Personal Information Management and User Behavior

Latest papers

• RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

  Arman Behnam, Sunglyoung Kim, Liangwei Yang · 2 October 2026

  A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the past bears on the message in front of it. Testing this requires a real person's record, and such records are private, so benchmarks generate the pe…

• Empty Commitments: When Agents Promise What Their Runtime Cannot Deliver

  Jiaqi Tang, Lan Wei, Bingyu Shen, Boyang Li · 2 October 2026

  A chatbot that says "I will remind you tomorrow" will not run again until the user writes. We call such a promise an empty commitment: a promise of an action after the current turn that nothing in the agent's tools or runtime can carry out. Unlike a broken promise, its emptiness follows from the age…

• MemFit: Efficient Long-Term Agentic Memory

  Mitchell Piehl, Muchao Ye · 2 October 2026

  Long-term memory systems for large language models (LLMs) have gained popularity for extending reasoning capabilities across applications. Current memory systems rely on LLM agents to organize and consolidate memory, resulting in costly, inefficient write operations. To address this limitation, we p…

• ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality

  Xisen Jin, Jingheng Li, Zhenglun Chen, Junyi Du, Xiang Ren · 2 October 2026

  As large language model (LLM) agents become widely adopted, they are increasingly deployed for tasks that require persistent monitoring or recurring actions (e.g., market analysis). These agents are expected to operate unattended for days or weeks, act at the right timing, and adapt to the dynamic e…

• Heavy-Tailed Memory Traces in Long-Horizon Language Agents

  Xinyuan Song, Zekun Cai · 2 October 2026

  Long-horizon language agents increasingly rely on external memory as a frozen world model, yet current memory systems are usually judged only by task success or token cost. We argue that the missing object is the shape of memory use: under finite context and repeated retrieval, agent memory can conc…

• AMU:Admission and Memory Update for Personalized Conversations---Structured Memory with SLM Guided Control

  Tao Hwang, Yishi Diao · 1 October 2026

  Large language models (LLMs) have become the foundation of personalized assistants, but maintaining persistent user memory across long-term interactions remains challenging. Existing memory systems often focus on storage, retrieval, or consolidation, while memory writing remains less controlled: tra…

• ProgressCompass: Embodied Progress Reward Models Are Lost Without the Right Context

  Jianshu Zhang, Keliang Wu, Chengxuan Qian, Xiyuan Yang, Ce Zhang, Ariel Tian, Anbang Liu, Haoran Lu, Han Liu · 1 October 2026

  Embodied agents now take on ever longer tasks. For long tasks, knowing only whether a task finally succeeds or fails says little; the steps along the way matter. Progress Reward Models (PRMs) score how far a task has come at every step, and serve as dense rewards, verifiers and monitors. Yet in long…

• Memory Consolidation Flattens the Temporal Shape of User Facts

  Sugam Panthi, Muhaiminul Yeamin, Siyan Luo, Rabab Abdelfattah · 1 October 2026

  Long-term memory systems turn conversations into short stored notes. A note can keep a user fact while losing evidence about whether the fact still holds. For example, "I am driving a Peugeot" can become "The user drives a Peugeot," which drops the cue that the activity is ongoing. We call this aspe…

• cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents

  Pranjal Aggarwal, Lawrence Keunho Jang, Sean Welleck, Daniel Fried, Ruslan Salakhutdinov, Jing Yu Koh · 1 October 2026

  Computer use agents (CUAs), which use graphical user interfaces (GUIs) to complete tasks on a computer, have recently surpassed human performance on many standard benchmarks, including difficult long-horizon tasks. Their capabilities are undoubtedly impressive, however, a key barrier to the widespre…

• Richard: Voice-First Mobile Interaction for Persistent Tasks

  Xinyang Chen · 1 October 2026

  Mobile terminals need to provide application and network services while supporting users' control over their attention. We explore voice-first interaction organized around requests and delegated tasks, allowing users to leave a conversation and later inspect, revise, and retrieve the work. We presen…

• Beyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent

  Zheyuan Zhang, Mengyuan Chao, Ke Xiao, Ziyi Chen, Daoan Zhang, Yan Zhang, Yanfang Ye, Wei Xu · 1 October 2026

  Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume that users always accurately and sufficiently communicate a fixed intent. However, this oracle communication assumption rarely holds in practice: use…

• When Context Changes: Understanding Update Failures in LLMs

  Junyu Guo, Yuchen Fang, Shangding Gu, Costas Spanos, James Demmel, Javad Lavaei · 1 October 2026

  As preferences, goals, and facts change, LLM agents must use the current state while earlier versions remain in context. Yet they can answer with an old value of the same variable, a failure that we call stale binding. To study when models use outdated information and why, we introduce Controlled In…

• Action Conditioned Bisimulation For GUI Agent Memory

  Hongbo Zhang, Liuyang Song, Quanquan Li, Daqian Yang, Yan Wen, Zhengtao Yao · 1 October 2026

  An agent that remembers what it did on a web page must decide when two pages count as the same. Memories built on observation similarity merge pages that look alike but behave differently, and GUIs are full of such pages: two tabs of one widget or two rows of one menu answer the same click different…

• LongPuzzleBench: Evaluating GUI Agents on Long-Horizon Visual Puzzles

  Bingo Zhang, Haochuan Lu, Zongjie Li, Genjian Li, Ari Yu Zhang, Chaozheng Wang · 30 September 2026

  GUI agents need long-horizon visual reasoning: they must interpret a changing interface while keeping a multi-step plan viable as earlier actions constrain later ones. Existing benchmarks evaluate grounding, computer use, and game play, but rarely test whether agents stay coherent across long chains…

• Just-In-Time Agent Memory with Runtime Agentic Research

  Bingyu Yan, Chaofan Li, Hongjin Qian, Shuqi Lu, Chaozhuo Li, Zheng Liu · 30 September 2026

  Memory is critical for AI agents. Many existing agent-memory systems follow an Ahead-of-Time (AOT) design, constructing memory before a specific request arrives. While this reduces online serving cost, such request-agnostic memory construction can discard fine-grained information that later becomes …

• Adapting Context Compression for Long-Horizon Agents with Counterfactual Continuations

  Guanghui Min, Liang Wu, Mingjia Shi, Yinhan He, Mayank Darbari, Liangjie Hong, Chen Chen · 30 September 2026

  Long-horizon agents require context compression to manage growing interaction histories. Compression quality, however, is ultimately determined by downstream execution. Existing prompt-adaptation methods infer compression errors by comparing full-context and compressed trajectories. Such comparisons…

• Mnemon: Raw Records, Fast Judgments, Slow Thoughts

  Guangren Wang · 30 September 2026

  Long-term memory lets an LLM assistant use a history it can no longer reread, and most memory systems build it by rewriting conversations into facts, graphs or typed memories at write time. We argue that the work of memory divides, as thinking does, into two systems. Most of it is fast System 1 work…

• When Successful Memories Mislead Embodied Agents:Memory Adaption For Task-Conditioned Execution

  Quanquan Li, Hongbo Zhang, Yihe Chi, Liuyang Song, Jingyu Li, Yuxiang Huang, Hongzhen Zhang, Guitao Cao · 30 September 2026

  Experience reuse can reduce repeated exploration in embodied agents, but a trajectory that succeeded previously may be unsuitable for the current execution context. Existing memory systems pri marily optimize construction and retrieval; semantic relevance and historical success therefore remain insu…

• UserProxyBench: Evaluating LLM User Simulators for Agent Benchmarks and Training

  Ashish Jain, Armaan Sandhu · 30 September 2026

  Interactive agent benchmarks and multi-turn reinforcement learning increasingly place a second language model in the role of the user. This simulated user controls what information the agent receives and when, yet current benchmarks score only the agent and do not directly measure whether the user c…

• ContextRender: From Execution Dependencies to Agent Context

  Savini Kashmira, Jayanaka L. Dantanarayana, Lingjia Tang, Jason Mars · 30 September 2026

  LLM agents performing long-horizon tasks accumulate tool results that later steps may need. Passing the full history to every invocation is costly even when it fits within the context window, while reducing it risks omitting needed information. Existing context management methods can overlook how ea…

• EnterpriseBench: Benchmarking LLM Agents on Enterprise-Level Strategic Reasoning and Decision-Making

  Min Yang, Yichen Pan, Jinghua Piao, Dandan Song, Yongshun Gong, Yong Li · 30 September 2026

  LLM agents are increasingly expected to support enterprise workflows, where tasks often involve missing information, uncertainty, feedback, and long-term trade-offs. However, existing enterprise and financial benchmarks mainly test static capabilities such as information extraction, numerical calcul…

• FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents

  Shantanu Dixit, Anson Bastos, Xuchao Zhang, Chetan Bansal, Saravan Rajmohan · 30 September 2026

  LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation from attention dilution. Existing context-compression methods learn what to discard offline: by contrastively optimizing guidelines, distilling compre…

• Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym

  Jio Oh, Seunghyun Do, Young-Jun Lee, Steven Euijong Whang, Dongyeop Kang · 30 September 2026

  Proactive LLM agents can turn idle compute into useful support before users ask. Yet even correct work can misread user context, impose review costs, or undermine trust. This work proposes foundations for designing, realizing, and evaluating proactive LLM agents around three joint principles (3T): T…

• Asking for What Was Never Requested: Horizontal and Vertical Proactivity in Agents

  Ido Levy, Asaf Yehudai, Segev Shlomov, Asaf Adi, Leshem Choshen · 30 September 2026

  An agent that uses tools typically responds to what the user explicitly asks, yet completing the task may require information the user never requested. Work on proactive agents mainly studies whether and when an agent should act on its own, not what information it should pursue. We study a distinct …

• Absorbed in Inertia: Activation Analysis for Computer-Use Agents

  Giulio Segalini, Zhi Wen Soi, J\'er\'emie Decouchant, Lydia Chen · 30 September 2026

  Computer-use agents have become increasingly capable of executing tasks on live desktops through natural-language instructions, based on trajectories of screenshots, actions, and reasoning. We discover that they can stealthily exhibit inertia, in which they repeat fruitless actions despite recognizi…