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ARXIV:2605.20423 · THEORY OF MIND · SUBMITTED 21 MAY · 20:31 UTC · FRESHNESS STALE
ARXIV:2605.20423THEORY OF MINDSUBMITTED 21 MAY · 20:31 UTCFRESHNESS STALESharmin Sultana Srishty · Kazi Mahathir Rahman · Malaika Parizat Sakkhi · Samia Shahid Prianna · Shaikhul Islam Sinat · arXiv
OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning.
Opportunity summary
Pain OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult.
Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In our experiments, OSCToM-8B gives the best overall result among the systems tested. A public repository is linked, so build verification can inspect implementation…
Theory of Mind moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning.
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10.48550/arXiv.2605.20423OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning.
Abstract
Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult. This paper presents OSCToM (Observer-Self Conflict Theory of Mind), an approach for modeling nested belief conflicts in LLM-based ToM tasks. The key case is one in which an observer's view of another agent conflicts with the observer's own belief state. Such cases go beyond simple perspective-taking and require recursive, multi-layered reasoning. OSCToM combines reinforcement learning (RL), an extended domain-specific language, and compositional surrogate models to generate observer-self conflicts. In our experiments, OSCToM-8B gives the best overall result among the systems tested. It improves on the reported ExploreToM results on FANToM and remains competitive on Hi-ToM and BigToM. On the information-asymmetric FANToM benchmark, OSCToM reaches 76% accuracy, compared with the 0.2% reported by ExploreToM. The data-synthesis procedure is also 6x more efficient, indicating that targeted training data can help smaller models handle advanced cognitive reasoning. The project code is available at https://github.com/sharminsrishty/osct.
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PROBLEM
OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult.
METHOD
Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In our experiments, OSCToM-8B gives the best overall result among the systems tested. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper...
WHY NOW
Theory of Mind moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Large Language Models (LLMs) perform well on many language tasks, but their Theory of Mind (ToM) reasoning is still uneven in complex social settings. Existing benchmarks, including ExploreToM, do not always test the recursive beliefs and information asymmetries that make these settings difficult.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. In our experiments, OSCToM-8B gives the best overall result among the systems tested. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Theory of Mind moved forward this cycle; last verified May 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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OSCToM enhances LLMs' Theory of Mind reasoning by modeling nested belief conflicts using reinforcement learning.
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Theory of Mind
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2/3 checks · 67%
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