arXiv:2606.11724v1 Announce Type: new Abstract: Theory of Mind (ToM) reasoning requires inferring agents' beliefs from partial and asymmetric observations, which remains an open challenge for LLMs. Existing prompting-based approaches improve ToM reasoning through observable-event filtering or temporal belief chains, without explicitly modeling nested beliefs. We introduce RecToM, an inference-time framework for ToM reasoning that models nested beliefs via recursive perspective construction. RecToM constructs each character perspective from the preceding character perspective along the characte
The paper, published in 2026, details a novel inference-time framework for improving Theory of Mind in LLMs, reflecting ongoing advancements in AI reasoning capabilities.
Improving Theory of Mind in LLMs is crucial for developing more sophisticated AI agents capable of understanding and navigating complex social interactions and multi-agent environments.
This research introduces recursion into belief modeling for LLMs, moving beyond simpler methods to enable more nuanced comprehension of nested beliefs and perspectives.
- · AI Agent Developers
- · LLM Research Institutions
- · Robotics Companies
- · Companies relying on simplistic AI models
- · Legacy AI architectures
More capable AI assistants and social robots that can better infer human intentions and beliefs.
Accelerated development of AI systems for complex multi-agent tasks, potentially in strategic and defense applications.
Ethical considerations around AI's ability to 'understand' and manipulate human perspectives becoming more pressing.
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Read at arXiv cs.AI