Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915721842262016 |
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| author | Shen, Xu Zhang, Qi Wang, Song Tan, Zhen Zhao, Xinyu Yao, Laura Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Lee, Kwonjoon Chen, Tianlong |
| author_facet | Shen, Xu Zhang, Qi Wang, Song Tan, Zhen Zhao, Xinyu Yao, Laura Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Lee, Kwonjoon Chen, Tianlong |
| contents | Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents and disrupt the trajectory. In this paper, we present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction. MASC rethinks detection as history-conditioned anomaly scoring via two complementary designs: (1) Next-Execution Reconstruction, which predicts the embedding of the next step from the query and interaction history to capture causal consistency, and (2) Prototype-Guided Enhancement, which learns a prototype prior over normal-step embeddings and uses it to stabilize reconstruction and anomaly scoring under sparse context (e.g., early steps). When an anomaly step is flagged, MASC triggers a correction agent to revise the acting agent's output before information flows downstream. On the Who&When benchmark, MASC consistently outperforms all baselines, improving step-level error detection by up to 8.47% AUC-ROC ; When plugged into diverse MAS frameworks, it delivers consistent end-to-end gains across architectures, confirming that our metacognitive monitoring and targeted correction can mitigate error propagation with minimal overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_14319 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction Shen, Xu Zhang, Qi Wang, Song Tan, Zhen Zhao, Xinyu Yao, Laura Tadiparthi, Vaishnav Mahjoub, Hossein Nourkhiz Pari, Ehsan Moradi Lee, Kwonjoon Chen, Tianlong Artificial Intelligence Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors: a single faulty step can propagate across agents and disrupt the trajectory. In this paper, we present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction. MASC rethinks detection as history-conditioned anomaly scoring via two complementary designs: (1) Next-Execution Reconstruction, which predicts the embedding of the next step from the query and interaction history to capture causal consistency, and (2) Prototype-Guided Enhancement, which learns a prototype prior over normal-step embeddings and uses it to stabilize reconstruction and anomaly scoring under sparse context (e.g., early steps). When an anomaly step is flagged, MASC triggers a correction agent to revise the acting agent's output before information flows downstream. On the Who&When benchmark, MASC consistently outperforms all baselines, improving step-level error detection by up to 8.47% AUC-ROC ; When plugged into diverse MAS frameworks, it delivers consistent end-to-end gains across architectures, confirming that our metacognitive monitoring and targeted correction can mitigate error propagation with minimal overhead. |
| title | Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.14319 |