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

Researchers introduce an agentic, framework-based approach to reproducibly translate machine learning papers—specifically in Prognostics and Health Management (PHM)—into executable, comparable benchmark implementations. By mapping papers onto a shared framework with structured slot-binding interfaces, the method addresses critical reproducibility gaps caused by incomplete documentation, implicit design choices, and restricted dataset access.

Reproducibility remains a stubborn challenge in applied machine learning research. Published papers often lack sufficient implementation detail, making it difficult for practitioners to rebuild methods or conduct fair comparisons. This study addresses a genuine bottleneck: converting academic descriptions into production-ready code while preserving methodological intent and enabling systematic benchmarking across studies.

The framework introduces an agent-based translation layer that maps paper equations and protocols into standardized components—task definitions, dataset adapters, windowing strategies, model definitions, and evaluators. A slot-binding interface explicitly records unresolved assumptions rather than papering over them, turning implicit design choices into visible, auditable decisions. Testing across 16 PHM papers demonstrates that coupling agentic generation with shared infrastructure yields implementations more comparable and reliable than isolated code synthesis approaches.

This approach has direct implications for how industrial AI research advances. In domains like predictive maintenance, where datasets are proprietary and failure modes are critical, reproducible benchmarking accelerates technology adoption and builds confidence in deploying algorithmic systems. Framework-based reproduction also reduces redundant reimplementation effort across research teams, lowering barriers to validation and extension.

Future adoption hinges on whether domain-specific benchmarking frameworks proliferate beyond PHM. The methodology could extend to healthcare diagnostics, network anomaly detection, or other specialized ML applications where standardized evaluation remains fragmented. The explicit treatment of assumptions also creates audit trails valuable for regulated industries, where transparency in model development carries compliance weight.

• →Agentic framework-based reproduction produces more comparable and reliable implementations than isolated paper-to-code translation approaches.
• →Explicit slot-binding interfaces make implicit design choices visible and auditable, improving transparency in algorithm reproduction.
• →The method addresses reproducibility failures caused by incomplete documentation, proprietary datasets, and missing evaluation protocol details.
• →Standardized framework benchmarking accelerates validation and deployment of industrial AI systems in regulated domains.
• →The approach is domain-agnostic and could extend beyond PHM to healthcare, anomaly detection, and other specialized ML applications.