The paper introduces Active Preference Acquisition (APA), a method for web agents to decide which unknown user preferences to ask about by evaluating the Prospective Personalization Value (PPV)—the expected cumulative benefit across future tasks minus the cost of asking. APA calculates counterfactual gains for each candidate preference using task prototypes and aggregates them by expected reuse, enabling agents to prioritize questions that may not help immediately but improve long‑term performance. Experiments on the ActiveWebPref benchmark show APA can acquire a large share of valuable preferences and achieve higher net utility than baseline strategies, demonstrating that considering future reuse is crucial for effective personalization.
By Yiqi Wang, Zizhi Zeng, Jiaqi Zhang, Yiqun Duan, Mingkai Zheng, Taotao Cai
The paper introduces Just-in-Time Memory (JitMem), a system that defers memory curation until a task is read, allowing a curator to synthesize task‑specific memory payloads based on the current query. Unlike traditional write‑time curation, JitMem retains raw trajectories and trains the curator using immediate task success, avoiding long‑horizon credit‑assignment issues. Experiments on ALFWorld, WebShop, and τ²‑bench show JitMem consistently outperforms both no‑memory agents and existing write‑time memory methods, with improvements of up to 16.3 absolute success‑rate points.
whyItMatters":"By curating memory at read time, JitMem enables more effective, task‑adaptive recall that directly improves agent performance across diverse benchmarks."
By Yefan Zhou, Yang Li, Zeyu Leo Liu, Semih Yavuz, Shafiq Joty
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
The paper introduces ATRBench, a benchmark that measures the proactivity gap in long‑lived LLM agents by evaluating their ability to ask for user preferences that are not needed immediately but may be useful in future sessions. It defines the Ask‑to‑Remember (ATR) task, where agents must decide whether to request a reusable preference now, and shows that current state‑of‑the‑art agents perform significantly below an oracle. The study identifies preference acquisition as the main bottleneck and provides a diagnostic framework for improving agent proactivity.
By Bin Wu, Guanyun Zou, Bingbing Wang, Huan Zhao, Chuan Shi
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
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