From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

TL;DR

This paper introduces a conformal interpretability framework for analyzing temporal concepts in LLM agents, enabling understanding, early failure detection, and potential performance improvement in interactive environments.

Contribution

It proposes a novel step-wise conformal interpretability method that identifies and leverages temporal concepts in LLMs for better transparency and control.

Findings

Temporal concepts are linearly separable in LLM activation space.

The framework enables early failure detection in LLM agents.

Preliminary results show potential for improving agent performance.

Abstract

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 are then trained on these representations to identify directions of temporal concepts - latent directions in the model's activation space that correspond to consistent notions of success, failure or reasoning drift. Experimental results on two simulated interactive environments, namely ScienceWorld and AlfWorld, demonstrate that these temporal concepts are linearly separable, revealing interpretable structures aligned with task success. We further show preliminary results on improving an LLM agent's performance by leveraging the proposed framework for steering the identified successful directions inside the model. The proposed approach, thus, offers a principled method for early failure detection as well as intervention in LLM-based agents, paving the path towards trustworthy autonomous language models in complex interactive settings.