AdaFleet: An Adaptive Orchestrator–Subagent Coevolution Framework for Efficient Quantitative Signal Discovery

Self-improving multi-agent systems must balance task execution against changes to the prompts, policies and parameters that guide their work. In continual discovery, this choice is further complicated by a changing acceptance criterion, as each successful discovery alters which future candidates are admissible. We propose AdaFleet, an adaptive orchestrator–subagent coevolution framework that connects reflective prompt revision, dispatch-rule revision and joint fine-tuning through a shared discovery history. A pre-specified feedback rule triggers prompt revision and fine-tuning, while adoption tests compare proposed changes with incumbents. We instantiate the framework in quantitative signal discovery within an evaluation world whose six admission checks account for standalone quality and the growing pool of accepted signals. Under matched evaluator-call budgets, AdaFleet with a 4B backbone admits 6.2 times as many signals as an adapted AlphaSAGE baseline and 3.7 times as many as our re-implementation of NVIDIA's discovery pipeline. The full framework also achieves higher average admissions than adaptive dispatch across five seeds. These findings support coordinated adaptation for continual discovery under changing acceptance constraints.