arXiv：cs.AI· Peihua Mai, Zhuoyan Shao, Xinbao Qiao, Meng Zhang, Xinyue Zhou, Yan Pang·· 6 小时前AI 评分55
研究：LLM 的错觉模式感知强于人类并导致虚假推理
Illusory Pattern Perception Drives Spurious Inference in Large Language Models
AI 导读
一篇被 NeurIPS 2026 接收的 arXiv 论文（arXiv:2610.07791）首次系统研究 LLM 的错觉模式感知，将经典心理学范式应用于三个任务并与人类行为直接对比。
正文
Abstract:Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tendency, often described as "connecting the dots" where none exist, can result in systematic reasoning errors. This paper investigates whether Large Language Models (LLMs) exhibit such perceptual tendencies, which can lead to systematic errors in downstream applications. To our knowledge, this work presents the first systematic study of illusory pattern perception in LLMs, adapting classic psychological paradigms to three tasks with direct empirical comparison to human behaviors. We find that LLMs frequently exhibit stronger illusory pattern perception than humans. In particular, models tend to over-associate frequent positive attributes with majority groups or large organizations, and show increased tendencies to construct causal narratives from ambiguous events. To uncover the mechanism behind these behaviors, we develop a feature interpretability framework based on Sparse Autoencoders (SAEs) to analyze internal representations. Our results reveal that holistic frequency perception and analytic cognitive orientation are linked to the emergence of illusory perceptions. These findings highlight a previously underexplored cognitive-like illusion that may affect the reliability of LLM reasoning. Code available at this https URL.
| Comments: | accepted by NeurIPS 2026 |
| Subjects: | Artificial Intelligence (cs.AI) |
| ACM classes: | I.5.5 |
| Cite as: | arXiv:2610.07791 [cs.AI] |
| (or arXiv:2610.07791v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07791 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Peihua Mai [view email]
[v1]
Tue, 6 Oct 2026 05:37:12 UTC (5,750 KB)
来源：arXiv：cs.AI · arxiv.org