SOURCE-LINKED INTELLIGENCE
Risk-Aware Online Conformal State Probing
AI-based autonomous agents, typically hosted at data centers, must acquire state information from robots or edge devices in order to issue informed control decisions. Managing uncertainty about the state is particularly consequential in safety-critical settings, in which average-case guarantees are insufficient. In this context, we study a sequential decision maker process that jointly decides which actions to take and when to probe given access to an arbitrary state prediction model. We propose online conformal state probing (OCSP), an action and probing policy that certifies worst-case relia
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-22T08:55:09.000Z
First collected: 2026-09-23T04:21:13.910Z. This is not the publication date.