Active memory management—deciding *when* to remind an agent about past information—outperforms passive retrieval and improves long-horizon task performance by 6-8% across benchmarks.
This paper tackles 'behavioral state decay' in long-horizon tasks—where important information gets buried in expanding context windows. Instead of passive memory retrieval, the authors propose a separate memory agent that actively monitors trajectories, maintains a structured memory bank, and selectively injects relevant reminders into an action agent's decision-making.