PaperAudit-Bench (Fast Mode) - o4-mini Errors: leaderboard

PaperAudit-Bench (Tu et al., 2026) tests whether a model finds errors injected into accepted papers. This board uses the 46 NeurIPS 2025 oral papers as corrupted by o4-mini (10 to 20 injected errors of eight types each), reviewed by each model in the Fast mode's single pass over the whole paper; text-only models see the papers without figures. Macro-F1 in percent over papers, the harmonic mean of error coverage and finding precision with a GPT-5.1 judge matching findings to injected errors, higher is better; the synthesis model is itself one of the detectors.

Metric: Macro-F1 (%; the per-paper harmonic mean of error coverage and finding precision, averaged over the 46 NeurIPS 2025 oral papers of the benchmark's NeurIPS branch as corrupted by o4-mini with 10-20 injected errors of eight types; Fast mode, one global review pass over the whole paper; findings matched to the injected errors by a GPT-5.1 judge; the three text-only detectors read the papers with figures removed). Source: arxiv.org. Saturation forecast: Around February 2027. 10 models tracked.

Top models

#ModelScoreOverall rank
1Grok 445.2#307
2Gemini 2.5 Pro44.6#266
3GPT-542.1#167
4Qwen 3 235B A22B 2507 Instruct40.5#619
5O4 Mini39.2#353
6GLM-4.6 (Non-reasoning)38.7#486 (GLM-4.6)
7Claude Sonnet 4.536.1#250
8DeepSeek V3.1 (Non-reasoning)23.2#497 (DeepSeek V3.1)
9Kimi K2 090514.4#558

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=paperaudit-bench-fast-mode-o4-mini-errors · How It Works · Data refreshed daily, snapshot 2026-11-13.