mlsanityData to 7 Oct 2026

Before Agents Decide: Epistemic Action in LLM-Based Systems

arXiv 2610.00511 ↗First posted 30 Sep 2026cs.AI cs.MAcited by 0

On the map: LLM agents and tool use → Social simulation and multi-agent debate also fits: General LLM agent frameworks and benchmarks

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use. Builds on 7 of its 17 references that are in the map. First posted on arXiv 30 Sep 2026.

Builds on

7 of 17 references.

References · 7

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,628
  3. 2022WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agentscited by 1,197
  4. 2022ScienceWorld: Is your Agent Smarter than a 5th Grader?cited by 298
  5. 2023LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelscited by 279
  6. 2016Revisiting Active Perceptioncited by 90
  7. 2024DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agentscited by 89

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026When Agents Say One Thing and Do Another: Validating Elicited Beliefs from LLMs
  2. 2025Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
  3. 2025Who Sees What? Structured Thought-Action Sequences for Epistemic Reasoning in LLMs
  4. 2026AI scientists produce results without reasoning scientifically
  5. 2026Doing What They Say, Not What They Reason: Locating the Faithfulness Gap in LLM Agents
  6. 2025Thoughts without Thinking: Reconsidering the Explanatory Value of Chain-of-Thought Reasoning in LLMs through Agentic Pipelines
  7. 2026Universe Routing: Why Self-Evolving Agents Need Epistemic Control
  8. 2026The Epistemics of Agent Memory: Measuring, and Governing, the Consolidation Decision in Long-Horizon LLM Agents