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

2026-05-27

AI Summary

This work addresses the pervasive challenges in industrial Prognostics and Health Management (PHM) research—namely, poor reproducibility and limited cross-study comparability—stemming from data scarcity, ambiguous protocols, and implicit design choices. To overcome these issues, the authors propose an agent-based automated reproduction framework that, for the first time, integrates intelligent agents with a unified benchmark. The framework employs a slot-binding interface to structurally map tasks, data, and model components while explicitly documenting unresolved assumptions. It introduces standardized task contracts, a framework-enhanced assumption-binding mechanism, and evaluation hooks to enable hypothesis-aware, verifiable, and cross-paper comparable reproduction workflows. Experiments across 16 PHM papers demonstrate that this approach significantly improves both reproduction success rates and inter-method comparability, thereby advancing PHM research from isolated code synthesis toward systematic benchmark construction.

Abstract

Industrial Prognostics and Health Management (PHM) provides a representative case study 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. Existing paper-to-code systems generate implementations for individual papers, but these artifacts are often not directly comparable due to inconsistencies in assumptions and evaluation settings. We introduce \emph{agentic, framework-based PHM paper reproduction}, where an agent translates a paper into a shared PHM benchmark framework via a \emph{slot-binding interface}. This interface maps equations and protocol descriptions into structured components (task definitions, dataset adapters, windowing, targets, models, and evaluators), while explicitly recording unresolved assumptions. The resulting implementations are validated against standardized task contracts and evaluation hooks, enabling consistent and comparable benchmarking. We evaluate this approach on 16 PHM papers, comparing framework-enhanced, skill-based and prompt-based agentic reproduction against a recent framework-free paper-reproduction agent. We assess reproduction success, model-based code evaluation, framework binding of paper assumptions, and cross-paper benchmark comparability under standardized protocols. Our results show that coupling agentic generation with a shared framework transforms paper reproduction from isolated code synthesis into executable, assumption-aware, and systematically comparable benchmark implementations.

Problem

• reproducibility
• Prognostics and Health Management
• benchmarking
• under-specified methods
• paper-to-code

Innovation

• agentic reproduction
• framework-based benchmarking
• slot-binding interface
• assumption-aware implementation
• machine health intelligence