{"ID":2854434,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.14319","arxiv_id":"2510.14319","title":"Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction","abstract":"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\u0026When 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.","short_abstract":"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-le...","url_abs":"https://arxiv.org/abs/2510.14319","url_pdf":"https://arxiv.org/pdf/2510.14319v2","authors":"[\"Xu Shen\",\"Qi Zhang\",\"Song Wang\",\"Zhen Tan\",\"Xinyu Zhao\",\"Laura Yao\",\"Vaishnav Tadiparthi\",\"Hossein Nourkhiz Mahjoub\",\"Ehsan Moradi Pari\",\"Kwonjoon Lee\",\"Tianlong Chen\"]","published":"2025-10-16T05:35:37Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}