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Source note2 min read

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

Source

arXiv

arxiv.org/abs/2607.08716v1

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Source

Plain-Language Brief

Researchers introduce a proactive memory agent for long-horizon tasks, aiming to improve decision-making in scenarios where relevant state information can become buried over time. The memory agent updates a structured memory bank and decides when to intervene with memory-grounded reminders. The study shows that the memory agent improves performance on two benchmark tasks and outperforms other approaches.

What the Document Says

  • The paper presents a proactive memory agent for long-horizon tasks.
  • The memory agent updates a structured memory bank from the recent trajectory.
  • The memory agent decides whether to inject a reminder or remain silent.
  • The memory agent improves pass@1 on Terminal-Bench 2.0 and $τ^2$-Bench by +8.3 pp and +6.8 pp respectively.
  • Ablations show that selective intervention outperforms other approaches such as passive bank exposure, always-on injection, advisor-only guidance, and general retrieval.
  • The researchers also train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.

Key Concepts Mentioned

  • proactive memory agent
  • long-horizon tasks
  • memory bank
  • reminder injection
  • Terminal-Bench 2.0
  • $τ^2$-Bench, ablations, Qwen3.5-27B, SETA, SFT, GRPO

Claims Made by the Document

  • The proactive memory agent improves performance on long-horizon tasks.
  • Selective intervention outperforms other approaches.

Evidence Used

  • Improvements in pass@1 on Terminal-Bench and $τ^2$-Bench.
  • Results from ablations showing the superiority of selective intervention.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

In long-horizon tasks, decision-relevant state is often scattered across an expanding trajectory, while the action agent must surface it and act. As trajectories grow, task requirements, environment facts, prior attempts, diagnoses, and open subgoals can be buried in the context window or pushed beyond it, failing to influence decisions when needed. We call this failure mode 'behavioral state decay'. We study memory as an active intervention mechanism rather than passive retrieval. A separate memory agent runs alongside an unmodified action agent, updating a structured memory bank from the recent trajectory and deciding whether to inject a memory-grounded reminder or remain silent. The module is plug-and-play with frontier action agents and existing agent harnesses. Across Terminal-Bench 2.0 and $τ^2$-Bench, it improves pass@1 for both weaker and stronger action agents, with gains of +8.3 pp on Terminal-Bench and +6.8 pp on $τ^2$-Bench. Ablations show that selective intervention outperforms passive bank exposure, always-on injection, advisor-only guidance, and general retrieval. As an early step toward open-weight memory policies, we train Qwen3.5-27B on SETA using SFT and GRPO, improving validation reward and achieving partial transfer to Terminal-Bench.