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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2507.15015 |
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| _version_ | 1866917349129453568 |
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| author | Hou, Xinmeng Chang, Ziting Lu, Zhouquan Wenli, Chen Wan, Liang Feng, Wei Hu, Hai Guo, Qing |
| author_facet | Hou, Xinmeng Chang, Ziting Lu, Zhouquan Wenli, Chen Wan, Liang Feng, Wei Hu, Hai Guo, Qing |
| contents | Large language models (LLMs) fail on over one-third of multi-hop questions with counterfactual premises and remain vulnerable to adversarial prompts that trigger biased or factually incorrect responses, which exposes a fundamental deficit in self-regulated reasoning. We propose \textbf{MetaCrit}, a multi-agent framework grounded in Nelson and Narens' metacognitive regulation theory. MetaCrit decomposes reasoning regulation into four agents: object-level generation, a \emph{monitoring} agent that assesses response validity, a \emph{control} agent that critiques logical soundness, and a meta-level synthesizer that integrates all signals into a final response. Evaluation across eight benchmarks, four model backbones, and a college-level analytical writing study shows that MetaCrit significantly improves content truthfulness and logical soundness while eliminating toxic outputs. Its modular design allows individual agents to be integrated into existing frameworks as drop-in components without architectural modifications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MetaCrit: A Critical Thinking Framework for Self-Regulated LLM Reasoning Hou, Xinmeng Chang, Ziting Lu, Zhouquan Wenli, Chen Wan, Liang Feng, Wei Hu, Hai Guo, Qing Multiagent Systems Large language models (LLMs) fail on over one-third of multi-hop questions with counterfactual premises and remain vulnerable to adversarial prompts that trigger biased or factually incorrect responses, which exposes a fundamental deficit in self-regulated reasoning. We propose \textbf{MetaCrit}, a multi-agent framework grounded in Nelson and Narens' metacognitive regulation theory. MetaCrit decomposes reasoning regulation into four agents: object-level generation, a \emph{monitoring} agent that assesses response validity, a \emph{control} agent that critiques logical soundness, and a meta-level synthesizer that integrates all signals into a final response. Evaluation across eight benchmarks, four model backbones, and a college-level analytical writing study shows that MetaCrit significantly improves content truthfulness and logical soundness while eliminating toxic outputs. Its modular design allows individual agents to be integrated into existing frameworks as drop-in components without architectural modifications. |
| title | MetaCrit: A Critical Thinking Framework for Self-Regulated LLM Reasoning |
| topic | Multiagent Systems |
| url | https://arxiv.org/abs/2507.15015 |