Branch-reset: Escaping Idea Basins in Autoresearch Agents

Autoresearch agents tackle open-ended problems by repeatedly proposing candidate solutions, evaluating them, and using feedback to guide subsequent experiments. We show that independent runs of the same agent on the same task often plateau at substantially different scores, with gaps that persist even after considerable additional compute. Embedding their candidate artifacts by functional similarity provides further evidence that trajectories remain in localized regions of the solution space, which we call idea basins. To help agents escape these basins, we study a simple periodic intervention, branch-reset. It forks the agent into parallel trajectories that each inherit a copy of the accumulated workspace but begin with fresh chat contexts. After running each trajectory for a fixed horizon, the agent continues from the highest-scoring one. Across $13$ long-horizon research and engineering tasks, with individual agent runs lasting up to several days, branch-reset outperformed the single-run and best-of-$N$ baselines by a relative improvement of $66.0$% and $44.4$%, respectively, on the min–max normalized average score under an equal compute budget.