Most confidence estimators judge a response solely from the current inference trace. This paper argues that an LLM's own history — graded past episodes annotated with outcome and lessons — is a powerful signal for estimating whether a new answer will be correct. XConf uses recall + reflection over the model's experience to produce a revised, empirically grounded confidence score.
AI Agent Papers2026
Confidence Comes from Experience: Experiential Confidence Estimation from Reasoning to Agents
Estimates a model's probability of correctness by leveraging its past graded episodes: XConf recalls similar past cases and prompts the model to reflect and restate confidence, improving calibration and discrimination across reasoning, coding, multimodal QA, and agent tasks at much lower generation cost.
Introduction
Information
- Websitearxiv.org
- AuthorsCaiqi Zhang, Xiaochen Zhu, Chengzu Li, Yulong Chen, Dharshan Kumaran, Nigel Collier
- Published date2026/09/15
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