arXiv Computation and Language
The Machine's Internal Clock: Do LLMs Share Human Temporal Illusions?
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Reality Monitoring in Large Language Models: Self-Knowledge That Transforms with Conversation Memory
arXiv:2607. 23927v1 Announce Type: new Abstract: A conversational AI that cannot tell its own output from what a user said will treat its own mistakes as user-provided facts.
arXiv AI
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Query Timing Produces Opposite Positional Biases Between LLMs and Humans
arXiv:2608. 12387v1 Announce Type: cross Abstract: Positional biases such as recency and primacy effects have been documented in large language models (LLMs), yet the underlying mechanism by which these models make their evaluations remains poorly understood.
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Hindsight Bias in Clinical Temporal Reasoning: How Future Data Exposure Affects Large Language Model Judgment
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Illusory Pattern Perception Drives Spurious Inference in Large Language Models
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arXiv AI
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Beyond What Meets the Eye: Unveiling Situational Illusions for Multimodal Large Language Models
arXiv:2608.22232v1 Announce Type: new Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (ML...
arXiv AI
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Can LVLMs Uncover the Truth Behind Visual Illusions? An Analysis of Perceptual and Reasoning Capabilities
The paper introduces Illusion-Reasoning, a benchmark that uses real-world visual illusion images paired with annotated question-answer pairs to evaluate Large Vision Language Models (LVLMs). It argues that current evaluations focus too narrowly on perception or specific domains, and that a joint assessment of perception and reasoning in open-world settings is lacking. Using this benchmark, the authors demonstrate that many LVLMs’ reasoning abilities are less advanced than previously claimed, offering new insights and directions for optimization.