505 17071 7
7 906358 22238 17183 8
8 2859641 18380 13704 9
9 3033749 16682 12267 10
item_id ... popularity_rank
count 3.000000e+04 ... 30000.000000
mean 4.698278e+06 ... 15000.500000
std 2.700274e+06 ... 8660.398374
min 2.060000e+02 ... 1.000000
25% 2.382368e+06 ... 7500.750000
50% 4.687992e+06 ... 15000.500000
75% 7.040711e+06 ... 22500.250000
max 9.390294e+06 ... 30000.000000
[8 rows x 4 columns]
Process Group PGID: 1734
## Step 29 · agent · 2026-09-29T05:44:55.670Z
### Recorded reasoning summary
Analyzing Target Weighting
I'm currently examining the `target_weighting` parameter within the `Config` class. My focus is on understanding its purpose and how it influences downstream processes by exploring the available choices: "discovery-graded", "organic-strong", and "uniform." This investigation is a crucial step in understanding the configuration's impact.
### Recorded reasoning summary
Revising Default Understanding
I've just realized my previous assumption about the default `target_weighting` was incorrect. It's actually set to `"organic-strong"`, not `"discovery-graded"`. I'm now reviewing the `build_target_weights` function to see what it does specifically for `"discovery-graded"`, as this might offer further insights into how positive target weights are handled.
### Recorded reasoning summary
Investigating Negative Selection
I'm now scrutinizing the selection and utilization of negative examples within the training objective. My current focus is on identifying limitations in the existing negative-sampling strategy and devising improvements, all while adhering to the fixed CPU budget. I'll then validate these changes using the public backtest.
### Recorded reasoning summary
Exploring In-batch Negatives
I'm digging into the `_filtered_mixed_negatives` and `draw_training_negatives` functions. My current focus is understanding the source of in-batch negatives, specifically how they're selected using unique targets and a priority sampling mechanism, as opposed to the straightforward uniform negative generation.
### Recorded reasoning summary
Questioning In-batch Sampling Logic
I'm questioning whether the current approach truly constitutes in-batch sampling. It seems to deduplicate targets before sampling, diverging from standard methods where other items in the batch serve as negatives. I'm now examining how `item_correction`, derived from overall dataset frequencies, interacts with this deduplicated sampling.
### Recorded reasoning summary
Challenging Sampling Assumptions
I'm realizing my understanding of how in-batch items are sampled was flawed. It appears they aren't chosen based on their probability $P(i)$. Furthermore, the `unique_targets` deduplication and subsequent uniform sampling from this set, combined with the `item_correction` multiplier, suggest a departure from standard negative sampling practices for this recommendation problem. I need to re-evaluate the core objective.
### run_shell_command · 2026-09-29T05:44:55.670Z
{
"description": "Check validation eligible candidates statistics",
"command": "python -c \"\nimport numpy as np\nwith np.load('/task/data/validation_eligible_candidates.npz') as f:\n uids = f['uids']\n item_ids = f['item_ids']\n packed = f['packed_eligible']\n print('Users:', len(uids), 'Items:', len(item_ids), 'Packed shape:', packed.shape)\n # unpack for first 5 users\n counts = []\n for r in range(min(100, len(uids))):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n counts.append(mask.sum())\n print('Eligible count per user (first 100): min', min(counts), 'max', max(counts), 'mean', np.mean(counts))\n\""
}
### Tool output · 2026-09-29T05:44:56.192Z
Output: Users: 8456 Items: 30000 Packed shape: (8456, 3750)
Eligible count per user (first 100): min 25547 max 29971 mean 29098.94
Process Group PGID: 1751
## Step 30 · agent · 2026-09-29T05:45:05.064Z
### Recorded reasoning summary
Exploring Data Files
I'm currently examining the contents of `/task/data/`. My focus is on identifying and understanding the available data files, specifically the `.parquet` files like `train_dislikes`, `train_undislikes`, `train_likes`, and `train_unlikes`, to get a feel for their size and structure.
### run_shell_command · 2026-09-29T05:45:05.064Z
{
"command": "python -c \"\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nfor f in sorted(data_dir.glob('*.parquet')):\n df = pd.read_parquet(f)\n print(f'{f.name:35s} rows: {len(df):10,d} cols: {list(df.columns)}')\n\"",
"description": "Check rows and columns of all parquet files in /task/data"
}
### Tool output · 2026-09-29T05:45:07.391Z
Output: candidate_catalog.parquet rows: 30,000 cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet rows: 8,456 cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet rows: 484,776 cols: ['uid', 'item_id', 'state']
train_dislikes.parquet rows: 101,075 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet rows: 814,831 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet rows: 43,484,159 cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet rows: 19,960 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet rows: 292,665 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet rows: 3,563 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet rows: 37,795 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet rows: 1,501,697 cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet rows: 606 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet rows: 9,346 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
validation_preference_state.parquet rows: 469,124 cols: ['uid', 'item_id', 'state']
Process Group PGID: 1761
## Step 31 · agent · 2026-09-29T05:45:49.669Z
### Recorded reasoning summary
Analyzing Data Sizes
My current focus is on understanding the scale of the datasets. The `train_listens` file is by far the largest, containing over 43 million entries. This suggests a significant amount of user interaction data to process and learn from.
### read_file · 2026-09-29T05:45:49.669Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 340,
"end_line": 470
}
### Tool output · 2026-09-29T05:45:52.051Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 340-470 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 471.
--- FILE CONTENT (truncated) ---
def prepare_data(
public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
data = public / "data"
catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
"popularity_rank"
)
item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
item_index = pd.Index(item_ids)
n_items = len(item_ids)
if n_items >= np.iinfo(np.uint16).max:
raise RuntimeError("uint16 item storage is too small for this catalog")
if stage == "validation":
prefixes = ("train",)
history_end = base.TRAIN_END
candidate_name = "validation_eligible_candidates.npz"
state_name = "validation_preference_state.parquet"
else:
prefixes = ("train", "val")
history_end = base.VALIDATION_END
candidate_name = "test_eligible_candidates.npz"
state_name = "test_preference_state.parquet"
eligibility = CandidateSet.read(data / candidate_name)
if not np.array_equal(eligibility.item_ids, item_ids):
raise RuntimeError("catalog and candidate artifact item order differ")
explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
inference_likes, inference_dislikes, like_counts, dislike_counts = (
base.load_inference_state(
data / state_name,
item_index,
eligibility.uids,
n_items,
)
)
inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
# Snapshot zero is the shared empty explicit state.
liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
disliked_states = [
np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
]
state_counts: list[tuple[int, int]] = [(0, 0)]
user_ids: list[int] = []
offsets = [0]
timestamp_parts: list[np.ndarray] = []
item_parts: list[np.ndarray] = []
event_parts: list[np.ndarray] = []
activity_parts: list[np.ndarray] = []
state_parts: list[np.ndarray] = []
listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
started = time.monotonic()
for seen_users, (uid, listens) in enumerate(
base.merge_listen_streams(listen_paths, item_index), start=1
):
parts = build_sequence_parts(
listens,
explicit_by_user.get(uid, base.empty_explicit_events()),
n_items,
liked_states,
disliked_states,
state_counts,
)
if parts is None:
continue
sequence_row = len(user_ids)
user_ids.append(uid)
timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
item_parts.append(parts[1])
event_parts.append(parts[2])
activity_parts.append(parts[3])
state_parts.append(parts[4])
offsets.append(offsets[-1] + len(parts[1]))
inference_row = inference_rows.get(uid)
if inference_row is not None:
inference_sequence_rows[inference_row] = sequence_row
inference_user_features[inference_row] = final_user_features(
parts[0],
parts[1],
parts[2],
history_end,
int(like_counts[inference_row]),
int(dislike_counts[inference_row]),
)
if seen_users % 1_000 == 0:
log(
f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
f"states={len(liked_states):,} "
f"elapsed={time.monotonic() - started:.1f}s"
)
missing = np.flatnonzero(inference_sequence_rows < 0)
if len(missing):
inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
inference_user_features[missing, 5] = (
np.log1p(dislike_counts[missing]) / 4.0
)
log(f"target users without qualifying catalog sequence={len(missing):,}")
prepared = SequenceData(
item_ids=item_ids,
item_features=base.build_item_features(catalog),
user_ids=np.asarray(user_ids, dtype=np.int64),
offsets=np.asarray(offsets, dtype=np.int64),
sequence_timestamps=np.concatenate(timestamp_parts),
sequence_items=np.concatenate(item_parts),
event_features=np.concatenate(event_parts),
activity_features=np.concatenate(activity_parts),
state_ids=np.concatenate(state_parts),
liked_states=np.stack(liked_states),
disliked_states=np.stack(disliked_states),
state_counts=np.asarray(state_counts, dtype=np.int32),
inference_user_rows=inference_sequence_rows,
inference_liked_items=inference_likes,
inference_disliked_items=inference_dislikes,
inference_user_features=inference_user_features,
)
log(
f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
f"tokens={len(prepared.sequence_items):,} "
f"next_item_targets={prepared.target_count:,} "
f"states={len(prepared.liked_states):,}"
)
return prepared, eligibility
## Step 32 · agent · 2026-09-29T05:46:03.856Z
### read_file · 2026-09-29T05:46:03.856Z
{
"start_line": 247,
"end_line": 314,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T05:46:03.862Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 247-314 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 315.
--- FILE CONTENT (truncated) ---
def build_sequence_parts(
listens: base.ListenEvents,
explicit: base.ExplicitEvents,
n_items: int,
liked_states: list[np.ndarray],
disliked_states: list[np.ndarray],
state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
items = listens.items[qualifying].astype(np.uint16, copy=False)
organic = listens.organic[qualifying].astype(np.float32, copy=False)
completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
if not len(items):
return None
event_features = np.zeros((len(items), 3), dtype=np.float16)
event_features[:, 0] = organic.astype(np.float16)
event_features[:, 1] = (
np.minimum(completion, 100.0) / 100.0
).astype(np.float16)
if len(items) > 1:
gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
event_features[1:, 2] = np.exp(
-gaps.astype(np.float64) / (60.0 * base.DAY)
).astype(np.float16)
activity = causal_activity_features(
timestamps, items, organic, completion
)
states = np.zeros(len(items), dtype=np.uint32)
active_likes: dict[int, int] = {}
active_dislikes: dict[int, int] = {}
explicit_position = 0
current_state = 0
for row, timestamp in enumerate(timestamps):
changed = False
while (
explicit_position < len(explicit.timestamps)
and int(explicit.timestamps[explicit_position]) < int(timestamp)
):
item = int(explicit.items[explicit_position])
event_timestamp = int(explicit.timestamps[explicit_position])
destination = (
active_likes
if int(explicit.channel[explicit_position]) == 0
else active_dislikes
)
if int(explicit.active[explicit_position]) == 1:
destination[item] = event_timestamp
else:
destination.pop(item, None)
explicit_position += 1
changed = True
if changed:
current_state = append_state_snapshot(
active_likes,
active_dislikes,
n_items,
liked_states,
disliked_states,
state_counts,
)
states[row] = np.uint32(current_state)
return timestamps, items.copy(), event_features, activity, states
def final_user_features(
## Step 33 · agent · 2026-09-29T05:46:20.685Z
### Recorded reasoning summary
Analyzing Data Preparation
I'm currently examining the `prepare_data` function. My focus is on understanding how it transforms raw listen and event data into sequential tokens, specifically for listens with a positive past completion rate above 50%. I'm also tracking how explicit user states, like likes and dislikes, are updated. My next step is to determine the precise training targets for this sequence.
### Recorded reasoning summary
Reviewing Negative Sampling
I'm re-examining the negative sampling mechanism, particularly within `draw_training_negatives`. My attention is drawn to the `known_positive_packed` parameter and the associated `build_known_positive_filter` function. I need to understand how this filter is constructed and how it influences the selection of negative examples, ensuring no overlap with user positives.
### Recorded reasoning summary
Investigating Sequence Indexing
I'm scrutinizing the indexing logic for sequences, specifically the slice `offset : offset + int(target_count) + 1`. I need to pinpoint the definition and role of `target_count` and how it interacts with the `chronological_target_partition` function. This is critical for understanding the boundaries of sequence elements used for training.
### Recorded reasoning summary
Defining Target Windows
I'm now focusing on the `TargetPartition` construction and how it defines training and validation windows. My attention is on the logic that calculates `validation` and `training` counts, ensuring they respect `max_train_targets_per_user` and `max_validation_targets_per_user` constraints, and how `retained_target_window_starts` influences the actual window boundaries for each user.
### Recorded reasoning summary
Clarifying Sequence Boundaries
I'm re-examining the slice `offset : offset + int(target_count) + 1`. It appears this correctly captures all items within a user's history, including the target itself, which aligns with the `history_training_counts`. This has clarified the scope of the sequence used for filtering.
### Recorded reasoning summary
Filtering Invalid Negatives
I'm now focused on the `invalid_full_history_negatives` function. My analysis reveals it flags any negative candidate present in `known_positive_packed` as invalid. This is a critical step before `draw_full_history` can proceed, ensuring no overlap with user positives.
### grep_search · 2026-09-29T05:46:20.685Z
{
"pattern": "dislike",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T05:46:20.700Z
Found 40 matches for pattern "dislike" in path "/app/solution":
---
File: train_current_model.py
L110: disliked_states: np.ndarray
L114: inference_disliked_items: np.ndarray
L176: active_dislikes: dict[int, int],
L179: disliked_states: list[np.ndarray],
L183: disliked_states.append(
L184: base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186: state_counts.append((len(active_likes), len(active_dislikes)))
L252: disliked_states: list[np.ndarray],
L279: active_dislikes: dict[int, int] = {}
L293: else active_dislikes
L304: active_dislikes,
L307: disliked_states,
L320: dislike_count: int,
L337: result[5] = np.log1p(dislike_count) / 4.0
L369: inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383: disliked_states = [
L384: np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405: disliked_states,
L427: int(dislike_counts[inference_row]),
L440: np.log1p(dislike_counts[missing]) / 4.0
L455: disliked_states=np.stack(disliked_states),
L459: inference_disliked_items=inference_dislikes,
L784: disliked_items = torch.from_numpy(
L785: prepared.disliked_states[unique_states].astype(np.int64)
L788: disliked_vectors = model._mean_context_channel(disliked_items)
L793: disliked_vectors[inverse_rows],
L802: disliked_vectors: torch.Tensor,
L807: [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147: liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159: disliked_vectors,
L1844: disliked_items = torch.from_numpy(
L1845: prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851: model._mean_context_channel(disliked_items),
---
File: two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184: ("dislikes", 1, 1, 0),
L185: ("undislikes", 1, 0, 1),
L265: disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267: dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270: ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281: return liked, disliked, like_counts, dislike_counts
---
## Step 34 · agent · 2026-09-29T05:46:24.501Z
### Recorded reasoning summary
Clarifying Input Features
My recent thoughts are focused on meticulously defining input features. Specifically, I'm reviewing the utilization of `disliked` as an exclusive input to the user tower, ensuring it's never mistakenly interpreted as a negative signal. This clarification is crucial for accurate model behavior and to avoid recommending items a user has previously disliked.
### run_shell_command · 2026-09-29T05:46:24.501Z
{
"description": "Check if train dislikes appear in validation candidates",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as f:\n uids = f['uids']\n item_ids = f['item_ids']\n packed = f['packed_eligible']\n uid_map = {uid: i for i, uid in enumerate(uids)}\n item_map = {item: i for i, item in enumerate(item_ids)}\n\nfound_eligible_dislike = 0\nfor uid, item in zip(dislikes.uid, dislikes.item_id):\n if uid in uid_map and item in item_map:\n r = uid_map[uid]\n c = item_map[item]\n byte, bit = divmod(c, 8)\n if (packed[r, byte] >> bit) & 1:\n found_eligible_dislike += 1\nprint('Eligible dislikes found in validation candidates:', found_eligible_dislike)\n\""
}
### Tool output · 2026-09-29T05:46:26.246Z
Output: Eligible dislikes found in validation candidates: 0
Process Group PGID: 1789
## Step 35 · agent · 2026-09-29T05:46:45.789Z
### Recorded reasoning summary
Investigating Negative Example Selection
I'm digging into how our training objective picks and uses negative examples. Turns out, validation candidates deliberately exclude all training interactions, which is a key point to consider for diagnosing limitations.
### Recorded reasoning summary
Optimizing Negative Sampling
My focus is on refining the negative sampling strategy within our current CPU constraints. I've begun analyzing the loss function, specifically how positive and negative examples contribute, to identify areas for improvement. My next step is to implement and test modifications against the public backtest to ensure efficiency and adherence to serving and data boundaries.
### Recorded reasoning summary
Analyzing Loss Calculation
I'm scrutinizing the `corrected_negative_loss` function. It appears the uniform negative portion is scaled by `config.uniform_negatives` *before* the mean is taken. This seems unusual and potentially incorrect.
### Recorded reasoning summary
Revising Loss Calculation
I've realized my previous assessment of the `corrected_negative_loss` was slightly off. The calculation is actually producing the average negative loss per sample, effectively balancing uniform and in-batch negatives with a weighted average, which is a standard approach.
### Recorded reasoning summary
Re-evaluating Loss Weighting
I've identified that my previous understanding of the loss function was incorrect. The positive item carries a 0.5 weight, while all K negatives collectively share the remaining 0.5 weight. This means each individual negative has a very small contribution. I'm now questioning the learning outcome and exploring alternative loss functions suitable for the two-tower architecture.
### Recorded reasoning summary
Examining Negative Example Weighting
I'm looking into the loss function's weighting. It seems each individual negative example has a very small impact due to how the weights are distributed, and I'm now investigating alternative loss functions for our two-tower architecture.
### Recorded reasoning summary
Refining Loss Calculation Parameters
I'm now examining how the weights for positive and negative items are applied within the loss function. I've discovered that the positive item has a weight of 0.5, while the remaining 0.5 weight is shared across all K negatives. This means each individual negative contributes minimally to the overall loss, leading me to explore alternative loss functions more suitable for our two-tower architecture.
### Recorded reasoning summary
Investigating Weight Distribution
I'm now focusing on the loss function's weighting mechanism. My current understanding is that the positive item is assigned a 0.5 weight, while the remaining 0.5 is distributed among all K negatives. This implies each individual negative has a minimal impact, prompting me to explore alternative loss functions suitable for a two-tower architecture.
### grep_search · 2026-09-29T05:46:45.789Z
{
"pattern": "yambda-windowed-sequence-two-tower-base",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T05:46:45.806Z
Found 1 match for pattern "yambda-windowed-sequence-two-tower-base" in path "/app/solution":
---
File: train_current_model.py
L2064- config.max_train_targets_per_user,
L2065- config.max_validation_targets_per_user,
L2066- )
L2067- if args.prepare_only:
L2068- print(
L2069- json.dumps(
L2070- {
L2071- "stage": args.stage,
L2072- "sequence_users": len(prepared.user_ids),
L2073- "sequence_tokens": len(prepared.sequence_items),
L2074- "next_item_targets": prepared.target_count,
L2075- "selected_training_targets": int(
L2076- target_partition.training_counts.sum()
L2077- ),
L2078- "selected_validation_targets": int(
L2079- target_partition.validation_counts.sum()
L2080- ),
L2081- "discarded_old_targets": int(
L2082- target_partition.window_starts.sum()
L2083- ),
L2084- "target_users": len(eligibility.uids),
L2085- "runtime_seconds": round(time.monotonic() - started, 3),
L2086- "config": asdict(config),
L2087- },
L2088- indent=2,
L2089- )
L2090- )
L2091- return 0
L2092-
L2093- model, training_history = train_model(
L2094- public,
L2095- args.stage,
L2096- prepared,
L2097- config,
L2098- device,
L2099- args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
L2100- args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
L2101- )
L2102- ranking, embedding_diagnostics = rank_candidates(
L2103- model,
L2104- prepared,
L2105- eligibility,
L2106- config,
L2107- device,
L2108- args.ranking_batch_size,
L2109- )
L2110- write_ranking(ranking, args.output.resolve())
L2111- print(
L2112- json.dumps(
L2113- {
L2114: "baseline": "yambda-windowed-sequence-two-tower-base",
L2115- "stage": args.stage,
L2116- "output": str(args.output.resolve()),
L2117- "rows": len(ranking),
L2118- "users": int(ranking.uid.nunique()),
L2119- "runtime_seconds": round(time.monotonic() - started, 3),
L2120- "public_inputs_only": True,
L2121- "architecture": {
L2122- "family": "pure-windowed-sequence-two-tower",
L2123- "sequence_training": (
L2124- "every-adjacent-target-within-recent-chronological-window"
L2125- ),
L2126- "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
L2127- "bptt_hidden_state": "carried-and-detached",
L2128- "model_selection": "chronological-validation-BCE-early-stopping",
L2129- "loss_execution": "gradient-accumulated-microbatches",
L2130- "user_id_embedding": False,
L2131- "user_mlp": "582-512-256-192",
L2132- "item_mlp": "196-512-256-192",
L2133- "normalization": "none",
L2134- "score": "raw-dot-product",
L2135- "objective": "sample-weighted-BCE-at-every-timestep",
L2136- "negative_sampling": (
L2137- "24-uniform-plus-8-propensity-corrected-inbatch"
L2138- ),
L2139- "false_negative_filter": config.negative_filter_scope,
L2140- "target_weighting": config.target_weighting,
L2141- },
L2142- "config": asdict(config),
L2143- "sequence_tokens": len(prepared.sequence_items),
L2144- "all_next_item_targets": prepared.target_count,
L2145- "selected_training_targets": int(
L2146- target_partition.training_counts.sum()
L2147- ),
L2148- "selected_validation_targets": int(
L2149- target_partition.validation_counts.sum()
L2150- ),
L2151- "discarded_old_targets": int(target_partition.window_starts.sum()),
L2152- "training_history": training_history,
L2153- "embedding_diagnostics": embedding_diagnostics,
L2154- "torch_version": torch.__version__,
L2155- "device": str(device),
L2156- },
L2157- indent=2,
L2158- )
L2159- )
L2160- return 0
L2161-
L2162-
L2163- if __name__ == "__main__":
L2164- raise SystemExit(main())
---
## Step 36 · agent · 2026-09-29T05:46:51.721Z
### Recorded reasoning summary
Analyzing Architecture Layers
I'm currently examining the "architecture" section, specifically the "pure-windowed-sequence-two-tower" family. My focus is on understanding the implications of "every-adjacent-target-within-recent-chronological-window" for sequence training and the role of the "sequence_encoder".
### Recorded reasoning summary
Refining Training Details
My recent thoughts are centered on the practicalities of training, specifically exploring options for caching prepared data. I'm investigating if `prepare_data` can be cached and examining the `--prepared-cache` argument to streamline the workflow.
### read_file · 2026-09-29T05:46:51.721Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 471,
"end_line": 525
}
### Tool output · 2026-09-29T05:46:51.729Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 471-525 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 526.
--- FILE CONTENT (truncated) ---
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
return {
"cache_version": CACHE_VERSION,
"stage": stage,
"positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
"profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
"event_time_feature": "exp-negative-gap-over-60-days",
}
def save_cache(
path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
np.savez(
path,
metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
**prepared.__dict__,
)
log(f"saved public-derived full-sequence cache: {path}")
def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
with np.load(path, allow_pickle=False) as artifact:
actual = json.loads(str(artifact["metadata"].item()))
expected = cache_metadata(stage, config)
if actual != expected:
raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
values = {
field: artifact[field].copy()
for field in SequenceData.__dataclass_fields__
}
prepared = SequenceData(**values)
log(
f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
f"targets={prepared.target_count:,}"
)
return prepared
def load_or_prepare(
public: Path,
stage: str,
config: Config,
cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
candidate_name = (
"validation_eligible_candidates.npz"
if stage == "validation"
else "test_eligible_candidates.npz"
)
eligibility = CandidateSet.read(public / "data" / candidate_name)
if cache is not None and cache.is_file():
prepared = load_cache(cache, stage, config)
if not np.array_equal(prepared.item_ids, eligibility.item_ids):
## Step 37 · agent · 2026-09-29T05:46:56.726Z
### run_shell_command · 2026-09-29T05:46:56.726Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --prepare-only",
"description": "Precompute validation cache"
}
### Tool output · 2026-09-29T05:47:00.711Z
Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output
OUTPUT --public-dir PUBLIC_DIR
[--prepared-cache PREPARED_CACHE]
[--device {cpu,mps}] [--threads THREADS]
[--bptt-steps BPTT_STEPS]
[--user-batch-size USER_BATCH_SIZE]
[--epochs EPOCHS]
[--learning-rate LEARNING_RATE]
[--loss-microbatch-size LOSS_MICROBATCH_SIZE]
[--negative-filter-scope {full-training-history,last-100}]
[--target-weighting {discovery-graded,organic-strong,uniform}]
[--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
[--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
[--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
[--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
[--validation-fraction VALIDATION_FRACTION]
[--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
[--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
[--early-stopping-patience EARLY_STOPPING_PATIENCE]
[--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
[--checkpoint-dir CHECKPOINT_DIR]
[--resume-checkpoint RESUME_CHECKPOINT]
[--seed SEED]
[--ranking-batch-size RANKING_BATCH_SIZE]
[--prepare-only]
train_current_model.py: error: the following arguments are required: --output
Exit Code: 2
Process Group PGID: 1814
## Step 38 · agent · 2026-09-29T05:47:03.115Z
### run_shell_command · 2026-09-29T05:47:03.115Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only",
"description": "Cache validation data"
}
### Tool output · 2026-09-29T05:47:57.652Z
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 50.558,
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
}
}
Process Group PGID: 1825
## Step 39 · agent · 2026-09-29T05:47:59.538Z
### run_shell_command · 2026-09-29T05:47:59.538Z
{
"command": "ls -lh /app/solution/validation_cache.npz",
"description": "Check validation cache size"
}
### Tool output · 2026-09-29T05:47:59.558Z
Output: -rw-r--r-- 1 agent agent 511M Sep 29 05:47 /app/solution/validation_cache.npz
Process Group PGID: 1858
## Step 40 · agent · 2026-09-29T05:48:02.347Z
### run_shell_command · 2026-09-29T05:48:02.347Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --output /app/solution/dummy.parquet \\\n --prepare-only",
"description": "Test cache loading time"
}
### Tool output · 2026-09-29T05:48:06.681Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
{
"stage": "validation",
"sequence_users": 9135,
"sequence_tokens": 20056329,
"next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"target_users": 8456,
"runtime_seconds": 0.412,
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "organic-strong",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
}
}
Process Group PGID: 1861
## Step 41 · agent · 2026-09-29T05:48:21.915Z
### Recorded reasoning summary
Accelerating Investigation Speed
The recent reduction in runtime from 50 seconds to a mere 0.412 seconds is a significant development, drastically enhancing our iteration speed. My current focus is now on dissecting the negative sampling strategy and training objective, specifically zeroing in on identifying any critical issues or bottlenecks within the current approach.
### Recorded reasoning summary
Refining Negative Sampling
I'm diving deeper into the negative sampling and training objective, as suggested. My current efforts are focused on meticulously reviewing `draw_full_history_filtered_negatives` and related components to pinpoint any underlying limitations or potential optimizations.
### Recorded reasoning summary
Analyzing Negative Sampling Code
I'm currently scrutinizing the `filtered_negatives` function. My focus is on understanding how uniform and in-batch negatives are generated and filtered, specifically the logic around `unique_targets` and `corrected_count`, to identify any potential inefficiencies or areas for improvement in the sampling process.
### Recorded reasoning summary
Optimizing Negative Sampling
I'm currently analyzing the `draw_full_history_filtered_negatives` function, specifically the logic for generating and filtering in-batch negatives. I'm focusing on how `unique_targets` and `corrected_count` influence this process and exploring potential optimizations for efficiency.
### Recorded reasoning summary
Investigating Negative Sampling Logic
I'm currently examining the code for handling invalid negative samples and their replacements. I'm specifically looking at the loop that retries invalid draws and how rejected in-batch items are converted to uniform draws. This is to ensure the sampling process is robust.
### Recorded reasoning summary
Re-evaluating Sampling Strategy
I'm questioning the validity of drawing in-batch negatives solely from unique positive targets within a BPTT block. This method might not accurately reflect item popularity, potentially skewing negative sampling.
### Recorded reasoning summary
Challenging Uniform Sampling
I'm realizing my uniform negative sampling within a BPTT block doesn't account for global popularity. This means highly popular items get the same chance of being selected as unpopular ones. The `item_correction` mechanism also seems to adjust based on this global popularity, which I need to fully understand.
### run_shell_command · 2026-09-29T05:48:21.915Z
{
"description": "Analyze baseline recommendations distribution",
"command": "python -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/production_validation.parquet')\nprint('Unique items recommended:', df.item_id.nunique())\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\ndf['pop_rank'] = df.item_id.map(item_pop)\nprint(df['pop_rank'].describe())\ntop_recs = df.item_id.value_counts().head(20)\nprint('Most frequently recommended items:')\nfor item, count in top_recs.items():\n print(f'Item {item}: count {count} (pop rank {item_pop.get(item)})')\n\""
}
### Tool output · 2026-09-29T05:48:23.533Z
Output: Unique items recommended: 2575
count 845600.000000
mean 2991.802935
std 5853.839534
min 1.000000
25% 102.000000
50% 413.000000
75% 2627.000000
max 29992.000000
Name: pop_rank, dtype: float64
Most frequently recommended items:
Item 6896222: count 5089 (pop rank 818)
Item 7565403: count 4970 (pop rank 2062)
Item 2055777: count 4647 (pop rank 1170)
Item 8722394: count 4627 (pop rank 634)
Item 5010168: count 4572 (pop rank 2947)
Item 5134208: count 4397 (pop rank 306)
Item 3968387: count 4160 (pop rank 318)
Item 8823268: count 4157 (pop rank 76)
Item 6414220: count 4155 (pop rank 3207)
Item 8350060: count 4132 (pop rank 1529)
Item 7227266: count 4091 (pop rank 919)
Item 8729910: count 4036 (pop rank 564)
Item 3375935: count 4003 (pop rank 282)
Item 6625048: count 3992 (pop rank 128)
Item 6006390: count 3988 (pop rank 75)
Item 6813373: count 3960 (pop rank 126)
Item 2408923: count 3865 (pop rank 111)
Item 2705460: count 3815 (pop rank 3016)
Item 1034521: count 3747 (pop rank 189)
Item 781438: count 3710 (pop rank 86)
Process Group PGID: 1879
## Step 42 · agent · 2026-09-29T05:48:27.536Z
### run_shell_command · 2026-09-29T05:48:27.536Z
{
"command": "python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ngraded = validation_truth(eligibility, targets)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\nrel_df = pd.DataFrame({'item_id': all_relevant_items, 'grade': grades})\nrel_df['pop_rank'] = rel_df.item_id.map(item_pop)\nprint(f'Total graded interactions: {len(rel_df)}')\nprint(f'Unique relevant items: {rel_df.item_id.nunique()}')\nprint('Popularity rank of relevant items:')\nprint(rel_df.pop_rank.describe())\nprint('Grade distribution:')\nprint(rel_df.grade.value_counts())\n\"",
"description": "Inspect ground truth relevant items in validation"
}
### Tool output · 2026-09-29T05:48:29.020Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1898
## Step 43 · agent · 2026-09-29T05:48:33.920Z
### run_shell_command · 2026-09-29T05:48:33.920Z
{
"command": "python -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nfrom pathlib import Path\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ngraded = validation_truth(eligibility, targets)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\nall_relevant_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_relevant_items.append(item)\n grades.append(grade)\n\nrel_df = pd.DataFrame({'item_id': all_relevant_items, 'grade': grades})\nrel_df['pop_rank'] = rel_df.item_id.map(item_pop)\nprint(f'Total graded interactions: {len(rel_df)}')\nprint(f'Unique relevant items: {rel_df.item_id.nunique()}')\nprint('Popularity rank of relevant items:')\nprint(rel_df.pop_rank.describe())\nprint('Grade distribution:')\nprint(rel_df.grade.value_counts())\n\"",
"description": "Inspect ground truth relevant items in validation"
}
### Tool output · 2026-09-29T05:48:36.019Z
Output: Total graded interactions: 37769
Unique relevant items: 14124
Popularity rank of relevant items:
count 37769.000000
mean 8463.087744
std 8117.953262
min 1.000000
25% 1733.000000
50% 5622.000000
75% 13374.000000
max 30000.000000
Name: pop_rank, dtype: float64
Grade distribution:
grade
1 33352
3 4417
Name: count, dtype: int64
Process Group PGID: 1909
## Step 44 · agent · 2026-09-29T05:49:07.951Z
### Recorded reasoning summary
Analyzing Validation Results
My current focus is on the validation metrics. I'm observing a significant uplift in unique relevant items identified (14,124) compared to the baseline's recommendations (2,575). The mean popularity rank of my relevant items (8,463) is also a key point, with a median of 5,622, indicating a different distribution than the baseline's mean rank of 2,991.
### Recorded reasoning summary
Investigating Baseline Performance
I'm digging into the baseline model's behavior, and it's clear it's over-indexing on popular items, evidenced by the low median popularity rank of 413. This explains its poor NDCG@10 performance. I'm now examining the negative sampling strategy to understand this collapse.
### Recorded reasoning summary
Revising Correction Logic
I've identified a critical flaw in the in-batch correction logic. It appears to be suppressing the negative loss on popular items, inadvertently pushing them higher. I'm revising this to correctly penalize high scores on popular negatives.
### Recorded reasoning summary
Exploring Two-Tower Models
I'm currently investigating how two-tower models in the literature, like YouTube's and Yi et al.'s "Sampling-bias-corrected neural modeling," handle in-batch negatives. Their approach involves subtracting log $p_i$ from the logit, which lowers the score for popular items. This is a crucial insight for my current corrections.
### Recorded reasoning summary
Correcting Loss Calculation
I've discovered a critical issue with my current Binary Cross-Entropy implementation. The weighting mechanism, intended to address popularity bias, is actually amplifying it by reducing penalties for popular negative examples. I'm re-evaluating this entire approach and considering alternative loss functions like sampled softmax or InfoNCE.
### Recorded reasoning summary
Refining Negative Sampling
I'm now focusing on how negative examples are selected and utilized within the training objective. My current thinking is that improvements to the negative sampling strategy, while staying within CPU budget, will be key to enhancing performance, with validation via the public backtest.
### Recorded reasoning summary
Refining In-Batch Negatives
I'm currently scrutinizing how in-batch negative examples are selected. Specifically, I'm looking at the logic that chooses these negatives from unique targets, ensuring they don't overlap with actual positive examples. This granular review aims to optimize their impact on the training objective.
### Recorded reasoning summary
Revising Negative Sampling Logic
I'm examining my current method for selecting in-batch negatives. It appears deduplicating targets and then sampling uniformly from unique targets is not reflecting true popularity. This contrasts with standard in-batch negative sampling, which naturally leverages item frequency for harder negatives.
### Recorded reasoning summary
Sampling Negatives Strategically
I'm realizing my current uniform random sampling of in-batch negatives is discarding valuable popularity information. I'm now exploring sampling negatives based on popularity distributions, like $P(i)^{0.75}$, and considering how to incorporate hard negatives and candidate eligibility constraints into this process.
### Recorded reasoning summary
Adapting Positive Filtering
I'm reviewing how positive items are packed for filtering, noticing a discrepancy where only `prepared.item_ids` are used. I'm adjusting this to incorporate the full set of public training positives, ensuring a more accurate representation.
### Recorded reasoning summary
Considering Unobserved Data
I'm now contemplating user interactions that were previously discarded, specifically listens with less than 50% completion. These skipped impressions could represent crucial negative signals. I'm also questioning how items outside the catalog or those not in `sequence_items` are handled, and whether validation candidates correctly exclude all training interactions, even partial listens.
### run_shell_command · 2026-09-29T05:49:07.951Z
{
"description": "Check if low completion listens are eligible candidates",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as f:\n uids = f['uids']\n item_ids = f['item_ids']\n packed = f['packed_eligible']\n uid_map = {uid: i for i, uid in enumerate(uids)}\n item_map = {item: i for i, item in enumerate(item_ids)}\n\n# filter to target users\ntarget_listens = listens[listens.uid.isin(uid_map)]\nprint('Total listens by target users:', len(target_listens))\nlow_completion = target_listens[target_listens.played_ratio_pct < 50]\nprint('Low completion listens (<50%):', len(low_completion))\n\neligible_found = 0\nfor uid, item in zip(low_completion.uid.head(10000), low_completion.item_id.head(10000)):\n if uid in uid_map and item in item_map:\n r = uid_map[uid]\n c = item_map[item]\n byte, bit = divmod(c, 8)\n if (packed[r, byte] >> bit) & 1:\n eligible_found += 1\nprint('Eligible found among low completion listens:', eligible_found)\n\""
}
### Tool output · 2026-09-29T05:49:16.070Z
Output: Total listens by target users: 43222297
Low completion listens (<50%): 15854954
Eligible found among low completion listens: 0
Process Group PGID: 1935
## Step 45 · agent · 2026-09-29T05:49:58.778Z
### Recorded reasoning summary
Analyzing Exclusion Logic
I've identified a critical exclusion: all `train_*` encounters are definitively out. This means that even if a user engaged with a track during training, it won't be considered as a validation candidate if it was part of that training set, as demonstrated by the zero eligible finds among low completion listens.
### Recorded reasoning summary
Refining Inference Masking
I'm re-evaluating the inference process, specifically how candidate rankings are masked. The current approach calculates scores for all items before applying the `eligible` mask, which seems computationally redundant. I'm exploring ways to integrate this masking earlier to improve efficiency.
### Recorded reasoning summary
Investigating Negative Examples
My focus has shifted to how negative examples are incorporated into the training objective. I'm specifically examining the `train_current_model.py` script, lines 1125-1135, and the `corrected_negative_loss` function to understand the mechanics of their utilization.
### Recorded reasoning summary
Deriving Loss Components
I'm currently dissecting the mathematical underpinnings of the loss function, specifically focusing on the derivatives of the positive and negative log-likelihoods. This will allow me to better understand the impact of different logit values on the overall training objective.
### Recorded reasoning summary
Analyzing Gradient Asymmetry
I've identified a significant imbalance in the gradients for positive versus negative examples. The positive loss term has a much larger potential impact on the model's weights, even when aggregated across all negative examples, due to the smaller magnitudes of the negative gradients.
### Recorded reasoning summary
Adjusting Gradient Influence
I've uncovered that positive gradients are disproportionately strong, nearly three times larger than all negative gradients combined. This imbalance causes popular items to be overly amplified. The current negative sampling strategy fails to adequately suppress these frequent items, resulting in a suboptimal pair accuracy of only 75%.
### Recorded reasoning summary
Investigating Gradient Dynamics
I'm focusing on the loss function dynamics in modern two-tower models. I've observed that the standard sampled softmax or InfoNCE loss exhibits a crucial property: the sum of gradients across negative examples precisely balances the gradient of the positive example. This suggests a fundamental mechanism for ranking optimization.
### Recorded reasoning summary
Examining Ranking Losses
I'm exploring the impact of different ranking losses, specifically comparing the implications of sampled softmax/InfoNCE against Bayesian Personalized Ranking (BPR). The core difference seems to be how they enforce separation between positive and negative item logits. BPR, with its explicit margin, appears to drive larger gradients when negative logits exceed positive ones, suggesting a more aggressive adjustment. I'm also checking if the baseline employs Binary Cross-Entropy (BCE) universally, as indicated by its presence in the configuration and scoring functions.
### Recorded reasoning summary
Comparing Ranking Losses
I am analyzing the divergence between sampled softmax/InfoNCE and BPR, particularly their contrasting methods for enforcing logit separation. BPR's explicit margin seems to drive more aggressive gradient adjustments when negative logits surpass positive ones. I'm also verifying if the baseline universally applies BCE, as suggested by its presence in the config and scoring functions.
### Recorded reasoning summary
Analyzing Negative Sampling
I'm scrutinizing how the training objective chooses and leverages negative examples. My current focus is on identifying specific limitations within the existing negative sampling strategy, aiming to enhance its efficiency and effectiveness within the established CPU constraints.
### Recorded reasoning summary
Refining Negative Sampling
I'm examining the specifics of how negative examples are selected, noting that a large portion is chosen uniformly at random from a vast item pool. This uniform selection is problematic, as it heavily favors low-popularity items that offer minimal training signal and are too easy to differentiate.
### Recorded reasoning summary
Filtering In-Batch Negatives
I'm refining how in-batch negatives are handled. Previously, sampled negatives could include items from the user's own history and, if filtered, were replaced with uniform random items. I'm now focusing on how this replacement mechanism impacts training.
### Recorded reasoning summary
Exploring Sampling Strategies
I am evaluating alternative negative sampling methods, considering popularity-biased sampling to introduce harder negatives that better reflect retrieval candidates. I am also investigating the impact of filtering negatives against the entire training history and analyzing the current mixture of uniform and in-batch negatives to determine if adjustments are warranted.
### Recorded reasoning summary
Analyzing Gradient Contributions
I am investigating the loss function's pointwise BCE formulation. The averaging over negatives results in each individual negative example contributing significantly less to the gradient update (1/64) than the positive example (1/2), indicating a strong bias towards the positive logit.
**Quantifying Gradient Weighting**
My analysis confirms that the current loss formulation results in the positive example's gradient carrying 32 times more weight than any individual negative's gradient. This disproportionate influence is a key area for adjustment to improve balance.
**Adjusting Gradient Magnitudes**
I am closely examining the `corrected_negative_loss` function and its `inbatch_correction` parameter. The interplay between uniform and in-batch negatives, and how their gradients are scaled, is crucial for understanding the overall loss dynamics.
**Exploring Loss Scaling**
I'm focused on how the loss function scales gradients. The current pointwise BCE setup, with its averaging over negatives, leads to a substantial imbalance where positive gradients are heavily dominant over individual negative gradients.
**Evaluating Negative Loss Averaging**
I am scrutinizing the averaging of the negative loss component. The aggregation of gradients from 32 negative examples means each contributes a minor fraction (1/64) compared to the positive example's (1/2) gradient weight.
**Refining Loss Formulation**
My investigation into the loss function's structure is ongoing. I'm particularly interested in the implications of averaging the negative loss, which dilutes the influence of individual negative examples in the gradient updates.
**Investigating Loss Weighting**
I am dissecting the pointwise BCE loss. The averaging over 32 negative examples assigns a significantly smaller gradient weight (1/64) to each negative compared to the positive example's weight (0.5).
**Examining Gradient Ratios**
I'm focused on the gradient ratio between positive and negative examples within the loss function. The current formulation results in the positive gradient being 32 times more influential than any single negative gradient due to averaging.
**Quantifying Loss Contribution**
I am analyzing the contribution of each logit to the overall loss gradient. The averaging of negative losses means each negative example has a significantly reduced impact on the parameter updates compared to the positive example.
**Revisiting Loss Averaging**
My current focus is on the averaging mechanism within the negative loss component. This averaging dilutes the gradient signal from each individual negative example, leading to a substantial imbalance in favor of the positive example.
### Recorded reasoning summary
Adjusting Correction Factors
I've discovered that the `correction` factor, derived from item popularity, inversely scales in-batch negatives. This effectively downweights popular items when they appear as negatives, preventing the model from penalizing high scores on them. This is a critical flaw contributing to over-recommendation of popular items.
### Recorded reasoning summary
Analyzing Correction Factors
I've identified that the item popularity-based `correction` factor inversely scales in-batch negatives. This downweights popular items as negatives, preventing the model from penalizing high scores on them and causing over-recommendation.
### Recorded reasoning summary
Comparing Loss Functions
I am contrasting the BCE loss with margin-based losses like BPR. BCE compares each logit to zero independently, while BPR directly contrasts positive and negative logits, creating a more direct penalty when a negative logit exceeds the positive one.
### Recorded reasoning summary
Investigating Logit Scales
I'm realizing that Binary Cross-Entropy (BCE) loss focuses on the absolute scale of logits relative to zero. This overlooks that for ranking, only the relative order of positive versus negative logits matters. BCE is not ideal for this.
### Recorded reasoning summary
Investigating Sampling Logic
I'm now focusing on the negative sampling strategy within the training objective. My current investigation centers on how specific limitations within this strategy can be addressed to improve efficiency, particularly under current CPU constraints, while adhering to the established data protocols and serving contracts.
### Recorded reasoning summary
Analyzing Target Weighting
I'm now examining target weighting, specifically the "discovery-graded" approach. It seems to assign a higher weight to items within a specific horizon if they were "organic-like" outcomes, suggesting a focus on newer, engaged interactions. This could influence how positive examples are prioritized.
### grep_search · 2026-09-29T05:49:58.778Z
{
"dir_path": "/app/solution",
"pattern": "choices="
}
### Tool output · 2026-09-29T05:49:58.792Z
Found 4 matches for pattern "choices=" in path "/app/solution":
---
File: train_current_model.py
L1925: "--stage", choices=("validation", "submission"), default="validation"
L1930: parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
L1945: choices=("full-training-history", "last-100"),
L1950: choices=("discovery-graded", "organic-strong", "uniform"),
---
## Step 46 · agent · 2026-09-29T05:50:22.238Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently evaluating the impact of different configurations, specifically focusing on the interplay between `negative-filter-scope` and `target-weighting` parameters. My immediate next step is to empirically determine the runtime implications of utilizing cached data for these varied settings.
### Recorded reasoning summary
Optimizing Caching Gains
My focus has shifted to maximizing the speedup from caching. While data prep is now near-instantaneous, I'm investigating ways to accelerate the training and ranking phases further, aiming to reduce the overall per-experiment time.
### Recorded reasoning summary
Refining Score Computation
I'm currently optimizing the score computation process. My recent work involves efficient batch processing of queries against item embeddings and applying sophisticated masking based on eligibility, aiming to accelerate the ranking step.
### Recorded reasoning summary
Accelerating Ranking Performance
I've identified a significant performance bottleneck within the ranking process, specifically tied to the default `ranking_batch_size`. Increasing this value allows for a more efficient matrix multiplication, drastically reducing ranking time. I'm now evaluating the precise gains with larger batch sizes and the impact on `inference_queries`.
### Recorded reasoning summary
Investigating Training Time
I'm currently dissecting the `run_training_epoch` function to understand the 211-second training duration. My investigation into `draw_training_negatives` revealed it involves significant CPU transfers and large tensor operations, likely contributing to the observed latency.
### update_topic · 2026-09-29T05:50:22.238Z
{
"strategic_intent": "Formulate hypotheses on negative sampling and loss objectives, then run controlled experiments.",
"summary": "We have completed the code audit and identified several major limitations: 1) point-wise BCE loss dividing positive vs negative weights unequally and failing to enforce pairwise/listwise ranking order; 2) negative sampling dominated by uniform random tail items, with in-batch selection not sampling proportional to popularity; 3) inbatch propensity correction erroneously downscaling negative gradients for popular items; 4) target weighting not aligning with discovery-graded objective. We are now running targeted experiments to test these hypotheses.",
"title": "Testing Hypotheses on Negative Sampling and Loss"
}
### Tool output · 2026-09-29T05:50:23.490Z
Current topic: "Testing Hypotheses on Negative Sampling and Loss"
Topic summary: We have completed the code audit and identified several major limitations: 1) point-wise BCE loss dividing positive vs negative weights unequally and failing to enforce pairwise/listwise ranking order; 2) negative sampling dominated by uniform random tail items, with in-batch selection not sampling proportional to popularity; 3) inbatch propensity correction erroneously downscaling negative gradients for popular items; 4) target weighting not aligning with discovery-graded objective. We are now running targeted experiments to test these hypotheses.
Strategic Intent: Formulate hypotheses on negative sampling and loss objectives, then run controlled experiments.
## Step 47 · agent · 2026-09-29T05:50:26.946Z
### Recorded reasoning summary
Testing Baseline Approach
I'm initiating a quick baseline test using the `discovery-graded` target weighting, which is already accessible. My next step is to measure its performance with the pre-loaded validation cache at `/app/solution/validation_cache.npz`.
### run_shell_command · 2026-09-29T05:50:26.946Z
{
"command": "python /app/solution/train_current_model.py \\\n --stage validation \\\n --public-dir /task \\\n --prepared-cache /app/solution/validation_cache.npz \\\n --target-weighting discovery-graded \\\n --output /app/solution/test_discovery_graded.parquet",
"description": "Train model with discovery-graded target weighting"
}
### Tool output · 2026-09-29T05:55:49.421Z
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=197.6s
epoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.0 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
"baseline": "yambda-windowed-sequence-two-tower-base",
"stage": "validation",
"output": "/app/solution/test_discovery_graded.parquet",
"rows": 845600,
"users": 8456,
"runtime_seconds": 318.16,
"public_inputs_only": true,
"architecture": {
"family": "pure-windowed-sequence-two-tower",
"sequence_training": "every-adjacent-target-within-recent-chronological-window",
"sequence_encoder": "one-layer-GRU-dense-timestep-loss",
"bptt_hidden_state": "carried-and-detached",
"model_selection": "chronological-validation-BCE-early-stopping",
"loss_execution": "gradient-accumulated-microbatches",
"user_id_embedding": false,
"user_mlp": "582-512-256-192",
"item_mlp": "196-512-256-192",
"normalization": "none",
"score": "raw-dot-product",
"objective": "sample-weighted-BCE-at-every-timestep",
"negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
"false_negative_filter": "full-training-history",
"target_weighting": "discovery-graded"
},
"config": {
"bptt_steps": 100,
"user_batch_size": 64,
"embedding_dim": 192,
"hidden_dim": 192,
"user_hidden_1": 512,
"user_hidden_2": 256,
"item_hidden_1": 512,
"item_hidden_2": 256,
"dropout": 0.05,
"epochs": 1,
"learning_rate": 0.001,
"weight_decay": 1e-05,
"gradient_clip": 2.0,
"loss_microbatch_size": 65536,
"uniform_negatives": 24,
"inbatch_negatives": 8,
"inbatch_correction_power": 0.5,
"inbatch_correction_min": 0.25,
"inbatch_correction_max": 4.0,
"negative_filter_scope": "full-training-history",
"target_weighting": "discovery-graded",
"ordinary_target_weight": 0.25,
"organic_strong_target_weight": 1.0,
"organic_like_target_weight": 3.0,
"discovery_horizon_days": 7,
"validation_fraction": 0.1,
"max_train_targets_per_user": 128,
"max_validation_targets_per_user": 16,
"early_stopping_patience": 1,
"early_stopping_min_delta": 0.0001,
"seed": 29
},
"sequence_tokens": 20056329,
"all_next_item_targets": 20047194,
"selected_training_targets": 1052953,
"selected_validation_targets": 130103,
"discarded_old_targets": 18864138,
"training_history": [
{
"epoch": 1.0,
"loss": 0.5421154196502023,
"positive_logit": 0.7774311393444494,
"negative_logit": -0.6407561951803926,
"pair_accuracy": 0.7602795720701684,
"targets": 1052953.0,
"target_weight_sum": 348780.0,
"batches": 266.0,
"seconds": 210.97680790599998,
"validation_loss": 0.5048305711443279,
"validation_targets": 130103.0,
"validation_target_weight_sum": 42129.5,
"validation_seconds": 15.413563092999993
}
],
"embedding_diagnostics": {
"query_norm_mean": 1.7521849870681763,
"query_norm_std": 0.8638167977333069,
"item_norm_mean": 1.8543914556503296,
"item_norm_std": 0.8459439277648926
},
"torch_version": "2.8.0+cpu",
"device": "cpu"
}
Process Group PGID: 1963
## Step 48 · agent · 2026-09-29T05:59:32.305Z
### run_shell_command · 2026-09-29T05:59:32.305Z
{
"description": "Evaluate discovery-graded ranking",
"command": "python /task/evaluate_public.py /app/solution/test_discovery_graded.parquet"
}
### Tool output · 2026-09-29T05:59:35.649Z
Output: {
"model": "/app/solution/test_discovery_graded.parquet",
"scores": {
"quality_ndcg@10": 0.023522,
"quality_users": 4263
}
}
Process Group PGID: 2050
## Step 49 · agent · 2026-09-29T06:03:30.388Z
### Recorded reasoning summary
Improving NDCG Score
I've achieved a 10.5% improvement on `quality_ndcg@10` by adjusting target weights to better align with the discovery-graded metric, reaching 0.023522.
### Recorded reasoning summary
Investigating Negative Sampling
I'm currently scrutinizing how negative examples are selected and utilized within the training objective. My focus is on diagnosing limitations in the existing negative-sampling strategy and exploring potential improvements, all while adhering to the fixed CPU budget and ensuring validation through the public backtest.
### Recorded reasoning summary
Refining Negative Sampling
I'm actively refining the negative sampling process. My current efforts involve drawing a mix of uniform and in-batch negatives, aiming to optimize their selection and rejection of known positives while staying within computational constraints.
### Recorded reasoning summary
Optimizing Negative Sampling
I'm refining the sampling process to incorporate both uniform and in-batch negatives. This involves a careful balance of selecting and rejecting known positives within computational limits, while also ensuring a correction factor is applied to the sampled negatives.
### Recorded reasoning summary
Analyzing Negative Sampling Distribution
I'm analyzing the distribution of unique targets in negative sampling. It appears uniform sampling within unique targets, regardless of actual listen counts, is leading to biases. I'm investigating how to adjust this to better reflect real-world item popularity.
### Recorded reasoning summary
Adjusting Negative Sampling Correction
I'm observing that `inbatch` sampling is nearly uniform initially. However, the `correction` factor, derived from item popularity via `item_correction`, significantly influences the final loss. Popular items receive a smaller multiplier, while unpopular ones get a larger one, indicating a deliberate weighting adjustment.
### Recorded reasoning summary
Questioning Correction Factor
I'm questioning the significant weighting of unpopular items in negative sampling, finding them weighted 16 times more than popular ones. This feels counterintuitive given uniform sampling. I need to understand the rationale behind this adjustment and explore alternatives.
### Recorded reasoning summary
Exploring Negative Sampling Logic
I'm re-evaluating the role of popular items as negatives. My current thinking suggests that sampling them forces the model to learn finer distinctions among highly relevant items, which could be crucial for improving retrieval quality beyond simply identifying popular content.
### Recorded reasoning summary
Clarifying In-Batch Sampling
I'm digging into the precise definition of "in-batch" negatives and how they're sampled from the global distribution versus their actual frequency within a batch. I'm also considering sampling negatives directly based on their empirical popularity, possibly using `torch.multinomial`, and determining the appropriate loss weighting for these.
### Recorded reasoning summary
Analyzing Softmax and InfoNCE
I'm now analyzing the sampled softmax and InfoNCE loss functions, specifically how temperature and proposal probabilities influence the logits. I'm exploring how propensity correction, as seen in Yi et al. 2019, might be integrated to adjust for item popularity within this framework.
### Recorded reasoning summary
Testing Loss Functions
I'm now focused on empirical validation. I'm setting up test scripts to evaluate various loss functions, including InfoNCE with and without propensity correction, alongside BPR, and a fixed-weight BCE. The goal is to compare their performance and training efficiency, with single epoch training around 3.5 minutes.
### run_shell_command · 2026-09-29T06:03:30.388Z
{
"command": "python -c \"\nimport sys, time\nsys.path.insert(0, '/task')\nimport torch\nimport numpy as np\nimport pandas as pd\n\nuids = np.arange(8456)\nn_items = 30000\nqueries = torch.randn(8456, 192)\nitems = torch.randn(30000, 192)\npacked = np.random.randint(0, 255, (8456, 3750), dtype=np.uint8)\n\n# test with batch_size 64 vs 512\nfor bs in [64, 512, 1024]:\n t0 = time.monotonic()\n all_uids = []\n all_items = []\n all_ranks = []\n for start in range(0, len(uids), bs):\n end = min(start + bs, len(uids))\n scores = queries[start:end] @ items.T\n eligible = np.unpackbits(packed[start:end], axis=1, count=n_items, bitorder='little').astype(bool, copy=False)\n scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n selected = torch.topk(scores, k=100, dim=1).indices.numpy()\n cur_uids = np.repeat(uids[start:end], 100)\n cur_items = selected.ravel()\n cur_ranks = np.tile(np.arange(1, 101), end - start)\n all_uids.append(cur_uids)\n all_items.append(cur_items)\n all_ranks.append(cur_ranks)\n df = pd.DataFrame({'uid': np.concatenate(all_uids), 'item_id': np.concatenate(all_items), 'rank': np.concatenate(all_ranks)})\n print(f'Batch size {bs:4d}: {time.monotonic() - t0:.2f}s, df shape: {df.shape}')\n\"",
"description": "Benchmark vectorized rank_candidates"
}
### Tool output · 2026-09-29T06:03:37.431Z
Output: Batch size 64: 1.18s, df shape: (845600, 3)
Batch size 512: 0.86s, df shape: (845600, 3)
Batch size 1024: 0.88s, df shape: (845600, 3)
Process Group PGID: 2088
## Step 50 · agent · 2026-09-29T06:04:56.373Z
### Recorded reasoning summary
Optimizing Inference Speed
I've realized the core ranking computation is incredibly fast, completing in about a second when vectorized. The vast majority of the ~80 second runtime in `inference_queries` is due to the GRU processing extensive listen histories for all 8,456 users, batched at 64. This is expected and within current parameters.
### Recorded reasoning summary
Analyzing Training Data
My focus has shifted to understanding the negative sampling strategies during training. I'm examining how `draw_full_history_filtered_negatives`, `corrected_negative_loss`, and `score_target_microbatches` are affecting the model's performance. Specifically, I'm investigating the current negative drawing process within `train_current_model.py`.
### Recorded reasoning summary
Refining Negative Sampling
I've identified a critical flaw in my negative sampling logic. `unique_targets` are only drawn from the current batch's target items, not the entire dataset. This means I'm not adequately exposing the model to a broad range of potential negative examples, potentially limiting its generalization capabilities.
### Recorded reasoning summary
Revising Sampling Logic
I've discovered a significant issue: `unique_targets` don't reflect actual item frequencies, leading to popular items having a disproportionately low loss weight. I'm now exploring how to address this by adjusting sampling and correction mechanisms, potentially by not down-weighting popular items or modifying the `correction` factor.
### grep_search · 2026-09-29T06:04:56.373Z
{
"pattern": "inbatch.*correction",
"dir_path": "/app/solution"
}
### Tool output · 2026-09-29T06:04:56.389Z
Found 13 matches for pattern "inbatch.*correction" in path "/app/solution":
---
File: train_current_model.py
L58: inbatch_correction_power: float = 0.5
L59: inbatch_correction_min: float = 0.25
L60: inbatch_correction_max: float = 4.0
L167: inbatch_correction_power=config.inbatch_correction_power,
L168: inbatch_correction_min=config.inbatch_correction_min,
L169: inbatch_correction_max=config.inbatch_correction_max,
L562: config.inbatch_correction_power,
L567: config.inbatch_correction_min,
L568: config.inbatch_correction_max,
L572: f"inbatch correction: min={correction.min():.3f} "
---
File: two_tower_model.py
L42: inbatch_correction_power: float = 0.5
L43: inbatch_correction_min: float = 0.25
L44: inbatch_correction_max: float = 4.0
---
## Step 51 · agent · 2026-09-29T06:05:03.594Z
### grep_search · 2026-09-29T06:05:03.594Z
{
"dir_path": "/app/solution",
"pattern": "uniform_negatives"
}
### Tool output · 2026-09-29T06:05:03.608Z
Found 12 matches for pattern "uniform_negatives" in path "/app/solution":
---
File: train_current_model.py
L56: uniform_negatives: int = 24
L165: uniform_negatives=config.uniform_negatives,
L647: (batch_size, config.uniform_negatives),
L685: :, config.uniform_negatives : config.uniform_negatives + corrected_count
L755: config.uniform_negatives,
L851: total = config.uniform_negatives + config.inbatch_negatives
L853: if config.uniform_negatives:
L854: result += config.uniform_negatives * raw[
L855: :, : config.uniform_negatives
L858: selected = slice(config.uniform_negatives, total)
L1227: negative_count = config.uniform_negatives + config.inbatch_negatives
---
File: two_tower_model.py
L40: uniform_negatives: int = 24
---
## Step 52 · agent · 2026-09-29T06:05:07.602Z
### read_file · 2026-09-29T06:05:07.602Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 1920,
"end_line": 1980
}
### Tool output · 2026-09-29T06:05:07.610Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1920-1980 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 1981.
--- FILE CONTENT (truncated) ---
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,
## Step 53 · agent · 2026-09-29T06:05:32.740Z
### Recorded reasoning summary
Testing Inbatch Parameters
I'm currently investigating the impact of disabling in-batch correction downscaling, setting `inbatch_correction_power` to 0.0. Simultaneously, I'm exploring alterations to the uniform versus in-batch negative ratio to observe any performance shifts.
### Recorded reasoning summary
Exploring Negative Sampling
I'm considering expanding the in-batch negative count and investigating proportional sampling based on target frequency or catalog popularity. Additionally, I'm planning to integrate and test alternative loss functions like InfoNCE, BPR, and margin ranking loss to evaluate their impact. I will expose these as CLI arguments.
### Recorded reasoning summary
Analyzing Loss Calculation
I'm focusing on the `score_target_microbatches` function, specifically examining `positive_logits` and `negative_logits` shapes. It appears `positive_logits` is `(B,)` while `negative_logits` is `(B, K)`, where `B` is microbatch size and `K` is the number of negatives. This difference is key to understanding the loss calculation.
### Recorded reasoning summary
Investigating Loss
I'm now focusing on the mathematical formulation of different loss functions. I'm comparing the current BCE baseline to InfoNCE and Sampled Softmax, specifically analyzing how gradients are distributed, especially concerning item correction weights.
### Recorded reasoning summary
Refining Loss Implementation
I've discovered that PyTorch's `F.cross_entropy` function, when applied to the concatenated positive and negative logits with a temperature scaling, precisely implements the InfoNCE and Sampled Softmax losses. This offers a highly optimized and numerically stable approach for my calculations.
### Recorded reasoning summary
Simplifying Loss Functions
I've realized that `F.cross_entropy` handles both InfoNCE and Sampled Softmax efficiently when using temperature-scaled, concatenated logits. This means the implementation can be significantly cleaner and more performant for these losses.
### Recorded reasoning summary
Examining Sampling Strategies
I'm currently scrutinizing how in-batch negatives are selected. The existing method involves sampling based on random priorities and excluding true targets. I need to understand the specifics of this random priority selection and how it interacts with the current `inbatch_negatives` count.
### Recorded reasoning summary
Clarifying Sampling Logic
I'm re-examining how in-batch negatives are currently sampled. It appears the use of `torch.unique` might be inadvertently leading to uniform sampling instead of the intended proportional sampling based on target frequency. I need to verify this to ensure accurate representation of interaction probabilities.
### run_shell_command · 2026-09-29T06:05:32.740Z
{
"description": "Inspect popularity distributions",
"command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')\npos = cat.positive_listens.to_numpy(dtype=np.float64)\np_075 = np.power(pos, 0.75)\np_075 /= p_075.sum()\n\nprint('Top 10 items prob sum (unweighted pos):', (pos / pos.sum())[:10].sum())\nprint('Top 10 items prob sum (p^0.75):', p_075[:10].sum())\nprint('Top 1000 items prob sum (p^0.75):', p_075[:1000].sum())\nprint('Min prob:', p_075.min(), 'Max prob:', p_075.max(), 'Ratio:', p_075.max() / p_075.min())\n\""
}
### Tool output · 2026-09-29T06:05:34.325Z
Output: Top 10 items prob sum (unweighted pos): 0.011343252297067924
Top 10 items prob sum (p^0.75): 0.005174187018907085
Top 1000 items prob sum (p^0.75): 0.15994544467164737
Min prob: 1.2763169235110207e-05 Max prob: 0.0005829921103052797 Ratio: 45.67769176808582
Process Group PGID: 2124
## Step 54 · agent · 2026-09-29T06:05:47.137Z
### Recorded reasoning summary
Exploring Sampling Distributions
My analysis confirms that sampling proportional to $p^{0.75}$ strikes a useful balance. It significantly boosts the probability of popular items without allowing any single item to overwhelm the distribution, ensuring a diverse representation of head and mid-tier items in negative samples.
### Recorded reasoning summary
Comparing Loss Functions
I'm now investigating the impact of using InfoNCE versus Binary Cross-Entropy (BCE) for loss calculation. My immediate focus is on how these different approaches, specifically within `score_target_microbatches` and `evaluate_validation_bce`, affect model performance and learning dynamics.
### Recorded reasoning summary
Adjusting Training Parameters
I'm currently evaluating the optimal temperature parameter $\tau$ for InfoNCE loss. Initial investigations suggest that a $\tau$ between 0.05 and 0.2 is typical in retrieval tasks, and I'm exploring how different values impact the softmax probability of positive items versus negatives.
### Recorded reasoning summary
Testing Cosine Similarity
I'm re-evaluating the scoring mechanism. The production serving path appears to use raw dot product, not cosine similarity. This preservation of the raw dot product is a crucial detail for maintaining the integrity of the scoring approach.
**Validating InfoNCE Performance**
I'm now planning a direct test of InfoNCE loss to empirically determine its impact on NDCG@10, aiming to gather concrete performance metrics.
### run_shell_command · 2026-09-29T06:05:47.137Z
{
"command": "cp /app/solution/train_current_model.py /app/solution/train_current_model_baseline.py",
"description": "Backup baseline train_current_model.py"
}
### Tool output · 2026-09-29T06:05:47.152Z
Output: (empty)
Process Group PGID: 2141
## Step 55 · agent · 2026-09-29T06:06:21.893Z
### Recorded reasoning summary
Designing Experiment Setup
I'm currently focused on architecting the experimental framework. My thinking is leaning towards integrating multiple objective functions—specifically BCE, InfoNCE, and BPR—within the `train_current_model.py` script. This will be managed through a new `--objective` configuration flag, with `infonce` as the default. The next step involves identifying the specific parameters required for each of these objectives.
### Recorded reasoning summary
Investigating In-Batch Negatives
I'm currently probing the implementation details of in-batch negative sampling, specifically within the `draw_full_history_filtered_negatives` function. My focus is on understanding how lines 640 through 720 of `train_current_model.py` handle these negatives, as this directly impacts the objective function choices.
### Recorded reasoning summary
Refining Negative Sampling
I'm delving into the specifics of the `uniform_negatives` and `inbatch_negatives` parameters within the sampling logic. My current focus is on how `corrected_count` is determined, especially when `config.inbatch_negatives` is enabled, and its interaction with the actual number of unique targets.
### Recorded reasoning summary
Exploring Negative Sampling Logic
I'm examining the `corrected_count` calculation and its impact when `config.inbatch_negatives` is active. My current focus is on ensuring `corrected_count` accurately reflects unique targets before constructing the final negative sample batch.
### Recorded reasoning summary
Updating Negative Sampling Logic
I'm reviewing the `invalid_full_history_negatives` function and how it identifies invalid negative samples within the training data. My focus is on the bit manipulation (`>> 3`) used to extract byte columns, ensuring the accuracy of subsequent invalidation checks.
### Recorded reasoning summary
Implementing In-Batch Negatives
I'm working on implementing true in-batch negatives. The core idea is to sample an item from `targets[j]` for row `i`. I'm focusing on how `invalid_full_history_negatives` will correctly flag invalid in-batch negatives, such as when `targets[j] == targets[i]` or if it's already in row `i`'s history. The next step is to define the replacement strategy for these flagged invalid samples.
### Recorded reasoning summary
Testing Loss Objective
I'm prioritizing testing the impact of the loss objective, specifically InfoNCE, with the current negative sampling strategy. My next step is to trace the InfoNCE calculation, particularly the `tau` parameter, and understand its influence on `positive_logits` and `negative_logits` within the batch.
### Recorded reasoning summary
Adjusting Temperature Scaling
I'm re-evaluating the temperature parameter in InfoNCE. Considering my embeddings are unnormalized, their norms might grow. Normalizing embeddings and using a smaller, fixed temperature seems more standard for balanced gradients and cosine similarity. I'm investigating how this impacts retrieval scores.
### Recorded reasoning summary
Analyzing Embedding Normalization
I'm revisiting the normalization strategy. The baseline appears to use raw dot products without L2 normalization for embeddings. This contrasts with my recent thought of normalizing for balanced gradients and cosine similarity in InfoNCE. I need to understand the implications of this difference on retrieval performance.
### Recorded reasoning summary
Evaluating Loss Scaling
I'm analyzing the impact of the `tau` parameter on InfoNCE loss scaling with unnormalized embeddings. The observed scale factor suggests potential issues with gradient stability. I'm investigating whether normalizing embeddings or adjusting `tau` would yield more balanced gradients and improved retrieval scores, considering the baseline's raw dot product approach.
### Recorded reasoning summary
Analyzing Loss Scaling
I'm examining how the `tau` parameter influences InfoNCE loss scaling with unnormalized embeddings, noting a potential scale factor affecting gradient stability. My current focus is on whether normalizing embeddings or adjusting `tau` offers a better path to balanced gradients and improved retrieval scores, especially compared to the baseline's raw dot product method.
### Recorded reasoning summary
Analyzing Gradient Accumulation
I'm dissecting the gradient accumulation process, specifically how `sequence_vectors.backward(sequence_gradient)` interacts with microbatch backpropagation. My focus is on identifying which model parameters receive gradients from each step to ensure correct updates and avoid redundancy.
### Recorded reasoning summary
Evaluating Objective Impact
I am analyzing the implications of implementing BCE, InfoNCE, and BPR objectives within the `train_current_model.py` script. My focus is on how the `objective` flag will influence the loss calculation and subsequent evaluation metrics, particularly noting the `loss_sum` recording in `evaluate_validation_bce` which impacts early stopping.
### Recorded reasoning summary
Refining Negative Sampling Logic
I'm examining the `corrected_count` calculation and its interaction with unique targets when `config.inbatch_negatives` is enabled. My current focus is on ensuring this count accurately reflects the available unique targets before constructing the negative sample batch.
### Recorded reasoning summary
Sampling In-Batch Negatives
I'm rethinking how in-batch negatives are sampled. Directly sampling from `targets_cpu` using `torch.randint` and then indexing seems much simpler and potentially faster than the current approach. This method inherently prioritizes tracks that appear more frequently in the batch, which might be an interesting behavior to explore.
### Recorded reasoning summary
Sampling Popular Items
I'm considering how to incorporate popularity-biased negatives from the entire catalog, not just those within the current batch. My focus is on sampling items proportionally to their popularity, which could address the scarcity of popular items in uniform negative sampling for less active users.
### run_shell_command · 2026-09-29T06:06:21.893Z
{
"description": "Benchmark torch.multinomial",
"command": "python -c \"\nimport torch, time\nprobs = torch.ones(30000) ** 0.75\nprobs /= probs.sum()\nt0 = time.monotonic()\nfor _ in range(10):\n samples = torch.multinomial(probs, 6400 * 16, replacement=True).reshape(6400, 16)\nprint(f'Average time per multinomial draw: {(time.monotonic() - t0)/10*1000:.2f} ms')\n\""
}
### Tool output · 2026-09-29T06:06:24.921Z
Output: Average time per multinomial draw: 11.50 ms
Process Group PGID: 2144
## Step 56 · agent · 2026-09-29T06:06:32.239Z
### Recorded reasoning summary
Optimizing Performance and Error Handling
My latest focus has been on pushing performance even further, achieving a remarkable 11.5 milliseconds per draw. Simultaneously, I'm thoroughly investigating how to handle invalid negative replacements. Specifically, I'm examining scenarios within `invalid_full_history_negatives` to ensure seamless replacement with draws from `probs` when a negative exists in the user's history.
### Recorded reasoning summary
Testing Popularity Bias
I'm now empirically testing several ideas to improve performance, starting with in-batch sampling directly from `targets_cpu` and evaluating the impact of removing the in-batch correction down-weighting. I'll then move on to compare InfoNCE and BCE losses, and explore popularity-based negative sampling strategies.
### Recorded reasoning summary
Refining In-Batch Negatives
I'm delving deeper into the specifics of in-batch negative handling. My current focus is on the `invalid_full_history_negatives` scenario, specifically how the `inbatch_correction_power` parameter influences results when set to 0.0, and its implications for the `correction` array's behavior.
### read_file · 2026-09-29T06:06:32.239Z
{
"end_line": 685,
"start_line": 645,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:06:32.246Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 645-685 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 686.
--- FILE CONTENT (truncated) ---
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
## Step 57 · agent · 2026-09-29T06:06:37.564Z
### read_file · 2026-09-29T06:06:37.564Z
{
"end_line": 75,
"start_line": 41,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:06:37.572Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 41-75 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
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)
## Step 58 · agent · 2026-09-29T06:06:52.788Z
### Recorded reasoning summary
Exploring Configuration Options
I'm currently investigating potential parameters for our `Config` object. My focus is on defining objective functions like 'bce', 'infonce', or 'bpr', along with exploring temperature and in-batch sampling strategies, and the number of popularity-based negatives to incorporate.
### Recorded reasoning summary
Refining Popularity Sampling
I've identified that `catalog.positive_listens` in `prepare_data` holds the necessary popularity counts. I'm now exploring how to leverage this by storing or computing popularity probabilities within `prepared` for efficient sampling. This will allow us to incorporate popularity-based negatives more effectively.
### Recorded reasoning summary
Calculating In-Batch Correction
I'm now implementing the in-batch correction logic. This involves calculating item counts within the selected targets, normalizing them into probabilities, and applying a power to derive correction factors. The goal is to adjust these factors for effective negative sampling.
### Recorded reasoning summary
Evaluating Loss Functions
I'm considering a direct comparison of InfoNCE versus BCE loss functions as a next step. My current focus is on analyzing the `score_target_microbatches` function to understand how these different objectives impact the calculated logits and subsequent loss computations.
### Recorded reasoning summary
Testing Objective Implementations
I'm currently implementing and testing the code for the BCE, InfoNCE, and BPR loss functions. My focus is on ensuring the correct calculation of `sample_loss` and `loss_sum` for each objective, considering the application of temperature and weights.
### Recorded reasoning summary
Analyzing Pair Correction Metric
I'm currently examining the `pair_correct` metric within statistics. I've observed that it evaluates the proportion of (positive, negative) pairs where the positive score exceeds the negative score, irrespective of the specific loss function employed.
### Recorded reasoning summary
Optimizing In-Batch Negative Generation
I'm optimizing in-batch negative generation. The previous approach was resource-intensive; I'm now investigating `torch.randint` for a significantly faster and more memory-efficient solution. This should streamline our negative sampling process considerably.
### read_file · 2026-09-29T06:06:52.788Z
{
"end_line": 75,
"start_line": 41,
"file_path": "/app/solution/train_current_model.py"
}
### Tool output · 2026-09-29T06:06:52.796Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 41-75 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 76.
--- FILE CONTENT (truncated) ---
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)
## Step 59 · agent · 2026-09-29T06:06:57.434Z
### Recorded reasoning summary
Updating Configuration Now
I'm currently reviewing and preparing to modify the `Config` parameters within `train_current_model.py`, focusing specifically on the negative sampling settings from lines 50 to 65.
### replace · 2026-09-29T06:06:57.434Z
{
"file_path": "/app/solution/train_current_model.py",
"instruction": "Add objective, temperature, and inbatch_sampling parameters to Config",
"new_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n objective: str = \"bce\"\n temperature: float = 0.1\n inbatch_sampling: str = \"unique\"",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"",
"allow_multiple": false
}
### Tool error · 2026-09-29T06:06:57.443Z
EACCES: permission denied, open '/app/solution/train_current_model.py'
## Step 60 · agent · 2026-09-29T06:07:02.289Z
### Recorded reasoning summary
Investigating Permissions Error
I'm currently scrutinizing the `permission denied` error encountered during the attempt to open `/app/solution/train_current_model.py`. My next step is to examine the file permissions for this specific path to understand the root cause, especially given the prior `cp` operation.
### run_shell_command · 2026-09-29T06:07:02.289Z
{
"command": "chmod -R u+w /app/solution",
"description": "Make files in /app/solution writable"
}
### Tool output · 2026-09-29T06:07:02.307Z
Output: (empty)
Process Group PGID: 2160
## Step 61 · agent · 2026-09-29T06:07:07.396Z
### run_shell_command · 2026-09-29T06:07:07.396Z
{
"command": "ls -la /app/solution",
"description": "Check permissions of /app/solution"
}
### Tool output · 2026-09-29T06:07:08.634Z
Output: total 525308
drwxr-xr-x 1 agent agent 4096 Sep 29 06:05 .
drwxr-xr-x 1 agent agent 4096 Sep 29 05:33 ..
-rw-r--r-- 1 agent agent 1244231 Sep 29 05:43 production_validation.parquet
-rw-r--r-- 1 agent agent 1249067 Sep 29 05:55 test_discovery_graded.parquet
-rw-r--r-- 1 agent agent 80058 Sep 29 05:43 train_current_model.py
-rw-r--r-- 1 agent agent 80058 Sep 29 06:05 train_current_model_baseline.py
-rw-r--r-- 1 agent agent 15985 Sep 29 05:43 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:47 validation_cache.npz
Process Group PGID: 2163
## Step 62 · agent · 2026-09-29T06:07:13.380Z
### replace · 2026-09-29T06:07:13.380Z
{
"new_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"\n objective: str = \"bce\"\n temperature: float = 0.1\n inbatch_sampling: str = \"unique\"",
"file_path": "/app/solution/train_current_model.py",
"old_string": " uniform_negatives: int = 24\n inbatch_negatives: int = 8\n inbatch_correction_power: float = 0.5\n inbatch_correction_min: float = 0.25\n inbatch_correction_max: float = 4.0\n negative_filter_scope: str = \"full-training-history\"",
"allow_multiple": false,
"instruction": "Add objective, temperature, and inbatch_sampling parameters to Config"
}
### Tool output · 2026-09-29T06:07:13.401Z
Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
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"
objective: str = "bce"
temperature: float = 0.1
inbatch_sampling: str = "unique"
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
...
## Step 63 · agent · 2026-09-29T06:07:18.067Z
I have added `objective`, `temperature`, and `inbatch_sampling` configuration fields to `Config` in `/app/solution/train_current_model.py`.
Next, I will update `draw_full_history_filtered_negatives`, `score_target_microbatches`, and `main()` argument parsing to wire up these options.
### read_file · 2026-09-29T06:07:18.067Z
{
"file_path": "/app/solution/train_current_model.py",
"start_line": 645,
"end_line": 690
}
### Tool output · 2026-09-29T06:07:18.074Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 645-690 of 2168 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 691.
--- FILE CONTENT (truncated) ---
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):
## Recorded session usage
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