发表机构
Fox School of Business, Temple University; Robert H. Smith School of Business, University of Maryland（天普大学福克斯商学院; 马里兰大学罗伯特·H·史密斯商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出将认知行动概念引入基于LLM的智能体设计，区分获取、转换和探测三种模式，强调决策前证据准备的重要性，并引入认知脚手架使证据产生过程可审计。
AI 中文摘要
在做出艰难决定之前，人们常常仅为了更清楚地理解情况而采取行动。我们转动一个物体以观察其另一面，将备选方案并排摆放，或改变一个条件并观察会发生什么。这些行动可能无法完成任务，但它们改善了做出下一个选择所需的证据。基于LLM的智能体能够搜索和探索，然而智能体设计较少关注一个更早的问题：现有证据是否已为决策做好准备？有时必要的证据缺失。在其他情况下，证据存在但其形式掩盖了关键信息，或者用于评判证据所需的比较尚不存在。认知科学将改善后续选择基础的行动称为认知行动。我们将这一概念引入基于LLM的智能体，并区分三种模式：获取缺失证据、转换现有证据，以及探测系统以产生揭示性响应。我们使用术语“认知脚手架”来指代使这些行动成为可能且可审计的接口、工具和环境。本文主张，智能体设计必须解决决策就绪证据是如何产生的问题。
英文摘要
Before a difficult decision, people often act simply to understand the situation better. We turn an object to see another side, place alternatives next to each other, or change one condition and observe what happens. These actions may not complete the task, but they improve the evidence needed for the next choice. LLM-based agents can search and explore, yet agent design gives less attention to an earlier question: is the available evidence ready for the decision? Sometimes necessary evidence is missing. In other cases, the evidence is present but its form hides what matters, or the comparison needed to judge it does not yet exist. Cognitive science calls actions that improve the basis for a later choice epistemic actions. We bring this idea to LLM-based agents and distinguish three modes: acquiring missing evidence, transforming available evidence, and probing a system to create a revealing response. We use the term epistemic scaffolding for the interfaces, tools, and environments that make these actions possible and auditable. This paper argues that agent design must address how decision-ready evidence is produced.
CommentsAccepted at the Foundations of Agentic Systems Theory (FAST) Workshop at NeurIPS 2026
