NetAgentBench is an FSM-based benchmark showing that state-of-the-art LLM agents solve basic network configs but suffer exploration meltdowns and coherence collapse on expert-level tasks.
A theory of timed automata
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The authors introduce affine repulsive RL policies that provably satisfy hard affine state constraints for black-box hybrid dynamical systems with affine reset maps by deriving sufficient closed-loop safety conditions and testing on pendulum and juggler examples.
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NetAgentBench: A State-Centric Benchmark for Evaluating Agentic Network Configuration
NetAgentBench is an FSM-based benchmark showing that state-of-the-art LLM agents solve basic network configs but suffer exploration meltdowns and coherence collapse on expert-level tasks.
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Learning Control Policies to Provably Satisfy Hard Affine Constraints for Black-Box Hybrid Dynamical Systems
The authors introduce affine repulsive RL policies that provably satisfy hard affine state constraints for black-box hybrid dynamical systems with affine reset maps by deriving sufficient closed-loop safety conditions and testing on pendulum and juggler examples.