DuoEvo: Recursive Self-Improvement via Heterogeneous Challenger-Solver Co-Evolution for Industrial Fault Diagnosis

Industrial automation requires reliable fault diagnosis under changing sensing conditions. Yet developing robust diagnostic models still relies on expert choices of training disturbances and network architectures. Recursive self-improvement (RSI) offers a route to automating model development, but most recent approaches leave the underlying neural architecture fixed. We introduce DuoEvo, a heterogeneous Challenger–Solver co-evolution framework that integrates challenge discovery with joint adaptation of model parameters and source-level structure. A Large Language Model(LLM)-based agent proposes executable noise programs and architecture revisions. Guided by the deployed Solver's weaknesses, the Challenger evolves a training curriculum; the Solver updates its parameters and evaluates structural revisions under matched curricula and budgets. Validation gates control deployment, and a patience rule retires unproductive structural search while challenge evolution and parameter learning continue. Each deployed Solver then defines the next round's challenge fitness, closing the recursive loop. On three bearing-diagnosis datasets, DuoEvo achieves the highest mean balanced accuracy among the compared methods on held-out synthetic corruptions under equal deployment-training budgets, with search costs reported separately. Fixed-reference evaluation shows bidirectional progress across all nine trajectories. Controlled analyses attribute most gains to Challenger evolution and a smaller, less certain contribution to structural adaptation. These results support a supervised self-improvement process that automates challenge discovery and model revision within a single feedback loop.