yambda-discovery-ranking-two-tower-base / run-002-opus-high-2026-08-28T17-34-10 Recorded model: claude-opus-5 ## Step 1 · user · 2026-08-29T00:55:08.310Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. 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. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, 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")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. 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 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. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## 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. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ### Harness context { "type": "deferred_tools_delta", "addedNames": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskOutput", "TaskStop", "WebFetch", "WebSearch" ], "addedLines": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskOutput", "TaskStop", "WebFetch", "WebSearch" ], "removedNames": [], "wireHiddenNames": [], "readdedNames": [], "pendingMcpServers": [], "needsAuthMcpServers": [], "failedMcpServers": [] } ### Harness context { "type": "agent_listing_delta", "addedTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "addedLines": [ "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)", "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)", "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)", "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)", "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)" ], "removedTypes": [], "isInitial": true, "showConcurrencyNote": false } ### Harness context { "type": "skill_listing", "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch", "skillCount": 13, "isInitial": true, "names": [ "dataviz", "update-config", "keybindings-help", "code-review", "simplify", "fewer-permission-prompts", "loop", "schedule", "claude-api", "workflow-authoring", "run", "init", "security-review" ] } ### Harness context { "type": "total_tokens_reminder", "text": "15000000 tokens left" } ## Step 2 · agent · 2026-08-29T00:55:21.582Z I'll start by exploring the data and environment. ### Bash · 2026-08-29T00:55:21.582Z { "command": "ls -la /task/ /task/data/ && cat /task/requirements.txt | head -50", "description": "List task files" } ### Tool output · 2026-08-29T00:55:21.656Z /task/: total 36 dr-xr-xr-x 1 root root 4096 Aug 29 00:50 . drwxr-xr-x 1 root root 4096 Aug 29 00:53 .. -r--r--r-- 1 root root 0 Aug 29 00:37 .keep dr-xr-xr-x 1 root root 4096 Aug 29 00:37 data -r--r--r-- 1 root root 5774 Aug 29 00:37 evaluate_public.py -r--r--r-- 1 root root 8265 Aug 29 00:37 ranking_evaluation.py -r--r--r-- 1 root root 1295 Aug 29 00:37 requirements.txt /task/data/: total 347392 dr-xr-xr-x 1 root root 4096 Aug 29 00:37 . dr-xr-xr-x 1 root root 4096 Aug 29 00:50 .. -r--r--r-- 1 root root 349352 Aug 29 00:37 candidate_catalog.parquet -r--r--r-- 1 root root 84127 Aug 29 00:37 target_users.parquet -r--r--r-- 1 root root 7736341 Aug 29 00:37 test_eligible_candidates.npz -r--r--r-- 1 root root 1092329 Aug 29 00:37 test_preference_state.parquet -r--r--r-- 1 root root 857213 Aug 29 00:37 train_dislikes.parquet -r--r--r-- 1 root root 5374652 Aug 29 00:37 train_likes.parquet -r--r--r-- 1 root root 319183255 Aug 29 00:37 train_listens.parquet -r--r--r-- 1 root root 155988 Aug 29 00:37 train_undislikes.parquet -r--r--r-- 1 root root 1855885 Aug 29 00:37 train_unlikes.parquet -r--r--r-- 1 root root 36571 Aug 29 00:37 val_dislikes.parquet -r--r--r-- 1 root root 298484 Aug 29 00:37 val_likes.parquet -r--r--r-- 1 root root 9947609 Aug 29 00:37 val_listens.parquet -r--r--r-- 1 root root 7845 Aug 29 00:37 val_undislikes.parquet -r--r--r-- 1 root root 72536 Aug 29 00:37 val_unlikes.parquet -r--r--r-- 1 root root 7573821 Aug 29 00:37 validation_eligible_candidates.npz -r--r--r-- 1 root root 1061761 Aug 29 00:37 validation_preference_state.parquet # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 ### Bash · 2026-08-29T00:55:21.582Z { "command": "cat /task/ranking_evaluation.py", "description": "Read evaluation helpers" } ### Tool output · 2026-08-29T00:55:21.652Z """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" ) 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), } ### Harness context { "type": "total_tokens_reminder", "text": "14981752 tokens left" } ## Step 3 · agent · 2026-08-29T00:55:23.292Z ### Bash · 2026-08-29T00:55:23.292Z { "command": "cat /task/evaluate_public.py", "description": "Read public evaluator" } ### Tool output · 2026-08-29T00:55:23.304Z #!/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() ### Bash · 2026-08-29T00:55:25.186Z { "command": "python -c \"\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state']:\n d=pd.read_parquet(f'/task/data/{f}.parquet')\n print(f, d.shape, list(d.columns))\n print(d.head(3))\nprint(pq.ParquetFile('/task/data/train_listens.parquet').metadata)\n\"", "description": "Inspect data files" } ### Tool output · 2026-08-29T00:55:26.792Z candidate_catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] item_id positive_listens organic_positive_listens popularity_rank 0 5862961 26615 20620 1 1 6901374 25536 17347 2 2 5635052 24691 17194 3 target_users (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 validation_preference_state (469124, 3) ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked test_preference_state (484776, 3) ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked created_by: parquet-cpp-arrow version 18.1.0 num_columns: 6 num_rows: 43484159 num_row_groups: 42 format_version: 2.6 serialized_size: 25540 ### Harness context { "type": "total_tokens_reminder", "text": "14976639 tokens left" } ## Step 4 · agent · 2026-08-29T00:55:40.369Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:55:41.076Z { "command": "free -g; nproc; python -c \"import torch;print(torch.__version__)\"", "description": "Check resources" } ### Tool output · 2026-08-29T00:55:43.980Z total used free shared buff/cache available Mem: 15 1 0 0 13 14 Swap: 0 0 0 8 2.8.0+cpu ### Bash · 2026-08-29T00:55:42.579Z { "command": "mkdir -p solution && python -c \"\nimport numpy as np\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint({k:(v.shape,v.dtype) for k,v in z.items()})\np=z['packed_eligible']\nimport numpy as np\nn=len(z['item_ids'])\ncnt=np.unpackbits(p[:50],axis=1,count=n,bitorder='little').sum(1)\nprint(cnt[:20], cnt.mean())\n\"", "description": "Inspect eligibility" } ### Tool output · 2026-08-29T00:55:44.585Z {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736 29476 28476 27836 27959 29626 29355 28904 28594 29529 29364] 29108.8 ### Harness context { "type": "total_tokens_reminder", "text": "14972749 tokens left" } ## Step 5 · agent · 2026-08-29T00:55:48.295Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:55:55.121Z { "command": "cd /app/solution && cat > prep.py <<'EOF'\nimport numpy as np, pandas as pd, pyarrow.parquet as pq, time\nt0=time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat.item_id.to_numpy(np.int64)\nn_items = len(items)\nimap = {int(v):i for i,v in enumerate(items)}\nnp.save('items.npy', items)\n\ndef load_listens(path):\n pf = pq.ParquetFile(path)\n outs=[]\n for b in pf.iter_batches(batch_size=2_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n d = b.to_pandas()\n iid = d.item_id.map(imap)\n m = iid.notna()\n d = d[m]\n d['ii'] = iid[m].astype(np.int32)\n outs.append(d[['uid','timestamp','ii','is_organic','played_ratio_pct']].astype(\n {'uid':np.int32,'timestamp':np.int64,'ii':np.int32,'is_organic':np.int8,'played_ratio_pct':np.int16}))\n return pd.concat(outs, ignore_index=True)\n\nfor name in ['train','val']:\n L = load_listens(f'/task/data/{name}_listens.parquet')\n L.to_parquet(f'L_{name}.parquet')\n print(name, L.shape, time.time()-t0, flush=True)\n del L\n\nfor fam in ['likes','dislikes']:\n for name in ['train','val']:\n d = pd.read_parquet(f'/task/data/{name}_{fam}.parquet')\n iid = d.item_id.map(imap); m=iid.notna(); d=d[m]; d['ii']=iid[m].astype(np.int32)\n d[['uid','timestamp','ii','is_organic']].astype({'uid':np.int32,'timestamp':np.int64,'ii':np.int32,'is_organic':np.int8}).to_parquet(f'{fam[0]}_{name}.parquet')\n print(fam,name,d.shape, flush=True)\nprint('done', time.time()-t0)\nEOF\npython prep.py", "description": "Prepare compact data" } ### Tool output · 2026-08-29T00:56:02.748Z train (31249613, 5) 5.716594219207764 val (1011771, 5) 5.892027139663696 likes train (554824, 5) likes val (21055, 5) dislikes train (65321, 5) dislikes val (2012, 5) done 6.089343786239624 ### Harness context { "type": "total_tokens_reminder", "text": "14971477 tokens left" } ## Step 6 · agent · 2026-08-29T00:56:32.112Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:56:34.707Z { "command": "python -c \"\nimport pandas as pd, numpy as np\nL=pd.read_parquet('L_train.parquet')\nprint(L.timestamp.min(), L.timestamp.max())\nprint('users', L.uid.nunique())\nV=pd.read_parquet('L_val.parquet'); print('val ts', V.timestamp.min(), V.timestamp.max(), V.uid.nunique())\ntu=pd.read_parquet('/task/data/target_users.parquet').uid.to_numpy()\nprint('target in train users', np.isin(tu, L.uid.unique()).mean())\nprint(L.played_ratio_pct.describe())\nprint('organic frac', L.is_organic.mean())\n\"", "description": "Timestamp and user stats" } ### Tool output · 2026-08-29T00:56:37.743Z 0 24786795 users 9217 val ts 24788600 25393395 7947 target in train users 0.9995269631031221 count 3.124961e+07 mean 6.550468e+01 std 4.389650e+01 min 0.000000e+00 25% 8.000000e+00 50% 1.000000e+02 75% 1.000000e+02 max 1.590000e+02 Name: played_ratio_pct, dtype: float64 organic frac 0.4998293578867681 ### Harness context { "type": "total_tokens_reminder", "text": "14969147 tokens left" } ## Step 7 · agent · 2026-08-29T00:56:49.258Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:56:54.689Z { "command": "python -c \"\nimport numpy as np, pandas as pd\nz=np.load('/task/data/validation_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); n=30000\np=z['packed_eligible']\nitems=np.load('items.npy')\n# note npz item order vs catalog order\nprint('same order', (z['item_ids'].astype(np.int64)==items).all())\nL=pd.read_parquet('L_train.parquet')\nK=pd.read_parquet('l_train.parquet')\nD=pd.read_parquet('d_train.parquet')\nfor r in [0,1,5]:\n u=uids[r]\n elig=np.unpackbits(p[r],count=n,bitorder='little').astype(bool)\n inel=set(np.nonzero(~elig)[0].tolist())\n su=L[L.uid==u]\n seen_all=set(su.ii.unique().tolist())\n seen_pos=set(su[su.played_ratio_pct>=50].ii.unique().tolist())\n liked=set(K[K.uid==u].ii.unique().tolist()); dis=set(D[D.uid==u].ii.unique().tolist())\n print(u, len(inel), 'allseen',len(seen_all),'pos',len(seen_pos),'liked',len(liked),'dis',len(dis))\n print(' inel-liked-dis', len(inel-liked-dis), 'liked+dis not inel', len((liked|dis)-inel))\n print(' inel subset of seen?', len(inel-seen_all))\n\"", "description": "Check eligibility semantics" } ### Tool output · 2026-08-29T00:56:57.090Z same order True 100 682 allseen 682 pos 600 liked 13 dis 2 inel-liked-dis 667 liked+dis not inel 0 inel subset of seen? 0 200 168 allseen 168 pos 163 liked 0 dis 0 inel-liked-dis 168 liked+dis not inel 0 inel subset of seen? 0 800 2524 allseen 2524 pos 1910 liked 86 dis 14 inel-liked-dis 2424 liked+dis not inel 0 inel subset of seen? 0 ### Harness context { "type": "total_tokens_reminder", "text": "14967712 tokens left" } ## Step 8 · agent · 2026-08-29T00:57:23.701Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:57:42.592Z { "command": "cat > build.py <<'EOF'\nimport numpy as np, pandas as pd, time, sys\nt0=time.time()\nT0=24786800; WEEK=604800\nINCLUDE_VAL = (len(sys.argv)>1 and sys.argv[1]=='final')\nNI=30000; K=128\n\ndef load_events(splits):\n parts=[]\n for s in splits:\n L=pd.read_parquet(f'L_{s}.parquet')\n kind=np.where(L.played_ratio_pct.to_numpy()>=50, np.where(L.is_organic.to_numpy()==1,2,1), 0).astype(np.int8)\n strong=((L.played_ratio_pct.to_numpy()>=80)&(L.is_organic.to_numpy()==1)).astype(np.int8)\n parts.append(pd.DataFrame({'uid':L.uid.to_numpy(),'ts':L.timestamp.to_numpy(),'ii':L.ii.to_numpy(),'kind':kind,'strong':strong}))\n for fam,kd in [('l',3),('d',4)]:\n E=pd.read_parquet(f'{fam}_{s}.parquet')\n parts.append(pd.DataFrame({'uid':E.uid.to_numpy(),'ts':E.timestamp.to_numpy(),'ii':E.ii.to_numpy(),\n 'kind':np.full(len(E),kd,np.int8),\n 'strong':(np.full(len(E),3,np.int8) if kd==3 else np.zeros(len(E),np.int8))*(E.is_organic.to_numpy()==1)}))\n return pd.concat(parts,ignore_index=True)\n\nev = load_events(['train','val'] if INCLUDE_VAL else ['train'])\nuids = np.sort(ev.uid.unique()); nu=len(uids)\numap = {int(u):i for i,u in enumerate(uids)}\nev['ui']=ev.uid.map(umap).astype(np.int32)\nev = ev.sort_values(['ui','ts'], kind='stable').reset_index(drop=True)\nui=ev.ui.to_numpy(np.int32); ts=ev.ts.to_numpy(np.int64); ii=ev.ii.to_numpy(np.int32)\nkind=ev.kind.to_numpy(np.int8); strong=ev.strong.to_numpy(np.int8)\nprint('events',len(ev),nu,time.time()-t0,flush=True)\n\n# first-interaction time per (user,item)\nkey = ui.astype(np.int64)*NI + ii\norder = np.argsort(key, kind='stable') # stable keeps ts order within key\nks = key[order]\nfirstmask = np.empty(len(ks), bool); firstmask[0]=True; firstmask[1:]= ks[1:]!=ks[:-1]\nuk = ks[firstmask]; uft = ts[order][firstmask]\nis_first = np.zeros(len(ks), bool); is_first[order[firstmask]] = True\nprint('pairs',len(uk),time.time()-t0,flush=True)\n\nustart = np.searchsorted(ui, np.arange(nu)); uend = np.searchsorted(ui, np.arange(nu), side='right')\n\nCUT_END = T0 + (WEEK if INCLUDE_VAL else 0)\ncuts = [CUT_END - WEEK - int(k*WEEK/2) for k in range(0,16)]\npos_kind = (kind>=1)&(kind<=3)\n\nH=[]; HK=[]; HT=[]; EU=[]; TE=[]; TI=[]; TG=[]\nfor T in cuts:\n lo = np.searchsorted(ts, T) # not global, need per-user\n for u in range(nu):\n a,b = ustart[u], uend[u]\n if b<=a: continue\n c = a + np.searchsorted(ts[a:b], T)\n e = a + np.searchsorted(ts[a:b], T+WEEK)\n # targets\n if e>c:\n sl = slice(c,e)\n m = (strong[sl]>0) & is_first[sl]\n if m.any():\n ti = ii[sl][m]; tg = strong[sl][m]\n # dedupe keep max grade\n o=np.argsort(-tg,kind='stable'); ti=ti[o]; tg=tg[o]\n _,uidx=np.unique(ti,return_index=True)\n ti=ti[uidx]; tg=tg[uidx]\n else: continue\n else: continue\n # history: last K positive events before T\n hidx = np.nonzero(pos_kind[a:c])[0]\n if len(hidx)<5: continue\n hidx = hidx[-K:] + a\n L=len(hidx)\n hi=np.zeros(K,np.int32); hk=np.zeros(K,np.int8); ht=np.zeros(K,np.int8)\n hi[K-L:]=ii[hidx]; hk[K-L:]=kind[hidx]\n dt=(T-ts[hidx])/86400.0\n ht[K-L:]=np.clip(np.log1p(dt)*2.0,0,15).astype(np.int8)+1\n eid=len(H)\n H.append(hi); HK.append(hk); HT.append(ht); EU.append(u)\n TE.append(np.full(len(ti),eid,np.int32)); TI.append(ti); TG.append(tg)\n print('cut',T,len(H),time.time()-t0,flush=True)\n\nnp.savez_compressed('train_'+('final' if INCLUDE_VAL else 'dev')+'.npz',\n H=np.array(H,np.int32),HK=np.array(HK,np.int8),HT=np.array(HT,np.int8),EU=np.array(EU,np.int32),\n TE=np.concatenate(TE),TI=np.concatenate(TI),TG=np.concatenate(TG).astype(np.int8),\n uids=uids)\nprint('saved',len(H),sum(len(x) for x in TI),time.time()-t0)\nEOF\npython build.py 2>&1 | tail -25", "description": "Build training examples" } ### Tool output · 2026-08-29T00:58:01.552Z events 31869758 9979 7.242335319519043 pairs 8054252 9.165929555892944 cut 24182000 4184 9.52771544456482 cut 23879600 8293 9.808139562606812 cut 23577200 12328 10.085317850112915 cut 23274800 16295 10.362085103988647 cut 22972400 20177 10.631194591522217 cut 22670000 23968 10.89437222480774 cut 22367600 27823 11.160430192947388 cut 22065200 31704 11.42930293083191 cut 21762800 35507 11.691993713378906 cut 21460400 39263 11.951335430145264 cut 21158000 42932 12.206868648529053 cut 20855600 46551 12.466639041900635 cut 20553200 50210 12.722816228866577 cut 20250800 53776 12.973493337631226 cut 19948400 57364 13.223674774169922 cut 19646000 60847 13.47585916519165 saved 60847 481369 17.281908750534058 ### Harness context { "type": "total_tokens_reminder", "text": "14963588 tokens left" } ## Step 9 · agent · 2026-08-29T00:58:21.635Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T00:58:32.133Z { "command": "cat > model.py <<'EOF'\nimport numpy as np, torch, torch.nn as nn, torch.nn.functional as F\n\nNI=30000\n\nclass ItemTower(nn.Module):\n \"\"\"Item tower: embedding -> residual MLP -> d-dim vector; plus learned bias appended.\"\"\"\n def __init__(self, d):\n super().__init__()\n self.emb = nn.Embedding(NI, d)\n nn.init.normal_(self.emb.weight, std=0.05)\n self.f1 = nn.Linear(d, d); self.f2 = nn.Linear(d, d)\n nn.init.zeros_(self.f2.weight); nn.init.zeros_(self.f2.bias)\n self.bias = nn.Embedding(NI, 1); nn.init.zeros_(self.bias.weight)\n def forward(self, idx=None):\n e = self.emb.weight if idx is None else self.emb(idx)\n v = e + self.f2(F.gelu(self.f1(e)))\n b = (self.bias.weight if idx is None else self.bias(idx)).squeeze(-1)\n return v, b\n\nclass UserTower(nn.Module):\n \"\"\"User tower: weighted pooling over history item embeddings -> residual MLP.\"\"\"\n def __init__(self, d, n_kind=5, n_time=17):\n super().__init__()\n self.emb = nn.Embedding(NI, d, padding_idx=None)\n nn.init.normal_(self.emb.weight, std=0.05)\n self.kw = nn.Embedding(n_kind, 1); nn.init.zeros_(self.kw.weight)\n self.tw = nn.Embedding(n_time, 1); nn.init.zeros_(self.tw.weight)\n self.ln = nn.LayerNorm(d)\n self.f1 = nn.Linear(d, 2*d); self.f2 = nn.Linear(2*d, d)\n nn.init.zeros_(self.f2.weight); nn.init.zeros_(self.f2.bias)\n self.scale = nn.Parameter(torch.tensor(3.0))\n def forward(self, H, HK, HT):\n mask = (HK > 0).float() # (B,K)\n h = self.emb(H) # (B,K,d)\n logit = (self.kw(HK) + self.tw(HT)).squeeze(-1)\n w = torch.exp(logit.clamp(-6, 6)) * mask\n w = w / (w.sum(1, keepdim=True) + 1e-6)\n pooled = torch.einsum('bk,bkd->bd', w, h)\n z = self.ln(pooled)\n u = z + self.f2(F.gelu(self.f1(z)))\n return u * F.softplus(self.scale)\n\nclass TwoTower(nn.Module):\n def __init__(self, d=64):\n super().__init__()\n self.user = UserTower(d); self.item = ItemTower(d)\n def user_vec(self, H, HK, HT):\n u = self.user(H, HK, HT)\n return torch.cat([u, torch.ones(u.shape[0], 1, dtype=u.dtype)], 1)\n def item_vec(self, idx=None):\n v, b = self.item(idx)\n return torch.cat([v, b.unsqueeze(-1)], -1)\nEOF\necho ok", "description": "Write model" } ### Tool output · 2026-08-29T00:58:32.155Z ok ### Harness context { "type": "total_tokens_reminder", "text": "14960694 tokens left" } ## Step 10 · agent · 2026-08-29T00:58:42.587Z ### Bash · 2026-08-29T00:58:42.587Z { "command": "cat > train.py <<'EOF'\nimport numpy as np, torch, torch.nn as nn, torch.nn.functional as F, time, sys, os\nfrom model import TwoTower, NI\ntorch.set_num_threads(8)\n\ntag = sys.argv[1] if len(sys.argv)>1 else 'dev'\nEPOCHS = int(os.environ.get('EPOCHS','6')); D=int(os.environ.get('D','64'))\nLR=float(os.environ.get('LR','3e-3')); B=int(os.environ.get('B','1024'))\nNEG=int(os.environ.get('NEG','30000'))\n\nz=np.load(f'train_{tag}.npz')\nH=torch.from_numpy(z['H'].astype(np.int64)); HK=torch.from_numpy(z['HK'].astype(np.int64)); HT=torch.from_numpy(z['HT'].astype(np.int64))\nTE=torch.from_numpy(z['TE'].astype(np.int64)); TI=torch.from_numpy(z['TI'].astype(np.int64)); TG=torch.from_numpy(z['TG'].astype(np.float32))\nW = torch.where(TG>=3, 1.6, 1.0)\nN=len(TE); print('pairs',N,'examples',len(H),flush=True)\n\nm=TwoTower(D)\nopt=torch.optim.AdamW(m.parameters(), lr=LR, weight_decay=1e-5)\nsteps=EPOCHS*((N+B-1)//B)\nsched=torch.optim.lr_scheduler.OneCycleLR(opt, max_lr=LR, total_steps=steps, pct_start=0.15)\nt0=time.time(); step=0\nfor ep in range(EPOCHS):\n perm=torch.randperm(N)\n tot=0.0; nb=0\n for s in range(0,N,B):\n idx=perm[s:s+B]\n e=TE[idx]; tgt=TI[idx]\n u=m.user_vec(H[e],HK[e],HT[e])\n if NEG>=NI:\n v=m.item_vec()\n logits=u@v.T\n else:\n neg=torch.randint(0,NI,(NEG,))\n cand=torch.cat([tgt,neg])\n v=m.item_vec(cand)\n logits=u@v.T\n tgt=torch.arange(len(idx))\n logits=logits.scatter(1, H[e], -1e4) if NEG>=NI else logits\n loss=(F.cross_entropy(logits,tgt,reduction='none')*W[idx]).mean()\n opt.zero_grad(); loss.backward(); opt.step(); sched.step()\n tot+=loss.item(); nb+=1; step+=1\n print(f'ep{ep} loss {tot/nb:.4f} {time.time()-t0:.0f}s',flush=True)\ntorch.save(m.state_dict(), f'model_{tag}.pt')\nprint('saved', time.time()-t0)\nEOF\nEPOCHS=1 timeout 600 python train.py dev 2>&1 | tail -5", "description": "Benchmark one epoch" } ### Tool output · 2026-08-29T01:00:43.385Z Command did not complete within its 120s timeout and was moved to the background (ID: bfetsbz4b). Output is being written to: /tmp/claude-1000/-app/746159f9-6934-411c-832b-430fa9886b51/tasks/bfetsbz4b.output. You will be notified when it completes. To check interim output, use Read on that file path. ### Harness context { "type": "total_tokens_reminder", "text": "14959533 tokens left" } ## Step 11 · agent · 2026-08-29T01:00:49.368Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:01:04.212Z { "command": "cat > infer.py <<'EOF'\n\"\"\"Build user histories at a cutoff, score all catalog items, emit top-100 ranking.\"\"\"\nimport numpy as np, pandas as pd, torch, sys, time\nfrom model import TwoTower, NI\ntorch.set_num_threads(8)\nT0=24786800; WEEK=604800; K=128\n\ndef build_hist(splits, T):\n parts=[]\n for s in splits:\n L=pd.read_parquet(f'L_{s}.parquet')\n kind=np.where(L.played_ratio_pct.to_numpy()>=50, np.where(L.is_organic.to_numpy()==1,2,1),0).astype(np.int8)\n parts.append(pd.DataFrame({'uid':L.uid.to_numpy(),'ts':L.timestamp.to_numpy(),'ii':L.ii.to_numpy(),'kind':kind}))\n for fam,kd in [('l',3),('d',4)]:\n E=pd.read_parquet(f'{fam}_{s}.parquet')\n parts.append(pd.DataFrame({'uid':E.uid.to_numpy(),'ts':E.timestamp.to_numpy(),'ii':E.ii.to_numpy(),'kind':np.full(len(E),kd,np.int8)}))\n ev=pd.concat(parts,ignore_index=True)\n ev=ev[ev.ts=1)&(kk<=3)\n if msk.sum()==0: continue\n ii=d.ii.to_numpy()[msk][-K:]; ts=d.ts.to_numpy()[msk][-K:]; kk=kk[msk][-K:]\n L=len(ii)\n Hs[r,K-L:]=ii; HKs[r,K-L:]=kk\n dt=(T-ts)/86400.0\n HTs[r,K-L:]=np.clip(np.log1p(dt)*2.0,0,15).astype(np.int64)+1\n rows_u=[];rows_i=[];rows_r=[]\n t0=time.time()\n with torch.no_grad():\n for s in range(0,nu,256):\n e=slice(s,min(s+256,nu))\n U=m.user_vec(torch.from_numpy(Hs[e]),torch.from_numpy(HKs[e]),torch.from_numpy(HTs[e]))\n sc=(U@V.T).numpy()\n elig=np.unpackbits(packed[e],axis=1,count=NI,bitorder='little').astype(bool)\n sc[~elig]=-1e9\n top=np.argpartition(-sc,100,axis=1)[:,:100]\n ord_=np.take_along_axis(sc,top,1).argsort(1)[:,::-1]\n top=np.take_along_axis(top,ord_,1)\n for j in range(top.shape[0]):\n rows_u.append(np.full(100,uids[s+j])); rows_i.append(top[j]); rows_r.append(np.arange(1,101))\n items=np.load('items.npy')\n df=pd.DataFrame({'uid':np.concatenate(rows_u),'item_id':items[np.concatenate(rows_i)],'rank':np.concatenate(rows_r)})\n df.to_parquet(out,index=False)\n print('wrote',out,df.shape,time.time()-t0)\n\nif __name__=='__main__':\n main(sys.argv[1], sys.argv[2], sys.argv[3])\nEOF\necho ok; sleep 45; cat /tmp/claude-1000/-app/746159f9-6934-411c-832b-430fa9886b51/tasks/bfetsbz4b.output", "description": "Write inference script" } ### Tool output · 2026-08-29T01:01:49.234Z ok pairs 481369 examples 60847 ep0 loss 13.2226 129s saved 129.19863605499268 [exited with code 0] ### Harness context { "type": "queued_command", "prompt": "\nbfetsbz4b\ntoolu_01E6hcVR7FBhrxdBtYMmfhGs\n/tmp/claude-1000/-app/746159f9-6934-411c-832b-430fa9886b51/tasks/bfetsbz4b.output\ncompleted\nBackground command \"Benchmark one epoch\" completed (exit code 0)\n", "commandMode": "task-notification", "timestamp": "2026-08-29T01:00:57.778Z" } ### Harness context { "type": "total_tokens_reminder", "text": "14957321 tokens left" } ## Step 12 · agent · 2026-08-29T01:02:03.593Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:02:08.585Z { "command": "python - <<'EOF'\nimport re\ns=open('train.py').read()\ns=s.replace(\"\"\" neg=torch.randint(0,NI,(NEG,))\n cand=torch.cat([tgt,neg])\n v=m.item_vec(cand)\n logits=u@v.T\n tgt=torch.arange(len(idx))\n logits=logits.scatter(1, H[e], -1e4) if NEG>=NI else logits\"\"\",\n\"\"\" nb_=len(idx)\n neg=torch.randint(0,NI,(NEG,))\n cand=torch.cat([tgt,neg])\n v=m.item_vec(cand)\n logits=u@v.T\n off=(~torch.eye(nb_,dtype=torch.bool))\n logits[:, :nb_]=logits[:, :nb_].masked_fill(off, -1e4)\n tgt=torch.arange(nb_)\n if NEG>=NI:\n Hp=torch.where(HK[e]>0, H[e], torch.full_like(H[e], NI))\n logits=torch.cat([logits, torch.zeros(len(idx),1)],1).scatter(1,Hp,-1e4)[:, :NI]\"\"\")\nopen('train.py','w').write(s)\nEOF\nEPOCHS=7 NEG=8192 nohup python train.py dev > log_dev.txt 2>&1 &\necho started", "description": "Start dev training" } ### Tool output · 2026-08-29T01:02:08.668Z started ### Bash · 2026-08-29T01:02:11.954Z { "command": "python - <<'EOF'\n# popularity fallback submission (valid structure), replaced later by the model\nimport numpy as np, pandas as pd\nitems=np.load('items.npy')\nfor split,out in [('test','submission.parquet')]:\n z=np.load(f'/task/data/{split}_eligible_candidates.npz')\n uids=z['uids'].astype(np.int64); p=z['packed_eligible']\n U=[];I=[];R=[]\n for r,u in enumerate(uids):\n e=np.unpackbits(p[r],count=30000,bitorder='little').astype(bool)\n top=np.nonzero(e)[0][:100]\n U.append(np.full(100,u)); I.append(items[top]); R.append(np.arange(1,101))\n pd.DataFrame({'uid':np.concatenate(U),'item_id':np.concatenate(I),'rank':np.concatenate(R)}).to_parquet(out,index=False)\n print(out,'ok')\nEOF\nls -la /app/solution/", "description": "Write fallback submission" } ### Tool output · 2026-08-29T01:02:13.933Z submission.parquet ok total 258992 drwxr-xr-x 1 agent agent 4096 Aug 29 01:02 . drwxr-xr-x 1 agent agent 4096 Aug 29 00:50 .. -rw-r--r-- 1 agent agent 217770474 Aug 29 00:56 L_train.parquet -rw-r--r-- 1 agent agent 5400977 Aug 29 00:56 L_val.parquet -rw-r--r-- 1 agent agent 3869 Aug 29 00:57 build.py -rw-r--r-- 1 agent agent 1014430 Aug 29 00:56 d_train.parquet -rw-r--r-- 1 agent agent 38667 Aug 29 00:56 d_val.parquet -rw-r--r-- 1 agent agent 2992 Aug 29 01:01 infer.py -rw-r--r-- 1 agent agent 240128 Aug 29 00:55 items.npy -rw-r--r-- 1 agent agent 6452661 Aug 29 00:56 l_train.parquet -rw-r--r-- 1 agent agent 324512 Aug 29 00:56 l_val.parquet -rw-r--r-- 1 agent agent 28 Aug 29 01:02 log_dev.txt -rw-r--r-- 1 agent agent 2328 Aug 29 00:58 model.py -rw-r--r-- 1 agent agent 15586015 Aug 29 01:00 model_dev.pt -rw-r--r-- 1 agent agent 1509 Aug 29 00:55 prep.py -rw-r--r-- 1 agent agent 735840 Aug 29 01:02 submission.parquet -rw-r--r-- 1 agent agent 2102 Aug 29 01:02 train.py -rw-r--r-- 1 agent agent 17586342 Aug 29 00:58 train_dev.npz ### Harness context { "type": "total_tokens_reminder", "text": "14955006 tokens left" } ## Step 13 · agent · 2026-08-29T01:02:16.328Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:02:16.399Z { "command": "sleep 75; cat log_dev.txt", "description": "Check training progress" } ### Tool output · 2026-08-29T01:03:31.427Z pairs 481369 examples 60847 ep0 loss 9.1428 72s ### Harness context { "type": "total_tokens_reminder", "text": "14954133 tokens left" } ## Step 14 · agent · 2026-08-29T01:03:40.217Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:03:40.587Z { "command": "python build.py final 2>&1 | tail -3", "description": "Build final training data" } ### Tool output · 2026-08-29T01:04:06.105Z cut 20553200 58765 18.22291874885559 cut 20250800 62331 18.556025981903076 saved 62331 496725 23.13249659538269 ### Harness context { "type": "total_tokens_reminder", "text": "14953454 tokens left" } ## Step 15 · agent · 2026-08-29T01:04:07.760Z ### Bash · 2026-08-29T01:04:07.760Z { "command": "sleep 180; cat log_dev.txt", "description": "Wait for training" } ### Tool error · 2026-08-29T01:06:07.790Z Exit code 143 Command timed out after 2m 0s ### Harness context { "type": "total_tokens_reminder", "text": "14953287 tokens left" } ## Step 16 · agent · 2026-08-29T01:06:09.509Z ### Bash · 2026-08-29T01:06:09.509Z { "command": "sleep 110; cat log_dev.txt", "description": "Check training progress" } ### Tool output · 2026-08-29T01:07:59.572Z pairs 481369 examples 60847 ep0 loss 9.1428 72s ep1 loss 6.7475 164s ep2 loss 5.1105 236s ep3 loss 4.2862 308s ### Harness context { "type": "total_tokens_reminder", "text": "14953147 tokens left" } ## Step 17 · agent · 2026-08-29T01:08:02.877Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:08:04.210Z { "command": "while [ ! -f model_dev.pt ] || ! grep -q saved log_dev.txt; do sleep 10; done; cat log_dev.txt; EPOCHS=7 NEG=8192 nohup python train.py final > log_final.txt 2>&1 & echo launched_final", "description": "Wait then launch final training", "timeout": 400000 } ### Tool output · 2026-08-29T01:11:04.436Z pairs 481369 examples 60847 ep0 loss 9.1428 72s ep1 loss 6.7475 164s ep2 loss 5.1105 236s ep3 loss 4.2862 308s ep4 loss 3.6791 381s ep5 loss 3.1796 453s ep6 loss 2.8427 525s saved 524.9852547645569 launched_final ### Harness context { "type": "total_tokens_reminder", "text": "14952698 tokens left" } ## Step 18 · agent · 2026-08-29T01:11:06.787Z ### Bash · 2026-08-29T01:11:06.787Z { "command": "python infer.py dev validation validation.parquet && python /task/evaluate_public.py validation.parquet", "description": "Score validation ranking", "timeout": 420000 } ### Tool output · 2026-08-29T01:11:32.342Z wrote validation.parquet (845600, 3) 4.3042497634887695 { "model": "validation.parquet", "scores": { "quality_ndcg@10": 0.014165, "quality_users": 4263 } } ### Harness context { "type": "total_tokens_reminder", "text": "14952405 tokens left" } ## Step 19 · agent · 2026-08-29T01:11:36.597Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:11:39.065Z { "command": "python - <<'EOF' && python /task/evaluate_public.py pop_val.parquet\nimport numpy as np, pandas as pd\nitems=np.load('items.npy')\nz=np.load('/task/data/validation_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); p=z['packed_eligible']\nU=[];I=[];R=[]\nfor r,u in enumerate(uids):\n e=np.unpackbits(p[r],count=30000,bitorder='little').astype(bool)\n top=np.nonzero(e)[0][:100]\n U.append(np.full(100,u)); I.append(items[top]); R.append(np.arange(1,101))\npd.DataFrame({'uid':np.concatenate(U),'item_id':np.concatenate(I),'rank':np.concatenate(R)}).to_parquet('pop_val.parquet',index=False)\nEOF", "description": "Popularity baseline score" } ### Tool output · 2026-08-29T01:11:46.290Z { "model": "pop_val.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } ### Harness context { "type": "total_tokens_reminder", "text": "14951780 tokens left" } ## Step 20 · agent · 2026-08-29T01:12:05.490Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:12:22.374Z { "command": "cat > README.md <<'EOF'\n# Two-tower discovery ranker (CPU PyTorch)\n\n## Problem framing\nRank, for each target user, the 100 best *previously-unseen* catalog tracks from\nthat user's cutoff-correct eligible candidate set. Eligibility was verified to be\n\"catalog items the user has never listened to / liked / disliked before the\ncutoff\", so the task is strictly new-item discovery. Training mirrors this:\ntargets are **first-ever** organic interactions in the seven days after a cutoff.\n\n## Architecture (`model.py`)\nScore(u, i) = `user_vec(u) · item_vec(i)` — an exact dot product of two\nindependently computable vectors of dimension `d+1` (d=64), so item vectors can be\nbuilt offline and served through maximum-inner-product search, and only the user\nvector is computed per request.\n\n* **Item tower** (`ItemTower`): item-ID embedding -> residual MLP\n (`e + W2 gelu(W1 e)`) -> `d` dims, concatenated with a learned per-item bias\n scalar. No user input, so it is fully precomputable.\n* **User tower** (`UserTower`): **no user-ID lookup**. It takes the last 128\n positive history events (played_ratio >= 50% listens, plus likes) and embeds\n each item with a separate history embedding table. Events are pooled with\n learned attention-free weights `exp(kind_weight + recency_bucket_weight)`,\n normalized to sum to 1, then passed through LayerNorm and a residual MLP\n (`d -> 2d -> d`) and scaled by a learned temperature. A constant `1` is\n appended so the item-bias dimension contributes.\n\nBoth towers are `nn.Module`s trained jointly. Nothing global is factorized: the\nmodel never materializes or decomposes a user x item matrix, and the user tower\ngeneralizes to unseen users from their history alone.\n\n## Training objective (`build.py`, `train.py`)\nSampled-softmax (cross-entropy) next-window retrieval. Training examples are\ngenerated at 16 rolling cutoffs spaced 3.5 days apart across the last ~8 weeks of\nthe observed period. For each (user, cutoff): history = the last 128 positive\nevents strictly before the cutoff; positives = items the user *first* interacts\nwith organically in the following 7 days, at 80%+ completion (grade 1) or via an\norganic like (grade 3). Grade-3 positives are up-weighted 1.6x. Negatives are\n8192 uniformly sampled catalog items per step (other rows' positives are masked\nout of the denominator so the sampler stays uniform). AdamW + OneCycle, 7 epochs,\nbatch 1024, ~480k (history, positive) pairs.\n\n## Results\n* Public validation graded NDCG@10: **0.014165** (4263 scored users)\n* Popularity-ordered eligible-candidate baseline: 0.010710\n\n## Refit and submission\nThe selected configuration is refit on `train_* + val_*` (validation events become\nobserved history, cutoffs shifted forward one week), then applied at the test\ncutoff.\n\n```sh\npython prep.py # compact catalog-restricted event tables\npython build.py # validation-stage training examples\nEPOCHS=7 NEG=8192 python train.py dev # validation model\npython infer.py dev validation validation.parquet\npython /task/evaluate_public.py validation.parquet\n\npython build.py final # refit examples (train + val)\nEPOCHS=7 NEG=8192 python train.py final\npython infer.py final test submission.parquet # -> /app/solution/submission.parquet\n```\nEOF\nsleep 60; cat log_final.txt", "description": "Write README, check final training" } ### Tool output · 2026-08-29T01:13:22.445Z pairs 496725 examples 62331 ep0 loss 9.1245 99s ### Harness context { "type": "total_tokens_reminder", "text": "14949124 tokens left" } ## Step 21 · agent · 2026-08-29T01:13:25.590Z ### Thinking unavailable — no text recorded ### Bash · 2026-08-29T01:13:25.947Z { "command": "sleep 115; cat log_final.txt", "description": "Check final training" } ### Tool output · 2026-08-29T01:15:20.982Z pairs 496725 examples 62331 ep0 loss 9.1245 99s ep1 loss 6.7656 174s ep2 loss 5.1505 249s ### Harness context { "type": "total_tokens_reminder", "text": "14948855 tokens left" } ## Step 22 · agent · 2026-08-29T01:15:22.925Z ### Bash · 2026-08-29T01:15:22.925Z { "command": "while ! grep -q saved log_final.txt; do sleep 15; done; cat log_final.txt", "description": "Wait for final training", "timeout": 500000 } ### Tool output · 2026-08-29T01:20:23.203Z pairs 496725 examples 62331 ep0 loss 9.1245 99s ep1 loss 6.7656 174s ep2 loss 5.1505 249s ep3 loss 4.3293 324s ep4 loss 3.7280 399s ep5 loss 3.2250 474s ep6 loss 2.8903 549s saved 549.1137042045593 ### Harness context { "type": "total_tokens_reminder", "text": "14948640 tokens left" } ## Step 23 · agent · 2026-08-29T01:20:27.724Z ### Bash · 2026-08-29T01:20:27.724Z { "command": "python infer.py final test sub_tmp.parquet && python - <<'EOF'\nimport pandas as pd, numpy as np, sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, read_ranking\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(Path('sub_tmp.parquet')), t)\ncs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nbad=sum(1 for u,i in f[['uid','item_id']].itertuples(index=False,name=None) if not cs.is_eligible(u,i))\nprint('ineligible',bad,'rows',len(f))\nassert bad==0\nimport shutil; shutil.move('sub_tmp.parquet','submission.parquet')\nprint('submission written')\nEOF", "description": "Generate and validate test submission", "timeout": 400000 } ### Tool output · 2026-08-29T01:20:44.405Z wrote sub_tmp.parquet (845600, 3) 1.428253412246704 ineligible 0 rows 845600 submission written ### Harness context { "type": "total_tokens_reminder", "text": "14948065 tokens left" } ## Step 24 · agent · 2026-08-29T01:20:46.396Z ### Bash · 2026-08-29T01:20:46.396Z { "command": "rm -f L_train.parquet L_val.parquet pop_val.parquet; ls -la /app/solution/", "description": "Clean large intermediates and list" } ### Tool output · 2026-08-29T01:20:46.431Z total 76680 drwxr-xr-x 1 agent agent 4096 Aug 29 01:20 . drwxr-xr-x 1 agent agent 4096 Aug 29 00:50 .. -rw-r--r-- 1 agent agent 3281 Aug 29 01:12 README.md -rw-r--r-- 1 agent agent 3869 Aug 29 00:57 build.py -rw-r--r-- 1 agent agent 1014430 Aug 29 00:56 d_train.parquet -rw-r--r-- 1 agent agent 38667 Aug 29 00:56 d_val.parquet -rw-r--r-- 1 agent agent 2992 Aug 29 01:01 infer.py -rw-r--r-- 1 agent agent 240128 Aug 29 00:55 items.npy -rw-r--r-- 1 agent agent 6452661 Aug 29 00:56 l_train.parquet -rw-r--r-- 1 agent agent 324512 Aug 29 00:56 l_val.parquet -rw-r--r-- 1 agent agent 198 Aug 29 01:10 log_dev.txt -rw-r--r-- 1 agent agent 198 Aug 29 01:20 log_final.txt -rw-r--r-- 1 agent agent 2328 Aug 29 00:58 model.py -rw-r--r-- 1 agent agent 15586015 Aug 29 01:10 model_dev.pt -rw-r--r-- 1 agent agent 15586059 Aug 29 01:20 model_final.pt -rw-r--r-- 1 agent agent 1509 Aug 29 00:55 prep.py -rw-r--r-- 1 agent agent 1810207 Aug 29 01:20 submission.parquet -rw-r--r-- 1 agent agent 2102 Aug 29 01:02 train.py -rw-r--r-- 1 agent agent 17586342 Aug 29 00:58 train_dev.npz -rw-r--r-- 1 agent agent 18004976 Aug 29 01:04 train_final.npz -rw-r--r-- 1 agent agent 1809426 Aug 29 01:11 validation.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14947878 tokens left" } ## Step 25 · agent · 2026-08-29T01:20:57.907Z Done. `/app/solution/submission.parquet` is written, validated (845,600 rows, all ranks 1–100, zero ineligible items). **What was built** A two-tower PyTorch model where the final score is exactly `user_vec · item_vec` over 65 dims (64 learned + 1 constant/bias dim), so item vectors are fully precomputable for MIPS and only one user vector is computed per request. - **Item tower**: item embedding → residual MLP → 64-d, concatenated with a learned per-item bias scalar. - **User tower**: no user-ID lookup. Last 128 positive history events (≥50% listens + likes) → history embedding table → learned weighted pooling (`exp(kind_weight + recency_bucket_weight)`, normalized) → LayerNorm → residual MLP → learned temperature scale. - No global user-item matrix is ever formed or factorized; training is minibatch sampled-softmax. **Training setup**: 16 rolling cutoffs spaced 3.5 days apart. For each (user, cutoff): history is the last 128 positives before the cutoff; positives are items the user interacts with *for the first time* organically in the next 7 days (≥80% completion → grade 1, organic like → grade 3, up-weighted 1.6×). This mirrors the eligibility semantics, which I verified are "catalog items never interacted with before the cutoff" — the task is strictly new-item discovery. 8192 uniform negatives per step with other rows' positives masked out of the denominator. AdamW + OneCycle, 7 epochs, ~480k pairs. **Scores** - Public validation graded NDCG@10: **0.014165** (4263 scored users) - Popularity-ordered eligible-candidate baseline: 0.010710 (+32% relative) **Refit**: the same configuration was refit on `train_* + val_*` with cutoffs shifted forward one week, then applied at the test cutoff. One caveat worth flagging: the training loss was still dropping steeply at epoch 7 on both runs, so the model is likely undertrained rather than overfit. With more wall clock, the highest-value next step is simply a longer schedule (12–15 epochs), followed by a larger `d` and a sequence encoder over the history instead of weighted pooling. I did not have time to validate a longer schedule, so I shipped the configuration I could actually measure.