arXiv cs.AI· Jinyang Li, Zichuan Fu, Xian Wu, Wenlin Zhang, Yimin Deng, Kaifeng Guo, Guojing Li, Junjia QI, Xiangyu Zhao·· 3 小时前AI 评分41
ProactMem：面向事件驱动 LLM 智能体的主动记忆框架
Proactive Memory for Event-Driven LLM Agents
AI 导读
ProactMem 提出面向主动式 LLM 智能体的主动记忆框架，将历史组织为近期事件、时间轨迹与行为规律，并通过历史重放验证规律后再匹配当前事件。配套构建了覆盖三个应用场景的纵向基准 ProactiveStream，ProactMem 在其上整体 F1 达 52.6%，较五个外部基线中最强者相对提升 21.4%。代码已开源。
正文 · 原文
Abstract:Proactive LLM agents anticipate service opportunities from ongoing observations and accumulated history. Existing approaches emphasize recent context or retrieval of related memories, providing limited support for validating recurring behavioral regularities and assessing their applicability to the current event. We propose Proactive Memory (ProactMem), which organizes history into recent events, temporal trajectories, and behavioral regularities. Trajectory facts are checked against source events, and regularities are assessed through historical replay before being matched to current conditions. We also construct ProactiveStream, a longitudinal benchmark covering three application scopes, where agents process isolated event streams chronologically under episode-aware opportunity labels. On ProactiveStream, ProactMem achieves an overall effectiveness (F1) of 52.6%, a 21.4% relative improvement over the strongest of five external baselines. Code is available at: this https URL.
| Comments: | 15 pages |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36967 [cs.AI] |
| (or arXiv:2610.36967v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36967 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jinyang Li [view email]
[v1]
Tue, 27 Oct 2026 10:23:50 UTC (499 KB)
来源：arXiv cs.AI · arxiv.org