LLMs Know When They Know, but Do Not Act on It: A Metacognitive Harness for Test-time Scaling

ArXi:2605.14186v1 Announce Type: new Large language models (LLMs) often expose useful signals of self-monitoring: before solving a problem, they can estimate whether they are likely to succeed, and after solving it, they can judge whether their answer is likely to be correct. However, these signals are typically measured or elicited in isolation, rather than used to control inference. In this work, we ask whether LLMs possess latent metacognitive ability that can be turned into effective test-time control.