5 citations · 9 across the 6 of their papers we have counts for
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cs.SE2026
ATLAS: Discovering Agent Strategies through LLM-Guided Abstraction and Automata Learning
Ignacio D. Lopez-Miguel, Andreas Happe, Jürgen Cito +3
Large Language Model (LLM)-based agents are increasingly used for complex tasks such as software testing and cybersecurity assessment. While these agents demonstrate impressive cap…
cs.LG2026
ORCAID: Oblique Rule-Based Continuous-Action Interpretation for Deep RL Policies
Ignacio D. Lopez-Miguel, Ezio Bartocci, Thomas Eiter +1
Explainability remains a key issue in reinforcement learning (RL). Distilling an interpretable policy from an agent trained in a complex environment is particularly challenging whe…