New AI Research Introduces Tomcat Agent for Theory of Mind in Human-Agent Collaboration
A research paper introduces a novel task called Instruction Inference to assess Theory of Mind (ToM) in dynamic, goal-oriented collaboration between humans and AI agents. The work explores how large language models (LLMs) can enable effective human-agent teaming by interpreting incomplete or ambiguous instructions. Researchers developed an LLM-based agent named Tomcat, designed to infer a human principal's unspoken intentions by exercising ToM reasoning. Two variants of Tomcat were implemented: one using few-shot chain-of-thought prompting (Fs-CoT) and another method. The study, published as arXiv:2507.02935v3, examines the prospects of AI agents understanding human mental states through shared context to improve collaborative outcomes. This research addresses the challenge of agents needing to go beyond literal instructions to grasp underlying goals in collaborative environments.
Key facts
- Research introduces Instruction Inference task for assessing Theory of Mind in human-agent collaboration
- Study explores using large language models (LLMs) for effective human-agent teaming
- Tomcat is an LLM-based agent designed to exhibit Theory of Mind reasoning
- Agent must interpret incomplete or ambiguous instructions from human principals
- Research examines how agents infer unspoken intentions from shared context
- Two variants of Tomcat were implemented including Fs-CoT (few-shot chain-of-thought)
- Paper published as arXiv:2507.02935v3 with announcement type replace-cross
- Focus on dynamic, goal-oriented collaborative environments where instructions may be unclear
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