MedCoG: Maximizing LLM Inference Density in Medical Reasoning via Meta-Cognitive Regulation

ArXi:2602.07905v2 Announce Type: replace Large Language Models (LLMs) have shown strong potential in complex medical reasoning yet face diminishing gains under inference scaling laws. While existing studies augment LLMs with various knowledge types, it remains unclear how effectively the additional costs translate into accuracy. In this paper, we explore how meta-cognition of LLMs, i.e., their self-assessment of their own cognitive states, can regulate the reasoning process.