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This paper introduces a novel automata-based approach for control synthesis from signal temporal logic specifications using reinforcement learning, improving robustness scores and satisfaction rates over existing methods.
This paper presents Prove-RT, an LLM-assisted framework for generating Prosa/Rocq mechanized theorem prover scripts for schedulability analysis in real-time systems, achieving a 44.7% success rate on a curated evaluation set.