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OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling

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arXiv:2605. 26322v1 Announce Type: new 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.

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Authors: Adam Bawatneh, Sagar Sapkota, Amrit Singh Bedi, Santu Karmaker, Mubarak Shah
Released: 2026-05-27 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, rect, under, eval, source, line, rate, extraction. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.26322

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