This paper identifies that long-running AI research agents get stuck in "idea basins"—localized regions of solution space—causing independent runs to plateau at different performance levels. The authors propose fork-and-flush, a method that periodically branches the agent into parallel trajectories with fresh chat contexts while preserving accumulated work, then continues from the best performer, achieving 66% relative improvement over single-run baselines across 13 complex research tasks.

• Independent runs of the same agent on identical tasks plateau at substantially different scores, with performance gaps persisting even after significant additional compute, suggesting agents explore only localized regions of the solution space rather than broadly searching.
• Fork-and-flush works by forking the agent into K parallel trajectories at episode boundaries, clearing the chat history while keeping the workspace intact, running each for a fixed horizon, then continuing from the highest-scoring branch—this simple intervention requires no architectural changes.
• Across 13 long-horizon tasks (individual runs up to 107 hours), fork-and-flush achieved 66% relative improvement over single-run and 44.4% over best-of-N baselines under equal compute budgets, with artifact embeddings confirming broader exploration through solution space.