From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistically label model's internal representation at each step as successful or failing. Linear probes a...
| Authors | Trilok Padhi, Ramneet Kaur, Krishiv Agarwal et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 March 2026 |
| Last updated at source | 1 July 2026 |
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