Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
arXiv:2606.31916v1 Announce Type: new Abstract: Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autonomous forms demands new evaluations. In this paper we evaluate an agent's ability to induce specific belief states in other agents by taking actions rather than using conversational persuasion, a capability we call Non-Conversational Planning ToM (NCP-ToM). NCP-ToM is likely to be essential for many agent use-cases, including within user-assistant interactions and pedago
The deployment of LLMs in increasingly agentic and autonomous forms necessitates new evaluation methods beyond passive question-answering, driving research into their capacity for complex goal-oriented behavior.
Understanding how LLMs can induce belief states in other agents through actions, rather than just conversation, reveals a critical next step in AI autonomy and its potential societal impact.
The evaluation of AI capabilities is shifting from purely linguistic competence to assessing an agent's ability to achieve sophisticated strategic objectives through non-conversational means.
- · AI agents developers
- · Robotics
- · Generative AI
- · Cybernetics
- · Simple conversational AI
- · Traditional AI benchmarking
- · Human-centric control paradigms
LLMs will be evaluated and developed with a focus on their capacity for strategic, action-oriented influence.
This development will accelerate the deployment of highly autonomous AI agents in various domains, requiring new ethical and regulatory frameworks.
The integration of such agentic AI could fundamentally alter human-AI interaction dynamics, potentially blurring lines between human and artificial influence in complex systems.
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Read at arXiv cs.CL