Can an agent that "improves itself" really learn new things without forgetting the old? Title: Are Self-Evolving Agents Continual Learners? URL: ❓ What did they study? Self-evolving agents that keep model weights frozen and rewrite the harness (prompts, memory, skills): ReasoningBank, GEPA, and Meta-Harness, tested on task streams where topic or workflow shifts midway. 💡 What happened? Performance rose in most settings (GEPA gained +3.7pp at the end on average). But the more adaptable a method was, the less stable it was. In all 12 method-stream pairs, the most plastic method was less stable than the most conservative one. ❓ Why does it forget? Methods that rewrite a shared prompt or workspace make global edits that interfere with unseen tasks. GEPA condensed seven BIRD sections into one and BIRD-F1's gain fell back to 0.0. ❓ Is memory retrieval the fix? It localizes updates, but not perfectly. Before a group's block it always retrieves a foreign memory, and for two similar workflows it pulled the wrong memory in 52% of episodes, cutting accuracy by 8.0pp. #AIAgents# #ContinualLearning#