基本信息
- 来源: arxiv
- 原始来源: https://arxiv.org/abs/2602.06948v1
- 作者: Jean Kaddour, Srijan Patel, Gbètondji Dovonon, Leo Richter, Pasquale Minervini, Matt J. Kusner
- 分类: cs.AI
- 论文时间: 2026-02-06T18:49:35Z
- 论文 PDF: https://arxiv.org/pdf/2602.06948v1.pdf
来源摘要/节选
Can AI agents predict whether they will succeed at a task? We study agentic uncertainty by eliciting success probability estimates before, during, and after task execution. All results exhibit agentic overconfidence: some agents that succeed only 22% of the time predict 77% success. Counterintuitively, pre-execution assessment with strictly less information tends to yield better discrimination than standard post-execution review, though differences are not always significant. Adversarial prompting reframing assessment as bug-finding achieves the best calibration.
来源说明
当前只保存了官方论文摘要，不代表论文全文。请以原始来源为准。
本页只呈现已做哈希绑定的来源证据，不包含基于旧正文或缺失原文的扩展推断。