NYSgpt/44b-eval

44b Eval

A frozen 9-way reranking benchmark over machine-learning papers, queried by their peer reviews.

44b Collection | 44b-reranker | 44b-reranker-mini

The benchmark behind every number on the 44b reranker cards: which review is the query, which paper is the gold, which eight papers are the hard negatives, and the scored result of every arm — so anyone can check a published number or score their own reranker on the same 3,157 queries.

• ✅ Paper-disjoint from training. None of the 1,534 test papers appears in training as a positive; 23 (1.5%) appear as some other query's negative, which biases against the fine-tuned models rather than for them.
• ✅ Real expert queries. Each query is a peer reviewer's own summary of a paper, written by a domain expert on OpenReview.
• ✅ Hard by construction. 8 negatives per query mined from embedding-space nearest neighbours and lexical near-matches, screened for duplicates. Chance P@1 = 0.111.
• ✅ Keys-only. OpenReview note IDs and arXiv / OpenAlex / Semantic Scholar paper IDs, each with its public URL — no review text, no abstracts.

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Contents

file	rows	what
test.jsonl	3,157	{note_id, note_url, kind, positive{scheme,id,url}, negatives[8]{scheme,id,url}} — one line per query
results.jsonl	7	the scorer's output per arm: P@1, P@2, P@3, MRR, mean rank, ms/query, truncation settings
manifest.json	1	split rule and seed, counts, disjointness proof, negative-mining recipe, ID-scheme census

Rebuilding the text. The query is the summary field of an official_review (or the metareview of a meta_review) at note_url. Each candidate is the title + abstract of the paper at its url: arxiv IDs resolve at arxiv.org, openalex IDs at openalex.org, s2 IDs at semanticscholar.org. 23 negatives (0.1%) are curated web documents with no third-party identifier and are marked resolvable: false.

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Performance

3,157 queries over 1,534 papers, 9 candidates each (1 positive + 8 hard negatives). Model arms on one A10G; retrieval arms are first-stage rankings of the same 9-candidate sets.

Rank	Arm	P@1	P@3	MRR	mean rank	ms/query
1	44b-reranker (149M, fine-tuned)	0.929	0.982	0.956	1.148	41.1
2	44b-reranker-mini (22.7M, fine-tuned)	0.865	0.954	0.914	1.322	7.4
3	BM25 (keyword)	0.856	0.940	0.905	1.407	—
4	gte-reranker-modernbert-base (off the shelf)	0.833	0.926	0.889	1.464	53.1
5	Hybrid RRF (k = 60)	0.728	0.793	0.788	2.371	—
6	Dense (text-embedding-3-small)	0.681	0.749	0.745	2.859	—
7	ms-marco-MiniLM-L-6-v2 (off the shelf)	0.643	0.817	0.750	2.210	9.4

These are reranking numbers — picking the right paper out of nine confusable ones — not open-corpus retrieval.

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Details

Property
Corpus	44B — arXiv, OpenAlex and Semantic Scholar records unified with OpenReview reviews; ICLR 2024–2025, NeurIPS 2024
Query	an official review's summary, or a meta-review's metareview; ≥ 200 characters
Split	by paper — sha256("44b-training-pairs-2026-07-31:" + paper) → [0,1), train < 0.8 ≤ val < 0.9 ≤ test
Disjointness	`
Negatives	8 per query: semantic (HNSW over text-embedding-3-small, ef_search 160) and lexical (Postgres FTS); false-negative screen on Semantic Scholar ID and near-identical title
Truncation at scoring	query 600 characters, document 900 characters
Frozen	2026-07-31

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How to use

import json

rows = [json.loads(l) for l in open("test.jsonl")]
for r in rows:
    query_url = r["note_url"]                       # fetch the review's summary here
    candidates = [r["positive"], *r["negatives"]]   # fetch title + abstract at each ["url"]
    # score all 9 with your reranker; the positive is at index 0 before you shuffle

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Licensing

Review text belongs to its authors on OpenReview; abstracts come from Semantic Scholar, OpenAlex and arXiv under their own terms. None of that text is redistributed here. This release ships identifiers, the benchmark construction, and our own measurements — released CC-BY-4.0. A third party resolves the IDs against the same public sources we did, which is the standard MS MARCO / BEIR shape.

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📬 Contact

Questions, results, or a model to add to the table? Open a discussion in the Community tab.

Citation

@misc{44beval2026,
  title  = {44b Eval: a peer-review-queried hard-negative reranking benchmark over ML papers},
  author = {NYSgpt},
  year   = {2026},
  url    = {https://huggingface.co/datasets/NYSgpt/44b-eval}
}