mlsanityData to 7 Oct 2026

MOMENTS: A Comprehensive Multimodal Benchmark for Theory of Mind

arXiv 2507.04415 ↗First posted 6 Jul 2025cs.CLcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 9 of its 41 references that are in the map. First posted on arXiv 6 Jul 2025.

Builds on

9 of 41 references.

References · 9

  1. 2024VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMscited by 749
  2. 2024Long Context Transfer from Language to Visioncited by 502
  3. 2023WhisperX: Time-Accurate Speech Transcription of Long-Form Audiocited by 320
  4. 2024MMToM-QA: Multimodal Theory of Mind Question Answeringcited by 84
  5. 2025Social Genome: Grounded Social Reasoning Abilities of Multimodal Modelscited by 24
  6. 2025Theory of Mind in Large Language Models: Assessment and Enhancementcited by 24
  7. 2021Deep Interpretable Models of Theory of Mindcited by 13
  8. 2024Long Story Short: Story-level Video Understanding from 20K Short Filmscited by 11
  9. 2023DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understandingcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 9.

11 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2024Through the Theory of Mind's Eye: Reading Minds with Multimodal Video Large Language Models
  2. 2026StoryTR: Narrative-Centric Video Temporal Retrieval with Theory of Mind Reasoning
  3. 2026Video-Only ToM: Enhancing Theory of Mind in Multimodal Large Language Models
  4. 2025SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions
  5. 2025From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
  6. 2025HumanSense: From Multimodal Perception to Empathetic Context-Aware Responses through Reasoning MLLMs
  7. 2026Belief Without Behavior: Measuring the Translation of Theory of Mind into Coordinated Social Action in Vision-Language Models
  8. 2024Multi-ToM: Evaluating Multilingual Theory of Mind Capabilities in Large Language Models