arXiv AI
Oct 21

ProMemAgent: Long-Horizon Proactive Assistance with Context-Aware Prospective Memory from Sensory Perceptions

ProMemAgent is a proactive agent that uses prospective memory to handle delayed user intentions over long horizons. It organizes past sensory perceptions into context-aware memory, generates prospective cues, and links earlier events to future developments for retrieval. The authors also introduce CPMBench, a benchmark of 1,000 instances across eight everyday categories, and show that ProMemAgent outperforms baselines in memory retrieval and proactive assistance tasks.

By Lilin Xu, Linyang He, Kaiyuan Hou, Yuang Fan, Minghui Zhao, Xuhai Xu, Xiaofan Jiang
arXiv AI
Oct 28

Proactive Memory for Event-Driven LLM Agents

Proactive Memory for Event-Driven LLM Agents introduces ProactMem, a system that organizes an agent’s history into recent events, temporal trajectories, and behavioral regularities. It validates trajectory facts against source events and assesses regularities through historical replay before matching them to current conditions. The authors also present ProactiveStream, a longitudinal benchmark with three application scopes, and report that ProactMem achieves an overall F1 of 52.6%, a 21.4% relative improvement over the strongest of five external baselines.

By Jinyang Li, Zichuan Fu, Xian Wu, Wenlin Zhang, Yimin Deng, Kaifeng Guo, Guojing Li, Junjia QI, Xiangyu Zhao
Hugging Face Trending Papers
Jul 9

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

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.