From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Trilok Padhi, Ramneet Kaur, Krishiv Agarwal, Adam D. Cobb, Daniel Elenius, Manoj Acharya, Colin Samplawski, Alexander M. Berenbeim, Nathaniel D. Bastian, Susmit Jha, Ugur Kursuncu, Anirban Roy
Why It Matters
What makes this one worth your time
Understanding and improving the decision-making processes of LLM agents in interactive environments is crucial for developing trustworthy and effective autonomous systems.
A framework for interpreting and improving LLM agents' temporal reasoning through conformal prediction and linear probing.
Summary
The paper introduces a framework for interpreting the temporal evolution of concepts in large language model (LLM) agents using a conformal interpretability approach. This involves step-wise reward modeling combined with conformal prediction to label the model's internal representations as successful or failing. Linear probes are used to identify latent directions in the model's activation space that correspond to task success or failure. Experiments in simulated environments demonstrate the separability of these temporal concepts and suggest potential performance improvements by steering the model based on these insights.
Key contributions
- Introduction of a conformal interpretability framework for temporal tasks in LLM agents.
- Demonstration of linearly separable temporal concepts in LLMs' activation space.
- Preliminary results on improving LLM performance by steering identified successful directions.
Notable insights
- Combining step-wise reward modeling with conformal prediction to interpret LLMs' internal states.
- Using linear probes to identify latent directions in activation space related to task success.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2604.19775v2 Announce Type: replace 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.