ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning
Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment. While these agents demonstrate impressive capabilities, their behavior is difficult to understand, explain, and analyze. Existing evaluations focus mainly on task success and execution traces, offering limited insight into the strategies employed by the agent. We present ATLAS (Automata Learning for Agent Trajectory Analysis and Strategy Discovery), an approach for recovering interpretable behavioral models from agent trajectories. ATLAS combines trace abstraction with automata learning to infer finite-state models that capture observed agent-environment i...
| Authors | Ignacio D. Lopez-Miguel, Andreas Happe, Jürgen Cito et al. |
|---|---|
| Primary category | cs.SE |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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