GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
A new benchmark, GroupToM-Bench, evaluates multimodal large language models on group-level theory of mind. While models excel at individual mental state reasoning, they fail at understanding collective behavior that emerges nonlinearly from social tensions and structural constraints. The benchmark covers a causal chain from micro-level beliefs, desires, and intentions to group tension and macro-level outcomes, using a seven-level cognitive audit. Experiments reveal a significant gap between current models and human baselines.
[2606.04184] GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
[Submitted on 2 Jun 2026]
Title:GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
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Abstract:True general intelligence requires not only a model of the physical world but also a social world model: the capacity to infer how individual mental states interact and crystallize into group-level outcomes. Despite notable progress in individual-level Theory of Mind (ToM) reasoning, existing multimodal large language models fail at this broader task. Collective behavior emerges non-linearly from social tensions, conformity dynamics, and structural constraints, meaning it cannot be recovered by merely summing individual intentions. We present GroupToM-Bench, the first multimodal benchmark for group-level ToM, built around a causal chain spanning micro-level BDI states (belief, desire, intention), meso-level group tension and structural constraints, and macro-level outcome prediction and mechanistic attribution. To probe this full arc, we develop a seven-level cognitive audit framework. Experiments reveal a gap between current models and human baselines, highlighting a failure to process social structures and non-linear collective dynamics.
Comments: Accepted by ACL 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.04184 [cs.CV]
(or arXiv:2606.04184v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.04184
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Weidong Tang [view email] [v1] Tue, 2 Jun 2026 20:06:32 UTC (10,581 KB)
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