# Computer-Using World Model

arXiv: https://arxiv.org/abs/2602.17365 · HTML: https://mlsanity.com/paper/2602.17365/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Feb 2026.

## Built on by (most cited first, 16 of 16)

- [Qwen-AgentWorld: Language World Models for General Agents](https://mlsanity.com/paper/2606.24597/) (2026)
- [Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond](https://mlsanity.com/paper/2604.22748/) (2026)
- [How Mobile World Model Guides GUI Agents?](https://mlsanity.com/paper/2605.10347/) (2026)
- [CUA-Suite: Massive Human-annotated Video Demonstrations for Computer-Use Agents](https://mlsanity.com/paper/2603.24440/) (2026)
- [Beyond State Consistency: Behavior Consistency in Text-Based World Models](https://mlsanity.com/paper/2604.13824/) (2026)
- [Scaling GUI Agents with Visual State Transitions](https://mlsanity.com/paper/2607.24112/) (2026)
- [Towards World Models in Biomedical Research](https://mlsanity.com/paper/2606.05925/) (2026)
- [AppDeltaWorld: Transition-Grounded Delta Code World Model for Mobile GUI Agents](https://mlsanity.com/paper/2608.05891/) (2026)
- [The Next Screenshot Knows: Gated Hindsight Distillation for Mobile GUI Agents](https://mlsanity.com/paper/2608.06065/) (2026)
- [GUI-CC: Benchmarking Contextual Consistency of GUI World Models as Agent Environments](https://mlsanity.com/paper/2609.00048/) (2026)
- [IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents](https://mlsanity.com/paper/2604.05157/) (2026)
- [VisCritic: Visual State Comparison as Process Reward for GUI Agents](https://mlsanity.com/paper/2606.24525/) (2026)
- [Desktop-Delta Bench: Do Computer-Use Models Understand Desktop GUI Transitions?](https://mlsanity.com/paper/2607.26041/) (2026)
- [Quo Vadis, World Modeling?](https://mlsanity.com/paper/2608.02713/) (2026)
- [Solaris: Towards Interfaces That Are Generated, Not Coded](https://mlsanity.com/paper/2609.00776/) (2026)
- [AutoGUIWorld: Image Generators as Visual World Models for GUI Agent](https://mlsanity.com/paper/2610.01215/) (2026)

## Abstract (arXiv)

Agents operating in complex software environments benefit from reasoning about the consequences of their actions, as even a single incorrect user interface (UI) operation can derail long, artifact-preserving workflows. This challenge is particularly acute for computer-using scenarios, where real execution does not support counterfactual exploration, making large-scale trial-and-error learning and planning impractical despite the environment being fully digital and deterministic. We introduce the Computer-Using World Model (CUWM), a world model for desktop software that predicts the next UI state given the current state and a candidate action. CUWM adopts a two-stage factorization of UI dynamics: it first predicts a textual description of agent-relevant state changes, and then realizes these changes visually to synthesize the next screenshot. CUWM is trained on offline UI transitions collected from agents interacting with real Microsoft Office applications, and further refined with a lightweight reinforcement learning stage that aligns textual transition predictions with the structural requirements of computer-using environments. We evaluate CUWM via test-time action search, where a frozen agent uses the world model to simulate and compare candidate actions before execution. Across a range of Office tasks, world-model-guided test-time scaling improves decision quality and execution robustness.