Meta-reasoning in autonomous agents: performance gains across benchmarks and models

Introduction: Meta-reasoning, the ability of an autonomous agent to monitor and regulate its own reasoning processes, has emerged as a critical component for achieving adaptive and trustworthy artificial intelligence. However, limited empirical evidence quantifies how meta-reasoning impacts agent performance across diverse contexts and model architectures. This study presents a comprehensive empirical analysis of meta-reasoning capabilities in large language model (LLM)-based autonomous agents using two established benchmarks: GAIA (General AI Assistants) and AgentBench.