arXiv cs.CL· Yuanchen Bei, Zhining Liu, Tianxin Wei, Weizhi Zhang, Zhichen Zeng, Philip S. Yu, Hendrik Hamann, Jingrui He, Hanghang Tong·· 11 小时前AI 评分34
Pro-Memory：为主动式 LLM 智能体引入前瞻记忆
Prospective Memory for Proactive LLM Agents
AI 导读
研究者提出 Pro-Memory 记忆框架，将“保留未来意图”与“决定何时执行”分离，让主动式 LLM 智能体在合适时机实现待办意图而非一有相关线索就行动。该框架把待办目标与其支撑证据关联，用原始目标和激活条件评估新观察，并根据实际执行或问题解决的证据更新意图状态，另设经验记忆将已完成任务的反馈蒸馏为后续决策指导。
正文 · 原文
Abstract:Most memory systems for LLM agents are retrospective, preserving and retrieving past information to inform current decisions. Proactive agents, however, also require prospective memory: retaining future-directed intentions and realizing them when appropriate opportunities arise, even without explicit requests. Crucially, remembering the right intention does not guarantee acting at the right time: a related observation may call for waiting, while a later cue may make the same intention actionable. Inspired by cognitive theories of human prospective memory, we introduce Pro-Memory, a memory framework that separates maintaining future intentions from deciding when and how to realize them. Pro-Memory preserves pending objectives linked to their supporting evidence, assesses new observations against their original goals and activation conditions, and coordinates appropriate contributions with the agent's ongoing actions. It further updates intention states based on actions actually issued or evidence of resolution, while an experience memory distills feedback from completed episodes into guidance for future decisions. Experiments on two benchmarks spanning delayed-intention tasks and open-ended proactive assistance demonstrate consistent improvements in proactive task performance over representative memory approaches across multiple LLM backbones, while limiting unnecessary interventions.
| Comments: | 17 pages, 8 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.05731 [cs.CL] |
| (or arXiv:2611.05731v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05731 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuanchen Bei [view email]
[v1]
Tue, 3 Nov 2026 23:03:06 UTC (511 KB)
来源：arXiv cs.CL · arxiv.org