"Scaling Discovery Requires Forgetting" Long-running AI research agents plateau as their context accumulates. It also becomes more conservative and make smaller changes instead of exploring new ideas. So this paper proposes forgetting, where you have the model periodically discarding all agent sessions and experiment histories, keeping only the best solution found so far. A fresh team of agents then resumes exploring from that solution. Across four coding and optimization tasks, this…