Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents

Proactive Memory Agent reduces behavioral state decay in long-horizon tasks

Researchers introduce a plug-and-play memory agent module that runs alongside an unmodified action agent, maintaining a structured memory bank and selectively injecting reminders when relevant state would otherwise be lost in long trajectories. The approach addresses 'behavioral state decay' — the failure mode where task-critical context gets buried or pushed out of the context window. Evaluated on Terminal-Bench 2.0 and τ²-Bench, the module yields +8.3 pp and +6.8 pp pass@1 gains respectively, with ablations confirming selective injection outperforms always-on or passive retrieval approaches. The authors also train an open-weight memory policy (Qwen3.5-27B) using SFT and GRPO, showing partial transfer to Terminal-Bench.