# Diffusion Models Are Real-Time Game Engines

Authors: Dani Valevski, Yaniv Leviathan, Moab Arar, Shlomi Fruchter.

Presents GameNGen, a diffusion model that interactively simulates DOOM from previous frames and player actions.

First public: 2024-08-27.
ArXiv initial submission: 2024-08-27.

[Original paper](https://arxiv.org/abs/2408.14837) · [Reading-list entry](https://neural-os.com/reading-list/papers/2408.14837/)
[Preprint BibTeX](https://neural-os.com/reading-list/papers/2408.14837/citation.bib) · [Preprint CSL-JSON](https://neural-os.com/reading-list/papers/2408.14837/citation.csl.json) · [JSON record](https://neural-os.com/reading-list/papers/2408.14837/record.json)

**Contribution:** Uses agent-collected gameplay, conditioning augmentation, and decoder fine-tuning to sustain autoregressive visual interaction.

**Model output:** The next rendered game frame.

**Action interface:** DOOM player actions.

**State and memory:** Conditioning on a sequence of recent frames and actions; continued play feeds generated frames back into the model.

**Relationship to NeuralOS:** Interactive-simulation background cited by NeuralOS; NeuralOS also refers to its perceptual-evaluation setup.

**Scope and limitations:** The simulator is trained and evaluated on DOOM.

[Relationship source](https://arxiv.org/html/2507.08800v2)

Related terms: GameNGen, neural game engine, real-time simulation, diffusion renderer.

## Approaches

- [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.

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