Before Agents Decide: Epistemic Action in LLM-Based Systems

The paper discusses how large language model (LLM) agents can perform epistemic actions—actions that improve the evidence base before making a decision. It identifies three modes of such actions: acquiring missing evidence, transforming available evidence, and probing the system to generate revealing responses. The authors introduce the concept of epistemic scaffolding, referring to interfaces, tools, and environments that enable and audit these actions, arguing that agent design must explicitly address how decision-ready evidence is produced.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• llms
• agents