# NeuralOS: Towards Simulating Operating Systems via Neural Generative Models

Authors: Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng.

Studies neural operating systems through learned, interactive GUI simulation: a model produces desktop screens in response to mouse and keyboard events, opening a path toward generative computer interfaces.

First public: 2025-07-11.
ArXiv initial submission: 2025-07-11.

[Original paper](https://arxiv.org/abs/2507.08800) · [Reading-list entry](https://neural-os.com/reading-list/papers/2507.08800/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/2507.08800/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/2507.08800/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/2507.08800/record.json)

**Contribution:** Formalizes OS-interface generation and develops state tracking, precise cursor conditioning, staged training, and data collection. The 2026 revision also demonstrates learning a new simulated application from synthesized training data.

**Model output:** Autoregressively generated desktop screen frames.

**Action interface:** Cursor coordinates, left/right clicks, and keyboard press/release events.

**State and memory:** A hierarchical recurrent model carries state across frames and conditions a diffusion renderer.

**Relationship to NeuralOS:** Introduces NeuralOS and its formulation of learned OS-interface simulation.

**Scope and limitations:** Fine-grained typing and long-horizon state retention remain difficult. The simulated interface does not provide the underlying filesystem, processes, or application services of a conventional OS.

[Relationship source](https://arxiv.org/html/2507.08800v1)

Dated versions: [Original version](https://arxiv.org/html/2507.08800v1) · [Revised version](https://arxiv.org/html/2507.08800v2)
[Code](https://github.com/yuntian-group/neural-os)

Related terms: NeuralOS, neural operating system, learned computer simulator, generative interface, GUI world model, desktop world model, action-conditioned screen generation.

## Method comparisons

**A game world model evaluated on desktop interactions**

DIAMOND learns visual game dynamics with a diffusion model. NeuralOS adapts DIAMOND to OS screen prediction as a baseline in Appendix A of its 2026 revision. NeuralOS adds a recurrent state representation and spatial cursor conditioning for desktop interaction.

Sources: [DIAMOND paper](https://arxiv.org/abs/2405.12399) · [NeuralOS Appendix A](https://arxiv.org/html/2507.08800v2#A1)

Paper details: [Diffusion for World Modeling: Visual Details Matter in Atari](https://neural-os.com/reading-list/papers/2405.12399/) · [NeuralOS: Towards Simulating Operating Systems via Neural Generative Models](https://neural-os.com/reading-list/papers/2507.08800/)

**Direct frame generation and renderable-code prediction**

NeuralOS predicts desktop frames using learned state and a diffusion renderer. Code2World predicts a UI through code that is then rendered into a screen, and tests its usefulness for mobile navigation. Code2World cites NeuralOS in Section 2.2.

Sources: [Original NeuralOS paper](https://arxiv.org/html/2507.08800v1) · [Code2World Section 2.2](https://arxiv.org/html/2602.09856v1#S2.SS2)

Paper details: [NeuralOS: Towards Simulating Operating Systems via Neural Generative Models](https://neural-os.com/reading-list/papers/2507.08800/) · [Code2World: A GUI World Model via Renderable Code Generation](https://neural-os.com/reading-list/papers/2602.09856/)


Date note: Initial arXiv submission on July 11, 2025; revised on March 12, 2026 and published at ICLR 2026. [Source](https://arxiv.org/abs/2507.08800).

## Published version

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models. Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng. International Conference on Learning Representations, 2026.
[Publication](https://proceedings.iclr.cc/paper_files/paper/2026/hash/2174a6749c3f769358cd21ade1221b34-Abstract-Conference.html) · [BibTeX](https://neural-os.com/reading-list/papers/2507.08800/publication.bib) · [CSL-JSON](https://neural-os.com/reading-list/papers/2507.08800/publication.csl.json)

## Approaches

- [Neural operating systems](https://neural-os.com/reading-list/guides/neural-operating-systems/): Study how learned generative models can supply computer interaction, interface behavior, and adaptable user experiences. The research direction includes conceptual proposals and learned interface simulators.
- [Interactive learned environments](https://neural-os.com/reading-list/guides/games-to-computers/): Learn how an environment changes after actions, then generate observations during continued interaction. Compare visual fidelity, control interfaces, memory, and the reliability of repeated predictions.
- [Environments and evaluation](https://neural-os.com/reading-list/guides/evaluation/): Test action response, visual quality, remembered state, and completed tasks separately. Real desktop and mobile environments provide execution-based checks that complement learned-simulator measurements.

Reading-list version 1.0.1; literature checked through 2026-10-19.