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
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
Personalized language-model assistants are often evaluated through a memory lens: can a model recall preferences users have explicitly stated in dialogue? More comprehensive personalization demands a harder capability -- inferring what users care about from the multimodal traces they naturally leave behind.
Theory of Mind (ToM), the ability to infer other's beliefs, intentions, and states of knowledge, is central to social interaction, yet remains challenging for current Multimodal Large Language Models (MLLMs), especially in multi-party meetings where cues are distributed across speech and behavior. Existing multimodal ToM benchmarks mainly focus on video-grounded question answering over overt, externally verifiable signals, and provide limited coverage of latent social states and group dynamics.
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
Training Multimodal Large Language Models for audio-visual social understanding is a crucial step toward embodied social intelligence. Chain-of-thought (CoT) reasoning has become the dominant approach, with HumanOmniV2 and its IntentBench benchmark as a prominent reference point.