The paper introduces MemPABench, an interactive environment designed to evaluate how personal agents remember and act on user interaction preferences. The benchmark features long‑horizon tasks where agents must decide when to clarify, confirm, or act autonomously, using data from four simulated users who provided 99 sessions over four months and five preference changes each. Experiments across four memory systems and LLM backbones show that current memory approaches offer limited improvement in preference selection, with retrieval‑based methods performing no better than no memory and file‑based memory sometimes applying preferences in irrelevant contexts.
By Jialin Li, Zhenhao Chen, Hanjun Luo, Hanan Salam
arXiv:2607. 20482v1 Announce Type: new Abstract: Recent advances in large language models have enabled web agents to autonomously execute complex tasks.
By Seungbin Yang, Chaewoon Ki, Dohyun Lee, Jaegul Choo, ChaeHun Park
arXiv:2608. 10042v1 Announce Type: cross Abstract: Tool-use LLMs are increasingly asked to act on users' behalf, but existing benchmarks usually focus on profile recall, style imitation, generic tool use, or response-level personalization.
By Xuexiong Yin, Zechuan Chen, Yongsen Zheng, Yuxiang Zhang, Jingyuan Yang, Bin Wang, Yubin Wang, Keze Wang
arXiv:2609.36488v1 Announce Type: new
Abstract: Large language model (LLM) agents have demonstrated strong performance on complex web navigation tasks, yet they remain brittle in real-world settings...
By Dongchan Shin, Xing Han L\`u, Jiaqi Deng, Jay Gala, Tom\'as Vergara Browne, Jaewon Moon, Fengyuan Liu, Alexandre Drouin, Siva Reddy, Alexandre Lacoste
MemoryArena is a new evaluation gym that benchmarks agent memory in interdependent multi‑session tasks. Unlike prior benchmarks that test memorization or single‑session action in isolation, MemoryArena requires agents to acquire memory while interacting with the environment and then use that memory to guide future decisions across a range of tasks such as web navigation, planning, information search, and formal reasoning. The benchmark reveals that agents excelling on existing long‑context memory tests perform poorly here, highlighting a gap in current memory evaluation methods.
By Zexue He, Yu Wang, Churan Zhi, Yuanzhe Hu, Tzu-Ping Chen, Lang Yin, Ze Chen, Tong Arthur Wu, Siru Ouyang, Zihan Wang, Jiaxin Pei, Julian McAuley, Yejin Choi, Alex Pentland
arXiv:2603.04191v2 Announce Type: replace
Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
By Qianyun Guo, Yibo Li, Yue Liu, Bryan Hooi