OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
Standard evaluations of Theory of Mind (ToM) in LLMs rely on end-point question answering, which does not reveal whether models actually construct mental-state representations. OmniToM addresses this by requiring explicit modeling of belief structures for all actors in a narrative. The benchmark comprises two stages—Belief Extraction and Belief Labeling—using a seven-dimensional schema. Built from 895 stories and 22,343 labeled belief propositions via a human-calibrated LLM-assisted pipeline, zero-shot evaluations show that current LLMs struggle with belief-tracking bottlenecks.
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Key points
- OmniToM evaluates ToM by requiring explicit belief structure modeling, not just final answers.
- Two-stage evaluation: Belief Extraction and Belief Labeling with a seven-dimensional schema.
- Built from 895 stories and 22,343 labeled belief propositions.
- Zero-shot results reveal a belief-tracking bottleneck in current LLMs.
Why it matters
This matters because omniToM evaluates ToM by requiring explicit belief structure modeling, not just final answers.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26322] OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
[Submitted on 25 May 2026]
Title:OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling
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Abstract:Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-point question answering, where performance is judged solely by the final answer to a social reasoning query. This paradigm obscures whether the model actually constructs the underlying mental-state representations required for robust reasoning, particularly in scenarios involving divergent, evolving, or mistaken beliefs. In order to address this research gap, we introduce OmniToM, a benchmark that directly evaluates these representations by requiring explicit modeling of belief structures for all relevant actors within a narrative. These structures are composed of belief propositions: minimal statements of what an actor takes to be true about the world or another actor's mental state, allowing knowledge, intentions, emotions, and false beliefs to be analyzed in a common format. Models are evaluated in two stages: Stage 1: Belief Extraction, which extracts from the story the beliefs relevant to its social dynamics, and Stage 2: Belief Labeling, which assigns each belief a seven-dimensional schema label covering recursive order, truth status, knowledge access, explicitness, content type, mental source, and context. Built from 895 stories from the existing ToMBench story corpus and augmented with 22,343 labeled belief propositions, OmniToM uses a human-calibrated LLM-assisted annotation pipeline. Across diverse models in zero-shot evaluation, OmniToM reveals an actor-specific belief-tracking bottleneck: current LLMs struggle with the knowledge-access and representational decisions required to transform narrative facts into actors' beliefs and shared mental states.
Comments: 30 pages, 8 figures, 19 tables; includes appendix
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.26322 [cs.AI]
(or arXiv:2605.26322v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.26322
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Adam Bawatneh [view email] [v1] Mon, 25 May 2026 20:45:08 UTC (1,301 KB)
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