MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent
  Systems
Authors: name: Xuanming Zhang, name: Yuxuan Chen, name: Min-Hsuan Yeh, name: Yixuan Li

Human social interactions depend on the ability to infer others' unspoken
intentions, emotions, and beliefs-a cognitive skill grounded in the
psychological concept of Theory of Mind (ToM). While large language models
(LLMs) excel in semantic understanding tasks, they struggle with the ambiguity
and contextual nuance inherent in human communication. To bridge this gap, we
introduce MetaMind, a multi-agent framework inspired by psychological theories
of metacognition, designed to emulate human-like social reasoning. MetaMind
decomposes social understanding into three collaborative stages: (1) a
Theory-of-Mind Agent generates hypotheses user mental states (e.g., intent,
emotion), (2) a Domain Agent refines these hypotheses using cultural norms and
ethical constraints, and (3) a Response Agent generates contextually
appropriate responses while validating alignment with inferred intent. Our
framework achieves state-of-the-art performance across three challenging
benchmarks, with 35.7% improvement in real-world social scenarios and 6.2% gain
in ToM reasoning. Notably, it enables LLMs to match human-level performance on
key ToM tasks for the first time. Ablation studies confirm the necessity of all
components, which showcase the framework's ability to balance contextual
plausibility, social appropriateness, and user adaptation. This work advances
AI systems toward human-like social intelligence, with applications in
empathetic dialogue and culturally sensitive interactions. Code is available at
https://github.com/XMZhangAI/MetaMind.