arXiv Computation and Language October 16, 2026 By Caixin Kang, Yifei Huang, Liangyang Ouyang, Mingfang Zhang, Ruicong Liu, Yoichi Sato

Can MLLMs Read the Room? A Multimodal Benchmark for Assessing Deception in Multi-Party Social Interactions

Read the original on arXiv Computation and Language →

The paper introduces MIDA, a multimodal benchmark that tests whether Multimodal Large Language Models can detect deception in multi‑party social interactions. It evaluates 12 state‑of‑the‑art MLLMs, finding that even powerful models like GPT‑4o perform poorly at distinguishing truthful from deceptive statements. The authors propose a Social Chain‑of‑Thought reasoning pipeline and a Dynamic Social Epistemic Memory module, showing that structured epistemic memory improves performance but that multimodal social‑cue reasoning remains a significant challenge.

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