ScienceBuddy introduces a recursive-in-recursive self-improvement paradigm for interactive scientific agents, combining evolution and model reinforcement learning to enable continual learning and harness adaptation. This system supports researchers in their everyday workflows, transforming requests, feedback, and execution evidence into tasks and evaluation rubrics.