To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis

• URL: http://arxiv.org/abs/2512.05925v1
• Date: Fri, 05 Dec 2025 18:04:10 GMT
• Title: To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis
• Abstract summary: Our analysis focuses on objective mistakes-e.g., errors in formulas, derivations, calculations, figures, and tables-that have a clearly verifiable ground truth.<n>We find that published papers contain a non-negligible number of objective mistakes and that the average number of mistakes per paper has increased over time-from 3.8 in NeurIPS 2021 to 5.9 in NeurIPS 2025 (55.3% increase).<n>We show that the AI Checker can propose correct fixes for 75.8% of the identified mistakes.
• Score: 47.124493265404595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How many mistakes do published AI papers contain? Peer-reviewed publications form the foundation upon which new research and knowledge are built. Errors that persist in the literature can propagate unnoticed, creating confusion in follow-up studies and complicating reproducibility. The accelerating pace of research and the increasing demands on the peer-review system make such mistakes harder to detect and avoid. To address this, we developed a Paper Correctness Checker based on GPT-5 to systematically identify mistakes in papers previously published at top AI conferences and journals. Our analysis focuses on objective mistakes-e.g., errors in formulas, derivations, calculations, figures, and tables-that have a clearly verifiable ground truth. We intentionally exclude subjective considerations such as novelty, importance, or writing quality. We find that published papers contain a non-negligible number of objective mistakes and that the average number of mistakes per paper has increased over time-from 3.8 in NeurIPS 2021 to 5.9 in NeurIPS 2025 (55.3% increase); from 4.1 in ICLR 2018 to 5.2 in ICLR 2025; and from 5.0 in TMLR 2022/23 to 5.5 in TMLR 2025. Human experts reviewed 316 potential mistakes identified by the AI Checker and confirmed that 263 were actual mistakes, corresponding to a precision of 83.2%. While most identified issues are relatively minor, correcting them would reduce confusion in the literature and strengthen reproducibility. The AI Checker also surfaced potentially more substantive mistakes that could affect the interpretation of results. Moreover, we show that the AI Checker can propose correct fixes for 75.8% of the identified mistakes. Overall, this study highlights the potential of frontier LLMs to detect and correct objective mistakes in published papers, helping to establish a firmer foundation of knowledge.

Related papers

• NovGauge: A Fine-Grained Benchmark for Diagnosing LLMs' Capability in Paper Novelty Assessment [54.4265627247104]
  Large language models (LLMs) are increasingly used in peer review at major AI conferences.<n>Existing benchmarks assess novelty as a single holistic score.<n>We present NovGauge, a human-anchored benchmark for fine-grained novelty assessment diagnosis.
  arXiv  Detail & Related papers  (2026-09-10T08:34:56Z)
• Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects [0.0]
  Large language models (LLMs) are increasingly used to generate peer reviews.<n>This study evaluates two multimodal LLMs as reviewers across 165 submissions to the 2026 International Conference on Learning Representations.
  arXiv  Detail & Related papers  (2026-07-31T12:12:33Z)
• Benchmarking Agentic Review Systems [25.437512824197736]
  A new class of agentic review systems are emerging as a remedy to the pressure placed on peer review systems by AI-assisted research.<n>We evaluate two open-source systems (OpenAIReview and coarse), one proprietary system (Reviewer3), and a zero-shot baseline.<n>We show that while AI reviews still have room for improvement, they can already track human quality judgments well, catch important errors, and earn positive feedback from real users.
  arXiv  Detail & Related papers  (2026-06-18T03:30:06Z)
• RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension [65.81339691942757]
  RPC-Bench is a large-scale question-answering benchmark built from review-rebuttal exchanges of high-quality computer science papers.<n>We design a fine-grained taxonomy aligned with the scientific research flow to assess models' ability to understand and answer why, what, and how questions in scholarly contexts.
  arXiv  Detail & Related papers  (2026-01-14T11:37:00Z)
• FLAWS: A Benchmark for Error Identification and Localization in Scientific Papers [10.04850395402571]
  The identification and localization of errors is a core task in peer review.<n>Recent advances in Large Language Models (LLMs) have sparked interest in their potential to support such evaluation tasks.<n>Despite the growing use of LLMs in review systems, their capabilities to pinpoint errors remain underexplored.
  arXiv  Detail & Related papers  (2025-11-26T19:19:44Z)
• BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers? [21.78901120638025]
  We investigate whether fabrication-oriented paper generation agents can deceive multi-model LLM review systems.<n>Our generator employs presentation-manipulation strategies requiring no real experiments.<n>Despite provably sound aggregation mathematics, integrity checking systematically fails.
  arXiv  Detail & Related papers  (2025-10-20T18:37:11Z)
• When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research [19.97666809905332]
  Large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists.<n>Recent advances in large language models (LLMs) have fueled the vision of automated scientific discovery, often called AI Co-Scientists.
  arXiv  Detail & Related papers  (2025-05-17T05:45:16Z)
• Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing [59.405145971637204]
  We propose a novel preference learning framework called eRror-Injected Self-Editing (RISE)<n>RISE injects predefined subtle errors into pivotal tokens in reasoning or steps to construct hard pairs for error mitigation.<n>Experiments validate the effectiveness of RISE, with preference learning on Qwen2-7B-Instruct yielding notable improvements of 3.0% on GSM8K and 7.9% on MATH with only 4.5K training samples.
  arXiv  Detail & Related papers  (2024-10-09T07:43:38Z)
• Missci: Reconstructing Fallacies in Misrepresented Science [84.32990746227385]
  Health-related misinformation on social networks can lead to poor decision-making and real-world dangers. Missci is a novel argumentation theoretical model for fallacious reasoning. We present Missci as a dataset to test the critical reasoning abilities of large language models.
  arXiv  Detail & Related papers  (2024-06-05T12:11:10Z)
• Evaluating Mathematical Reasoning of Large Language Models: A Focus on Error Identification and Correction [35.01097297297534]
  Existing evaluations of Large Language Models (LLMs) focus on problem-solving from the examinee perspective. We define four evaluation tasks for error identification and correction along with a new dataset with annotated error types and steps. Our principal findings indicate that GPT-4 outperforms all models, while open-source model LLaMA-2-7B demonstrates comparable abilities to closed-source models GPT-3.5 and Gemini Pro.
  arXiv  Detail & Related papers  (2024-06-02T14:16:24Z)
• Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews [51.453135368388686]
  We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM) Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level.
  arXiv  Detail & Related papers  (2024-03-11T21:51:39Z)
• The Earth is Flat? Unveiling Factual Errors in Large Language Models [89.94270049334479]
  Large Language Models (LLMs) like ChatGPT are in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education. We introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs.
  arXiv  Detail & Related papers  (2024-01-01T14:02:27Z)
• AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5? [0.0]
  This paper investigates the potential of AI in providing personalized code correction and generating feedback. GPT-3.5 exhibited weaknesses in its evaluation, including localization of errors that were not the actual errors, or even hallucinated errors.
  arXiv  Detail & Related papers  (2023-10-24T10:35:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.