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

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

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computation and Language
21h ago

MemoProAgent: Toward Memory-Driven Proactive Assistance

arXiv:2610.22671v2 Announce Type: replace Abstract: As assistants move from reactive answering toward proactive intervention, memory changes role: it no longer only supplies the answer to a query, it...

By Shenglai Zeng, Xiao Yang, Tiffany Ma, Xinyuan Zhang, Nanshu Wang, Deren Lei, Kai Sun, Xilun Chen, Tammy Stark, Xianhui Zhu, Yuetai Li, Rulin Shao, Tianxin Wei, Rakesh Wanga, Anuj Kumar, Jiliang Tang, Wen-tau Yih, Xin Luna Dong
arXiv Computation and Language
3d ago

ProPerAgent: Toward Memory-Driven Proactive Assistance

The paper introduces ProPerMem, a benchmark for memory-driven proactive assistance that pairs egocentric video with long, distractor-rich personal memories and human-verified trigger windows. Baseline models perform poorly, achieving at most 15% accuracy, while an oracle-driven GPT-5.3-Chat model reaches 96% when given evidence inside the trigger window. Building on these insights, the authors develop ProPerAgent, a plug‑and‑play system that retrieves relevant memory from a graph store and uses lightweight reinforcement learning to improve proactive intervention accuracy to 42.4% without training.

By Shenglai Zeng, Xiao Yang, Tiffany Ma, Xinyuan Zhang, Nanshu Wang, Deren Lei, Kai Sun, Xilun Chen, Tammy Stark, Xianhui Zhu, Yuetai Li, Rulin Shao, Tianxin Wei, Rakesh Wanga, Anuj Kumar, Jiliang Tang, Wen-tau Yih, Xin Luna Dong