Harnessing Agentic Evolution: Meta-Learning Framework for Self-Improving Agent Pipelines

arXiv·medium signal

Paper addresses the gap between rigid hand-designed evolutionary procedures and flexible but drifty general-purpose agents. Proposes a framework that iteratively generates candidates, evaluates them, and uses accumulated evidence (including candidates, scores, failures) to guide future search. Solves the credit-assignment problem of which intermediate changes drove improvement. Directly relevant to anyone building self-improving pipelines or autonomous optimization loops.