Critic Experience Bank: Self-Evolving Step-Level Confidence Estimation for LLM Agents
LLM agents operate in stateful environments, where a single erroneous step can waste limited interaction budget or cause irreversible effects before task failure becomes apparent. Reliable deployment therefore requires step-level confidence estimation: estimating, before execution, the probability that a proposed action will advance the task. Existing LLM confidence estimators are typically designed for static question answering under a fixed task context and evaluation criterion. For an agent, however, its action productivity depends on an environment transition that is observed only after execution. To address this challenge, we introduce Critic Experience Bank (CEB), a training-free fr...
| Authors | Yaopei Zeng, Congchao Wang, JianHang Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 July 2026 |
| Last updated at source | 6 October 2026 |
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