Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action

2026-06-30 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Expert, medium

Summary

A novel evaluation framework, NCP-ExploreToM, assesses Large Language Models' (LLMs) Non-Conversational Planning Theory of Mind (NCP-ToM), which is the ability to induce specific belief states in other agents through actions rather than conversation. This study evaluated six frontier models, including GPT-5, Gemini 2.5 Pro, and the Claude 4 series, alongside human participants across 600 task instances. GPT-5 achieved approximately 80% success in the agentic setting, uniquely outperforming human participants, though it demonstrated less robustness across contexts. A significant finding was that all models, similar to humans, performed better when inducing true belief states compared to false belief states, which is a positive indicator for alignment efforts. These results underscore emerging social-reasoning capabilities in LLMs for non-conversational tasks and emphasize the critical need for agentic evaluations to understand the safety and alignment of autonomous social agents.

Key takeaway

For AI Engineers developing autonomous LLM agents, you should prioritize evaluating their Non-Conversational Planning Theory of Mind (NCP-ToM) capabilities. This study indicates that while models like GPT-5 show strong agentic social reasoning, their robustness varies, especially with false belief states. Incorporate agentic evaluation frameworks like NCP-ExploreToM into your testing protocols to identify potential manipulation risks and ensure safer, more aligned agent deployments.

Key insights

Large Language Models can induce belief states in others through planned actions, extending Theory of Mind beyond conversation.

Principles

• Agentic evaluations are essential for LLM safety and alignment.
• Inducing true belief states is easier for LLMs than false ones.
• NCP-ToM is critical for user-assistant and pedagogical LLM agents.

Method

The NCP-ExploreToM framework provides models with belief state goals, requiring them to manipulate objects or characters in rooms to achieve these goals.

In practice

• Implement agentic ToM evaluations for autonomous LLMs.
• Focus alignment strategies on false belief state induction.

Topics

• Theory of Mind
• Large Language Models
• Agentic AI
• Belief State Induction
• LLM Evaluation
• AI Safety

Code references

• ulab-uiuc/ToMAP

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.