Metacognitive Arbitration as Uncertainty Compression in Multi-Agent Language Models

Multi-agent reasoning and metacognitive strategies are widely used to improve large language model ({LLM}) performance, yet the functional role of metacognition remains unclear. Prior methods often treat metacognition as a mechanism for generating better or more diverse reasoning. In this work, we argue that metacognition primarily functions as an information routing and compression mechanism rather than a generator of new reasoning content. We introduce MC-MAS, an inference-time framework that separates problem solving from metacognitive arbitration, where independent solvers propose candidate answers and an arbiter critiques and consolidates their outputs. Using routing-centric metrics: semantic novelty, entropy reduction, and overconfident errors across four reasoning benchmarks and multiple model settings, we show that MC-MAS introduces limited novelty but consistently reduces uncertainty and overconfidence, with accuracy gains that are modest and dataset-dependent. These results indicate that reliable improvements arise from structured arbitration rather than reflection or increased sampling alone.