Creating General User Models from Computer Use

• URL: http://arxiv.org/abs/2505.10831v2
• Date: Mon, 19 May 2025 05:49:47 GMT
• Title: Creating General User Models from Computer Use
• Authors: Omar Shaikh, Shardul Sapkota, Shan Rizvi, Eric Horvitz, Joon Sung Park, Diyi Yang, Michael S. Bernstein,
• Abstract summary: This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer.<n>The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences.
• Score: 62.91116265732001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-computer interaction has long imagined technology that understands us-from our preferences and habits, to the timing and purpose of our everyday actions. Yet current user models remain fragmented, narrowly tailored to specific apps, and incapable of the flexible reasoning required to fulfill these visions. This paper presents an architecture for a general user model (GUM) that learns about you by observing any interaction you have with your computer. The GUM takes as input any unstructured observation of a user (e.g., device screenshots) and constructs confidence-weighted propositions that capture user knowledge and preferences. GUMs can infer that a user is preparing for a wedding they're attending from messages with a friend. Or recognize that a user is struggling with a collaborator's feedback on a draft by observing multiple stalled edits and a switch to reading related work. GUMs introduce an architecture that infers new propositions about a user from multimodal observations, retrieves related propositions for context, and continuously revises existing propositions. To illustrate the breadth of applications that GUMs enable, we demonstrate how they augment chat-based assistants with context, manage OS notifications to selectively surface important information, and enable interactive agents that adapt to preferences across apps. We also instantiate proactive assistants (GUMBOs) that discover and execute useful suggestions on a user's behalf using their GUM. In our evaluations, we find that GUMs make calibrated and accurate inferences about users, and that assistants built on GUMs proactively identify and perform actions that users wouldn't think to request explicitly. Altogether, GUMs introduce methods that leverage multimodal models to understand unstructured context, enabling long-standing visions of HCI and entirely new interactive systems that anticipate user needs.

Related papers

• UserBench: An Interactive Gym Environment for User-Centric Agents [110.77212949007958]
  Large Language Models (LLMs)-based agents have made impressive progress in reasoning and tool use, but their ability to proactively collaborate with users remains underexplored.<n>We introduce UserBench, a user-centric benchmark designed to evaluate agents in multi-turn, preference-driven interactions.
  arXiv  Detail & Related papers  (2025-07-29T17:34:12Z)
• PersonaAgent: When Large Language Model Agents Meet Personalization at Test Time [87.99027488664282]
  PersonaAgent is a framework designed to address versatile personalization tasks.<n>It integrates a personalized memory module and a personalized action module.<n>Test-time user-preference alignment strategy ensures real-time user preference alignment.
  arXiv  Detail & Related papers  (2025-06-06T17:29:49Z)
• Know Me, Respond to Me: Benchmarking LLMs for Dynamic User Profiling and Personalized Responses at Scale [51.9706400130481]
  Large Language Models (LLMs) have emerged as personalized assistants for users across a wide range of tasks.<n> PERSONAMEM features curated user profiles with over 180 simulated user-LLM interaction histories.<n>We evaluate LLM chatbots' ability to identify the most suitable response according to the current state of the user's profile.
  arXiv  Detail & Related papers  (2025-04-19T08:16:10Z)
• Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues [54.81155589931697]
  Collaborative Instance object Navigation (CoIN) is a new task setting where the agent actively resolve uncertainties about the target instance.<n>We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA)<n>First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description.<n>An Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation.
  arXiv  Detail & Related papers  (2024-12-02T08:16:38Z)
• Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents [110.25679611755962]
  Current language model-driven agents often lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. We introduce Intention-in-Interaction (IN3), a novel benchmark designed to inspect users' implicit intentions through explicit queries. We empirically train Mistral-Interact, a powerful model that proactively assesses task vagueness, inquires user intentions, and refines them into actionable goals.
  arXiv  Detail & Related papers  (2024-02-14T14:36:30Z)
• Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs [5.06113628525842]
  We present a framework that can serve as an intermediary between a user and their user interface (UI) We employ a system that stands upon textual semantic mappings of UI components, in the form of annotations. Our engine can classify the most appropriate application, extract relevant parameters, and subsequently execute precise predictions of the user's expected actions.
  arXiv  Detail & Related papers  (2024-02-07T21:08:49Z)
• Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses [35.74453152447319]
  ExploreLLM allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM.
  arXiv  Detail & Related papers  (2023-12-01T18:31:28Z)
• GAM Coach: Towards Interactive and User-centered Algorithmic Recourse [28.137254018280576]
  We present GAM Coach, a novel open-source system that adapts integer linear programming to generate customizable counterfactual explanations for Generalized Additive Models (GAMs) A quantitative user study with 41 participants shows our tool is usable and useful, and users prefer personalized recourse plans over generic plans.
  arXiv  Detail & Related papers  (2023-02-27T21:57:42Z)
• COLA: Improving Conversational Recommender Systems by Collaborative Augmentation [9.99763097964222]
  We propose a collaborative augmentation (COLA) method to improve both item representation learning and user preference modeling. We construct an interactive user-item graph from all conversations, which augments item representations with user-aware information. To improve user preference modeling, we retrieve similar conversations from the training corpus, where the involved items and attributes that reflect the user's potential interests are used to augment the user representation.
  arXiv  Detail & Related papers  (2022-12-15T12:37:28Z)
• Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models [116.25562358482962]
  State-of-the-art neural language models can be used to solve ad-hoc language tasks without the need for supervised training. PromptIDE allows users to experiment with prompt variations, visualize prompt performance, and iteratively optimize prompts.
  arXiv  Detail & Related papers  (2022-08-16T17:17:53Z)
• Reinforced Imitative Graph Learning for Mobile User Profiling [34.62314685532468]
  We propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate. An event in which a user visits a POI will construct a new state, which helps the agent predict users' mobility more accurately.
  arXiv  Detail & Related papers  (2022-03-13T02:56:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.