AI RESEARCH
RESCORE: LLM-Driven Simulation Recovery in Control Systems Research Papers
arXiv CS.AI
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ArXi:2604.04324v1 Announce Type: new Reconstructing numerical simulations from control systems research papers is often hindered by underspecified parameters and ambiguous implementation details. We define the task of Paper to Simulation Recoverability, the ability of an automated system to generate executable code that faithfully reproduces a paper's results. We curate a benchmark of 500 papers from the IEEE Conference on Decision and Control (CDC) and propose RESCORE, a three component LLM agentic framework, Analyzer, Coder, and Verifier.