{ "metadata": { "version": "1.0.1", "updated_at": "2026-10-19", "search_refreshed_at": "2026-10-19", "topic_id": "neural-os", "name": "Neural operating systems", "definition": "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.", "record_count": 2, "topic_url": "https://neural-os.com/reading-list/guides/neural-operating-systems/", "source_url": "https://neural-os.com/reading-list/data/papers.json" }, "comparisons": [ ], "demos": [ ], "papers": [ { "id": "2310.04875", "title": "Prompt-to-OS (P2OS): Revolutionizing Operating Systems and Human-Computer Interaction with Integrated AI Generative Models", "authors": "Gabriele Tolomei, Cesare Campagnano, Fabrizio Silvestri, Giovanni Trappolini", "first_public": "2023-10-07", "first_public_source": "https://arxiv.org/abs/2310.04875", "venue": "IEEE CogMI 2023", "arxiv": "2310.04875", "paper_url": "https://arxiv.org/abs/2310.04875", "lens": [ "neural-os" ], "related_terms": [ "Prompt-to-OS", "generative interface", "neural operating system", "human-computer interaction" ], "summary": "Proposes an operating-system interaction paradigm in which connected generative models interpret user intentions and mediate or replace conventional application interfaces.", "contribution": "Sets out a broader vision for natural-language, personalized computer interaction, including language and diffusion models as interface components.", "limitations": "A conceptual proposal for generative computer interfaces.", "model_output": "Envisioned context-dependent responses and generated interfaces.", "action_interface": "Natural-language user requests, including text and speech.", "state_memory": "Proposes personalization and contextual interaction.", "relationship": "NeuralOS cites Prompt-to-OS as background for generative computer interaction.", "relationship_source": "https://arxiv.org/html/2507.08800v2", "author_names": [ "Gabriele Tolomei", "Cesare Campagnano", "Fabrizio Silvestri", "Giovanni Trappolini" ], "record_url": "https://neural-os.com/reading-list/papers/2310.04875/", "legacy_record_url": "https://neural-os.com/reading-list/papers/#paper-2310-04875", "citation_date": "2023-10-07", "json_url": "https://neural-os.com/reading-list/papers/2310.04875/record.json", "markdown_url": "https://neural-os.com/reading-list/papers/2310.04875/index.md", "bibtex_url": "https://neural-os.com/reading-list/papers/2310.04875/citation.bib", "csl_url": "https://neural-os.com/reading-list/papers/2310.04875/citation.csl.json" }, { "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" } ] } ] }