title: MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent
    Systems
summary: 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.
paper_authors._id: 6836670b2177a249476301a4
paper_authors.name: Xuanming Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:24.572Z
paper_authors._id: 65fc5109899083a2aad987c5
paper_authors.avatarUrl: /avatars/289dbb8128746d931118cff6f6871a45.svg
paper_authors.fullname: XUANMING ZHANG
paper_authors.type: user
paper_authors.user: XUANMINGZHANG
paper_authors._id: 6836670b2177a249476301a5
paper_authors.name: Yuxuan Chen
paper_authors._id: 6836670b2177a249476301a6
paper_authors.name: Min-Hsuan Yeh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:26.947Z
paper_authors._id: 63c07f198d1175e3399d2161
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1673559768829-noauth.jpeg
paper_authors.fullname: Min-Hsuan Yeh
paper_authors.type: user
paper_authors.user: samuelyeh
paper_authors._id: 6836670b2177a249476301a7
paper_authors.name: Yixuan Li
paper_title: MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent
    Systems
paper_summary: 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.
paper_ai_summary: MetaMind, a multi-agent framework inspired by metacognition, enhances LLMs' ability to perform Theory of Mind tasks by decomposing social understanding into hypothesis generation, refinement, and response generation, achieving human-like performance.
paper_ai_keywords: Theory of Mind (ToM) large language models (LLMs) Multi-agent framework Theory-of-Mind Agent Domain Agent Response Agent Cultural norms Ethical constraints Social intelligence Empathetic dialogue Culturally sensitive interactions