{
  "id": "2507.08800",
  "title": "NeuralOS: Towards Simulating Operating Systems via Neural Generative Models",
  "authors": "Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng",
  "first_public": "2025-07-11",
  "first_public_source": "https://arxiv.org/abs/2507.08800",
  "first_public_note": "Initial arXiv submission on July 11, 2025; revised on March 12, 2026 and published at ICLR 2026.",
  "venue": "ICLR 2026",
  "arxiv": "2507.08800",
  "paper_url": "https://arxiv.org/abs/2507.08800",
  "original_url": "https://arxiv.org/html/2507.08800v1",
  "revised_url": "https://arxiv.org/html/2507.08800v2",
  "code_url": "https://github.com/yuntian-group/neural-os",
  "lens": [
    "neural-os",
    "interactive-worlds",
    "evaluation"
  ],
  "related_terms": [
    "NeuralOS",
    "neural operating system",
    "learned computer simulator",
    "generative interface",
    "GUI world model",
    "desktop world model",
    "action-conditioned screen generation"
  ],
  "summary": "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.",
  "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.",
  "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.",
  "model_output": "Autoregressively generated desktop screen frames.",
  "action_interface": "Cursor coordinates, left/right clicks, and keyboard press/release events.",
  "state_memory": "A hierarchical recurrent model carries state across frames and conditions a diffusion renderer.",
  "relationship": "Introduces NeuralOS and its formulation of learned OS-interface simulation.",
  "relationship_source": "https://arxiv.org/html/2507.08800v1",
  "author_names": [
    "Luke Rivard",
    "Sun Sun",
    "Hongyu Guo",
    "Wenhu Chen",
    "Yuntian Deng"
  ],
  "record_url": "https://neural-os.com/reading-list/papers/2507.08800/",
  "legacy_record_url": "https://neural-os.com/reading-list/papers/#paper-2507-08800",
  "citation_date": "2025-07-11",
  "json_url": "https://neural-os.com/reading-list/papers/2507.08800/record.json",
  "markdown_url": "https://neural-os.com/reading-list/papers/2507.08800/index.md",
  "bibtex_url": "https://neural-os.com/reading-list/papers/2507.08800/citation.bib",
  "csl_url": "https://neural-os.com/reading-list/papers/2507.08800/citation.csl.json",
  "publication": {
    "title": "NeuralOS: Towards Simulating Operating Systems via Neural Generative Models",
    "author_names": [
      "Luke Rivard",
      "Sun Sun",
      "Hongyu Guo",
      "Wenhu Chen",
      "Yuntian Deng"
    ],
    "year": 2026,
    "type": "paper-conference",
    "container_title": "International Conference on Learning Representations",
    "url": "https://proceedings.iclr.cc/paper_files/paper/2026/hash/2174a6749c3f769358cd21ade1221b34-Abstract-Conference.html",
    "source_url": "https://proceedings.iclr.cc/paper_files/paper/2026/hash/2174a6749c3f769358cd21ade1221b34-Abstract-Conference.html",
    "verified_at": "2026-10-19"
  },
  "publication_bibtex_url": "https://neural-os.com/reading-list/papers/2507.08800/publication.bib",
  "publication_csl_url": "https://neural-os.com/reading-list/papers/2507.08800/publication.csl.json",
  "method_comparisons": [
    {
      "title": "A game world model evaluated on desktop interactions",
      "text": "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.",
      "source_ids": [
        "2405.12399",
        "2507.08800"
      ],
      "source_urls": [
        "https://arxiv.org/abs/2405.12399",
        "https://arxiv.org/html/2507.08800v2#A1"
      ],
      "source_labels": [
        "DIAMOND paper",
        "NeuralOS Appendix A"
      ],
      "topic_id": "interactive-worlds",
      "topic_label": "Interactive learned environments",
      "topic_url": "https://neural-os.com/reading-list/guides/games-to-computers/"
    },
    {
      "title": "Direct frame generation and renderable-code prediction",
      "text": "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.",
      "source_ids": [
        "2507.08800",
        "2602.09856"
      ],
      "source_urls": [
        "https://arxiv.org/html/2507.08800v1",
        "https://arxiv.org/html/2602.09856v1#S2.SS2"
      ],
      "source_labels": [
        "Original NeuralOS paper",
        "Code2World Section 2.2"
      ],
      "topic_id": "gui-planning",
      "topic_label": "GUI prediction and planning",
      "topic_url": "https://neural-os.com/reading-list/guides/games-to-computers/#gui-prediction-and-planning"
    }
  ]
}