This work demonstrates a practical approach to building AI agents that improve through real-world use—by coupling task strategy refinement with model training, ScienceBuddy shows how interactive AI can evolve alongside the research it supports rather than remaining static.
ScienceBuddy is an AI research assistant that improves itself through a two-level feedback loop: it refines how it approaches tasks (harness evolution) while simultaneously training its underlying model on researcher feedback. By working directly in researchers' workflows, it learns from real scientific work and continuously adapts to become more useful.