From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
Summary: arXiv:2604.19775v1 Announce Type: new
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.
Introduction
The rise of LLMs has transformed how we interact with technology. These models not only generate text but also engage in complex decision-making processes. However, understanding how they arrive at specific conclusions remains a significant challenge.
Conformal Interpretability Framework
This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. The conformal interpretability framework for temporal tasks combines step-wise reward modeling with conformal prediction, allowing researchers to statistically label the model’s internal representations at each step as either successful or failing.
Methodology
To implement this framework, linear probes are trained on the model’s representations. These probes identify latent directions in the activation space that correspond to consistent notions of success, failure, or reasoning drift. This method enables a clearer understanding of how LLMs process and evolve their concepts over time.
Experimental Results
The framework was tested in two simulated interactive environments: ScienceWorld and AlfWorld. The results demonstrated that the temporal concepts identified were linearly separable. This linear separability reveals interpretable structures aligned with task success, providing insights into the underlying mechanisms of LLMs.
Performance Improvement
Preliminary results also indicate that the proposed framework can enhance an LLM agent’s performance. By steering the identified successful directions within the model, researchers can intervene effectively and potentially rectify issues related to failures in task execution.
Conclusion
The conformal interpretability framework offers a principled method for early failure detection and intervention in LLM-based agents. By enhancing our understanding of how these models operate in complex interactive settings, we pave the way towards more trustworthy autonomous language models, which is crucial for their deployment in real-world scenarios.
Future Work
Future research may focus on refining the conformal interpretability framework and exploring its applicability across different domains and tasks. By improving interpretability, we can foster greater trust in LLMs and their capabilities in diverse applications.
References
- arXiv:2604.19775v1
- ScienceWorld Interactive Environment
- AlfWorld Simulation