Conformal Interpretability Framework for LLM Agent Temporal Concepts
A recent preprint on arXiv (2604.19775) presents a framework aimed at understanding how concepts evolve over time within LLM agents. This approach merges step-wise reward modeling with conformal prediction to statistically categorize internal representations at each phase as either successful or unsuccessful. Linear probes are utilized to detect latent directions that relate to success, failure, or shifts in reasoning. The research involved experiments carried out in two simulated environments.
Key facts
- arXiv preprint 2604.19775
- Title: From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
- Introduces conformal interpretability framework for temporal tasks
- Combines step-wise reward modeling with conformal prediction
- Labels model's internal representation at each step as successful or failing
- Trains linear probes to identify directions of temporal concepts
- Concepts include success, failure, and reasoning drift
- Experimental results on two simulated environments
Entities
Institutions
- arXiv