From paper to benchmark: agentic, framework-based reproduction of under-specified methods in machine health intelligence

ArXi:2605.28371v1 Announce Type: new Industrial Prognostics and Health Management (PHM) provides a representative for a broader challenge in applied machine learning: translating published papers into executable, benchmark-ready implementations. Reproducing under-specified methods in PHM is particularly difficult due to restricted access to industrial datasets, incomplete reporting of preprocessing and evaluation protocols, and implicit design choices (e.g., windowing, target construction, data splits) that critically affect performance.