PIVOTSBench is a benchmark designed to assess multimodal large language models’ ability to reason about fine‑grained interpersonal relationships. It is constructed from Social‑IQ 2.0 and YouTube data and evaluates models on predicting bidirectional relationship dimensions grounded in psychology research. The benchmark also includes auxiliary tasks that test models’ capacity to identify and use critical visual cues, and it examines the impact of visual modalities, social role information, and different prediction settings on model performance.
By Shuxiang Zhang, Yiting Yin, Wenxuan Song, Yuhang Wu, Miao Liu
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:2606. 11930v1 Announce Type: cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging multimodal learning problem because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
By Kuo-En Hung, Hung-Yue Suen, Shih-Ching Yeh, Hsiang-Wen Wang
arXiv:2606. 11930v2 Announce Type: replace-cross Abstract: Predicting psychological traits from asynchronous video interviews (AVIs) is a challenging problem in AI-assisted interview assessment because labeled datasets are limited while each response contains high-dimensional visual, acoustic, and verbal signals.
By Kuo-En Hung, Hung-Yue Suen, Shih-Ching Yeh, Hsiang-Wen Wang
arXiv:2608. 07512v1 Announce Type: cross Abstract: Asynchronous Video Interviews (AVIs) have become increasingly popular for personality assessment.
By Dongsheng Hu, Tianyi Zhang, Chuang Liu, Yuan Zong Yong Li, Wenming Zheng, Xiu-xiu Zhan
HUG‑VIS is a unified multimodal benchmark for human‑centered visual intelligence, comprising 8,400 half‑body videos of 30 professional actors performing 280 emotion‑action prompts in Mandarin. The dataset provides synchronized video, audio, text, and alpha mattes for four tasks—human emotion recognition, video generation, voice cloning, and video matting—allowing evaluation of both open‑ and closed‑source models under a zero‑shot protocol. Results reveal that linguistic cues dominate emotion recognition, visual affect is weakest, and that automatic metrics and human judgments diverge in generation and cloning tasks, while motion‑related boundary fidelity remains a key challenge for matting.
By Fei Ma, Zebang Cheng, Minghui Li, Hongbo Xu, Yuyong Tan, Yihua Shao, Hanling Wang, Zhou Liu, Yuqing Gao, Dong Wang, Long Ma, Laizhong Cui, Nicu Sebe, Qi Tian
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:2606. 26348v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can process diverse inputs, e.
By Po-han Li, Shenghui Chen, Sandeep Chinchali, Ufuk Topcu
arXiv:2507. 19634v4 Announce Type: replace-cross Abstract: Recent advances in large language models have laid the foundation for multimodal LLMs (MLLMs), which unify text, speech, and vision within a single framework.
By Sara Papi, Maike Z\"ufle, Marco Gaido, Beatrice Savoldi, Danni Liu, Ioannis Douros, Luisa Bentivogli, Jan Niehues
arXiv:2608.29286v1 Announce Type: new
Abstract: As Multimodal Large Language Models (MLLMs) evolve into sophisticated interactive assistants, their reliability depends not only on following instructi...
By Jinzhe Li, Gengxu Li, Jinnan Li, Yuan Wu, Yi Chang
arXiv:2606. 07541v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong performance on objective tasks such as video understanding and reasoning.
By Prabal Shrestha, Bohan Jiang, Haoning Xue, Huan Liu, Xinyi Zhou
arXiv:2606. 28556v1 Announce Type: new Abstract: Recent advances in large language models and vision-language models have enabled reasoning over multimodal data, offering opportunities for clinical applications such as decision support and triaging.
By Maria Xenochristou, Ashutosh Joshi, Korosh Vatanparvar, Mohammad Abuzar Hashemi, Prasad Kasu, Deepak Bansal, Anchal Nema, Nivedita Wadhwa, Prashams S Jain, Rebecca Abraham, Will Kimbrough, Dilek Hakkani-Tur, Wilko Schulz-Mahlendorf