OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind
The paper presents OSCToM, a new approach for modeling nested belief conflicts in language models' Theory of Mind reasoning. It highlights the limitations of existing benchmarks and demonstrates that OSCToM significantly improves performance on complex social reasoning tasks. The results indicate that targeted training data can enhance smaller models' cognitive reasoning capabilities.
- ▪OSCToM combines reinforcement learning and compositional surrogate models to generate observer-self conflicts.
- ▪The approach achieved 76% accuracy on the FANToM benchmark, a significant improvement over the 0.2% reported by ExploreToM.
- ▪The data-synthesis procedure used in OSCToM is six times more efficient than previous methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20423 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Artificial Intelligence arXiv:2605.20423 (cs) [Submitted on 19 May 2026] Title:OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind Authors:Sharmin Sultana Srishty, Kazi Mahathir Rahman, Malaika Parizat Sakkhi, Samia Shahid Prianna, Shaikhul Islam Sinat View a PDF of the paper titled OSCToM: RL-Guided Adversarial Generation for High-Order Theory of Mind, by Sharmin Sultana Srishty and 4 other authors View PDF HTML (experimental) 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.
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