@inproceedings{shen-etal-2026-metacognitive,
title = "Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction",
author = "Shen, Xu and
Zhang, Qi and
Wang, Song and
Tan, Zhen and
Zhao, Xinyu and
Yao, Laura and
Tadiparthi, Vaishnav and
Mahjoub, Hossein Nourkhiz and
Moradi Pari, Ehsan and
Lee, Kwonjoon and
Chen, Tianlong",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1168/",
pages = "23320--23337",
ISBN = "979-8-89176-395-1",
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 When benchmark, MASC consistently outperforms all baselines, achieving up to 7.8{\%} AUC-ROC improvement in the challenging w/o GT setting, and further delivers consistent gains on AgentErrorBench. 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."
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<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 When benchmark, MASC consistently outperforms all baselines, achieving up to 7.8% AUC-ROC improvement in the challenging w/o GT setting, and further delivers consistent gains on AgentErrorBench. 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.</abstract>
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%0 Conference Proceedings
%T Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction
%A Shen, Xu
%A Zhang, Qi
%A Wang, Song
%A Tan, Zhen
%A Zhao, Xinyu
%A Yao, Laura
%A Tadiparthi, Vaishnav
%A Mahjoub, Hossein Nourkhiz
%A Moradi Pari, Ehsan
%A Lee, Kwonjoon
%A Chen, Tianlong
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shen-etal-2026-metacognitive
%X 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, achieving up to 7.8% AUC-ROC improvement in the challenging w/o GT setting, and further delivers consistent gains on AgentErrorBench. 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.
%U https://aclanthology.org/2026.findings-acl.1168/
%P 23320-23337
Markdown (Informal)
[Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction](https://aclanthology.org/2026.findings-acl.1168/) (Shen et al., Findings 2026)
ACL
- Xu Shen, Qi Zhang, Song Wang, Zhen Tan, Xinyu Zhao, Laura Yao, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Kwonjoon Lee, and Tianlong Chen. 2026. Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 23320–23337, San Diego, California, United States. Association for Computational Linguistics.