# PaperAudit-Bench: Benchmarking Error Detection in Research Papers for Critical Automated Peer Review

arXiv: https://arxiv.org/abs/2601.19916 · HTML: https://mlsanity.com/paper/2601.19916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 33 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery](https://mlsanity.com/paper/2408.06292/) (2024)
- [Agent Laboratory: Using LLM Agents as Research Assistants](https://mlsanity.com/paper/2501.04227/) (2025)
- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025)
- [CycleResearcher: Improving Automated Research via Automated Review](https://mlsanity.com/paper/2411.00816/) (2024)
- [DeepReview: Improving LLM-based Paper Review with Human-like Deep Thinking Process](https://mlsanity.com/paper/2503.08569/) (2025)
- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents](https://mlsanity.com/paper/2410.13185/) (2024)
- [SRFT: A Single-Stage Method with Supervised and Reinforcement Fine-Tuning for Reasoning](https://mlsanity.com/paper/2506.19767/) (2025)
- [MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research](https://mlsanity.com/paper/2505.19955/) (2025)
- [Learning When to Think: Shaping Adaptive Reasoning in R1-Style Models via Multi-Stage RL](https://mlsanity.com/paper/2505.10832/) (2025)
- [Can LLM feedback enhance review quality? A randomized study of 20K reviews at ICLR 2025](https://mlsanity.com/paper/2504.09737/) (2025)
- [AI Idea Bench 2025: AI Research Idea Generation Benchmark](https://mlsanity.com/paper/2504.14191/) (2025)
- [Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context](https://mlsanity.com/paper/2412.17596/) (2024)
- [MLRC-Bench: Can Language Agents Solve Machine Learning Research Challenges?](https://mlsanity.com/paper/2504.09702/) (2025)
- [When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research](https://mlsanity.com/paper/2505.11855/) (2025)
- [IRIS: Interactive Research Ideation System for Accelerating Scientific Discovery](https://mlsanity.com/paper/2504.16728/) (2025)
- [When Your Reviewer is an LLM: Biases, Divergence, and Prompt Injection Risks in Peer Review](https://mlsanity.com/paper/2509.09912/) (2025)
- [FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers](https://mlsanity.com/paper/2511.21843/) (2025)
- [Reviewing Scientific Papers for Critical Problems With Reasoning LLMs: Baseline Approaches and Automatic Evaluation](https://mlsanity.com/paper/2505.23824/) (2025)
- [Unveiling the Merits and Defects of LLMs in Automatic Review Generation for Scientific Papers](https://mlsanity.com/paper/2509.19326/) (2025)
- [Automatic Reviewers Fail to Detect Faulty Reasoning in Research Papers: A New Counterfactual Evaluation Framework](https://mlsanity.com/paper/2508.21422/) (2025)
- [Perception-Consistency Multimodal Large Language Models Reasoning via Caption-Regularized Policy Optimization](https://mlsanity.com/paper/2509.21854/) (2025)
- [PaperDebugger: A Plugin-Based Multi-Agent System for In-Editor Academic Writing, Review, and Editing](https://mlsanity.com/paper/2512.02589/) (2025)
- [Are the confidence scores of reviewers consistent with the review content? Evidence from top conference proceedings in AI](https://mlsanity.com/paper/2505.15031/) (2025)
- [ReviewGuard: Enhancing Deficient Peer Review Detection via LLM-Driven Data Augmentation](https://mlsanity.com/paper/2510.16549/) (2025)

## Built on by (most cited first, 3 of 3)

- [Dynamic Dual-Granularity Skill Bank for Agentic RL](https://mlsanity.com/paper/2603.28716/) (2026)
- [STRIDE: A Self-Reflective Agent Framework for Reliable Automatic Equation Discovery](https://mlsanity.com/paper/2605.17790/) (2026)
- [Not Just Reason, Not Just Scan: Reinforcement Learning for Proactive Scientific Error Verification over Academic Paper](https://mlsanity.com/paper/2608.26596/) (2026)

## Abstract (arXiv)

Large language models can generate fluent peer reviews, yet their assessments often lack sufficient critical rigor when substantive issues are subtle and distributed across a paper. In this paper, we introduce PaperAudit-Bench, which consists of two components: (1) PaperAudit-Dataset, an error dataset covering both errors identifiable within individual sections and those requiring cross-section reasoning, designed for controlled evaluation under long-context settings; and (2) PaperAudit-Review, an automated review framework that integrates structured error detection with evidence-aware review generation to support critical assessment. Experiments on PaperAudit-Bench reveal large variability in error detectability across models and detection depths, highlighting the difficulty of identifying such errors under long-context settings. Relative to representative automated reviewing baselines, incorporating explicit error detection into the review workflow produces systematically stricter and more discriminative evaluations, demonstrating its suitability for peer review. Finally, we show that the dataset supports training lightweight LLM detectors via SFT and RL, enabling effective error detection at reduced computational cost.