Caiqi Zhang

LLM agents can be turned into powerful verifiers by giving them a workspace and evidence tools, outperforming baselines on complex long-horizon tasks by resolving disagreements and challenging consensus.

This work proposes XConf (eXperiential Confidence): estimating confidence together with the model's accumulated experience, and sees experiential confidence estimation as a new paradigm for future general-purpose confidence estimation.