Proactive Memory Agent for Long-Horizon Tasks

Md. Shakhoyat Rahman Shujon · hf · 2026-07-11

This work focuses on behavioral state decay in long-horizon tasks: as the trajectory grows longer, the state information critical for decision-making gets scattered across the context or even pushed out of the window, causing the agent to "forget" at crucial moments.

Method: Proactive Memory Agent

Leaves the original action agent unmodified
Introduces an additional memory agent
The memory agent continuously maintains a structured memory bank
It decides when to inject a contextually relevant reminder to the action agent and when to remain silent

Experimental Conclusions

This module can be directly attached to frontier action agents and existing harnesses as a plug-and-play solution
It improves pass@1 on both Terminal-Bench 2.0 and τ^2-Bench
Benefits both weaker and stronger action agents
Ablation studies show that selective intervention outperforms passive retrieval, constant injection, advisor-only approaches, and general retrieval

Training Attempts

The authors also trained an open-weight memory policy:

Utilized SETA data
Employed SFT + GRPO
Achieved higher validation rewards with partial transfer to Terminal-Bench

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Proactive Memory Agent for Long-Horizon Tasks

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