# The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users

arXiv: https://arxiv.org/abs/1301.7385 · HTML: https://mlsanity.com/paper/1301.7385/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2013.

## Built on by (most cited first, 11 of 11)

- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [ProAgent: Harnessing On-Demand Sensory Contexts for Proactive LLM Agent Systems in the Wild](https://mlsanity.com/paper/2512.06721/) (2025)
- [LLAMAPIE: Proactive In-Ear Conversation Assistants](https://mlsanity.com/paper/2505.04066/) (2025)
- [Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates](https://mlsanity.com/paper/2506.22276/) (2025)
- [GUIDE: A Benchmark for Understanding and Assisting Users in Open-Ended GUI Tasks](https://mlsanity.com/paper/2603.25864/) (2026)
- [Prototyping Multimodal GenAI Real-Time Agents with Counterfactual Replays and Hybrid Wizard-of-Oz](https://mlsanity.com/paper/2510.06872/) (2025)
- [Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories](https://mlsanity.com/paper/2308.01732/) (2023)
- [Enabling a Social Robot to Process Social Cues to Detect when to Help a User](https://mlsanity.com/paper/2110.11075/) (2021)
- [Differentiable User Models](https://mlsanity.com/paper/2211.16277/) (2022)
- ["What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use](https://mlsanity.com/paper/2605.00497/) (2026)

## Abstract (arXiv)

The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be employed to infer a users needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for the Office Assistant in the Microsoft Office '97 suite of productivity applications.