From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
arXiv:2604.19775v2 Announce Type: replace-cross 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-w
The rapid advancement and deployment of LLM agents necessitate new methods for understanding and ensuring their reliable operation, particularly as they become more autonomous.
Improved interpretability of AI agents' decision-making processes is crucial for their adoption in critical applications and for building trust in their autonomous functions.
This framework offers a refined approach to understanding the internal temporal logic of LLM agents, moving beyond black-box operations towards more transparent and explainable AI.
- · AI ethicists
- · Developers of autonomous AI systems
- · Regulators of AI
- · Sectors adopting LLM agents
- · Opponents of autonomous AI
- · Those relying on purely black-box AI approaches
Enhanced interpretability tools will accelerate the development and deployment of more complex LLM agents in diverse, sensitive environments.
Increased transparency will likely lead to greater public and regulatory acceptance of AI agents, facilitating their integration into various industries.
This could establish new industry standards for AI agent accountability, potentially influencing future AI policy and liability frameworks.
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Read at arXiv cs.CL