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EMNLP Findings2025

MoMentS: A Comprehensive Multimodal Benchmark for Theory of Mind

Emilio Villa-Cueva, S. M. Masrur Ahmed, Rendi Chevi, Jan Christian Blaise Cruz, Kareem Elzeky, Fermin Cristobal, Alham Fikri Aji, Skyler Wang, Rada Mihalcea, Thamar Solorio

Abstract

Understanding Theory of Mind is essential for building socially intelligent multimodal agents capable of perceiving and interpreting human behavior. We introduce MOMENTS (Multimodal Mental States), a comprehensive benchmark designed to assess the ToM capabilities of multimodal large language models (LLMs) through realistic, narrative-rich scenarios presented in short films. MOMENTS includes over 2,300 multiple-choice questions spanning seven distinct ToM categories. The benchmark features long video context windows and realistic social interactions that provide deeper insight into characters' mental states. We evaluate several MLLMs and find that although vision generally improves performance, models still struggle to integrate it effectively. For audio, models that process dialogues as audio do not consistently outperform transcript-based inputs. Our findings highlight the need to improve multimodal integration and point to open challenges that must be addressed to advance AI's social understanding.

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the full paper · 2 top-tier papers that cite it · related work

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Cited by top-tier papers2

  1. MindPower: Enabling Theory-of-Mind Reasoning in VLM-based Embodied AgentsRuoxuan Zhang, Qiyun Zheng, Zhiyu Zhou, Ziqi Liao et al.CVPR 2026 · 6 citations
  2. MovieGraph-ToM: Evaluating Long-Range Theory of Mind in Large Language Models via Implicit Social-Causal GraphsTingjiang Wei, Qin Ni, Rong Gao, Yingying Wang et al.AAAI 2026

Builds on10

  1. SOTOPIA: Interactive Evaluation for Social Intelligence in Language AgentsXuhui Zhou, Hao Zhu, Leena Mathur, Ruohong Zhang et al.ICLR 2024 · 288 citations
  2. A Case Study of the Shortcut Effects in Visual Commonsense ReasoningKeren Ye, Adriana KovashkaAAAI 2021 · 47 citations
  3. Towards A Holistic Landscape of Situated Theory of Mind in Large Language ModelsZiqiao Ma, Jacob Sansom, Run Peng, Joyce ChaiEMNLP Findings 2023 · 13 citations
  4. Hi-ToM: A Benchmark for Evaluating Higher-Order Theory of Mind Reasoning in Large Language ModelsYufan Wu, Yinghui He, Yilin Jia, Rada Mihalcea et al.EMNLP Findings 2023 · 8 citations
  5. MMToM-QA: Multimodal Theory of Mind Question AnsweringChuanyang Jin, Yutong Wu, Jing Cao, Jiannan Xiang et al.ACL 2024 · 8 citations

Related papers

  1. Theory of Mind in Large Language Models: Assessment and EnhancementRuirui Chen, Weifeng Jiang, Chengwei Qin, Cheston TanACL 2025
  2. HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized BenchmarksTing Zhou, Daoyuan Chen, Qirui Jiao, Bolin Ding et al.CVPR 2026
  3. ToMBench: Benchmarking Theory of Mind in Large Language ModelsZhuang Chen, Jincenzi Wu, Jinfeng Zhou, Bosi Wen et al.ACL 2024 · 6 citations
  4. Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human StatesYang Xiao, Jiashuo Wang, Qiancheng Xu, Changhe Song et al.ACL 2025 · 12 citations
  5. SIV-Bench: A Video Benchmark for Social Interaction Understanding and ReasoningFanqi Kong, Weiqin Zu, Xinyu Chen, Yaodong Yang et al.ACL Findings 2026
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