Researchers grow a hypothesis tree for AI coding agents

AI coding agents can tend to isolate research, running experiments and generating ideas that are then forgotten when context windows reset. This can waste tokens, as models then repeat the same mistakes and hit the same dead ends. But new research argues that it’s not the model itself, but the overarching ‘tree,’ that needs tweaking.