Title: Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction
Primary area: foundation or frontier models, including LLMs
Keywords: Large language modelm, Multi-agent System

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, 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.