Beyond What Meets the Eye: Unveiling Situational Illusions for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2608.22232v2
• Date: Tue, 25 Aug 2026 05:51:53 GMT
• Title: Beyond What Meets the Eye: Unveiling Situational Illusions for Multimodal Large Language Models
• Abstract summary: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLLMs) in practical applications.<n>We term this phenomenon situational illusions and investigate how MLLMs perform under such illusions.<n>We build on the core idea of systematically inspecting and reasoning over visual evidence for contextual understanding, developing prompting for closed-source models and supervised fine-tuning for open-source models.
• Score: 9.436265912103435
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world situation appearances can deviate from their underlying physical states, challenging the reliability of multimodal large language models (MLLMs) in practical applications. In this paper, we term this phenomenon situational illusions and investigate: (1) how MLLMs perform under such illusions, and (2) how to mitigate the limitations. We first develop a comprehensive where-what-how taxonomy that characterizes where situational illusions occur, what targets they take, and how they arise. Building on this taxonomy, we introduce MSIBench, a benchmark designed to assess the discrimination, understanding, and reasoning capabilities of MLLMs under situational illusions. Evaluations of 27 model configurations reveal that current MLLMs are highly vulnerable to these illusions and exhibit 6 typical failure modes related to visual observation, grounding, and reasoning. To mitigate the limitations, we build on the core idea of systematically inspecting and reasoning over visual evidence for contextual understanding, developing prompting for closed-source models and supervised fine-tuning for open-source models, respectively. These two simple yet effective methods improve model performances by 20% at most, suggesting a practical path toward more reliable multimodal perception and reasoning in complex real-world environments.

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