Humans possess an innate ability to understand fine-grained interpersonal relationships, which is central to everyday social interactions. Although such reasoning is inherently multimodal, it remains largely unexplored by existing multimodal large language models (MLLMs).
arXiv:2609.22778v1 Announce Type: new
Abstract: Multimodal large language models (MLLMs) are increasingly used as evaluators, yet their reliability in professional assessment tasks that require exper...
By Yuhan Lu, Yi Yao, Hua Shen, Katie Aafjes-van Doorn, Zhaonan Wang
arXiv:2601. 14569v2 Announce Type: replace-cross Abstract: Social understanding abilities are crucial for multimodal large language models (MLLMs) to interpret human social interactions.
By Leena Mathur, Bhaavanaa Thumu, Youssouf Kebe, Louis-Philippe Morency
CoMMET is a new multimodal benchmark designed to evaluate Theory of Mind (ToM) in Multimodal Large Language Models (MLLMs). It expands beyond existing text-only, belief-focused tests by covering a wider range of mental states, incorporating moral evaluation, and enabling multi-turn, open-ended interactions. The dataset is grounded in psychological theory and provides a comprehensive assessment across different model families and sizes, revealing strengths, limitations, and future improvement directions.
By Ruirui Chen, Weifeng Jiang, Chengwei Qin, Kaiwen Wei, Yanzhen Yue, Cheston Tan
arXiv:2610.15129v1 Announce Type: new
Abstract: Human social perception is incremental: we infer intent before an utterance is complete, revise our interpretation as new cues arrive, and attend to sp...
By Zonghuan Li, Hayley Hung, Chirag Raman
arXiv:2506.23101v2 Announce Type: replace-cross
Abstract: Ensuring the trustworthiness of Multimodal Large Language Models (MLLMs) requires rigorous evaluation of gender bias, particularly in sociall...
By Yue Xu, Wenjie Wang