Microsoft Research's 'Fork-and-Flush' method reduces idea basins and improves long-running AI agents
Microsoft Research shows that growing chat context can trap autonomous 'autoresearch' agents in suboptimal 'idea basins' during long-running tasks. Their Fork-and-Flush approach forks agents into parallel branches that inherit workspaces but flush chat history; across 13 tasks (using GPT-5.6-terra and GPT-5.5 via GitHub Copilot CLI) it raised the min-max-normalized average score to 0.78 versus 0.47 for single runs, improving consistency as well.