{"slug": "neural-computers", "title": "Neural Computers", "summary": "A paper posted to arXiv on 7 April 2026 (revised 16 April 2026, v2) by Mingchen Zhuge and co-authors proposes \"Neural Computers\" (NCs), a machine form that unifies computation, memory, and I/O in a learned runtime state, with the long-term goal of a general-purpose \"Completely Neural Computer\" (CNC). The authors instantiate NCs as video models that roll out screen frames from instructions, pixels, and user actions in CLI and GUI settings, and report that NCs acquire elementary interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain challenging. The paper outlines a roadmap toward CNCs, positioning the work as a computing paradigm beyond today's agents and conventional computers.", "body_md": "# Computer Science > Machine Learning\n\n  [Submitted on 7 Apr 2026 (\n\n[v1](https://arxiv.org/abs/2604.06425v1)), last revised 16 Apr 2026 (this version, v2)]\n# Title:Neural Computers\n\n[View PDF](https://arxiv.org/pdf/2604.06425)\n\n[HTML (experimental)](https://arxiv.org/html/2604.06425v2)\n\nAbstract:We propose a new frontier: Neural Computers (NCs) that unify computation, memory, and I/O of traditional computers in a learned runtime state. Our long-term goal is the Completely Neural Computer (CNC): the mature, general-purpose realization of this emerging machine form, with stable execution, explicit reprogramming, and durable capability reuse. As an initial step, we study whether elementary NC primitives can be learned solely from collected I/O traces, without instrumented program state. Concretely, we instantiate NCs as video models that roll out screen frames from instructions, pixels, and user actions (when available) in CLI and GUI settings. We show that NCs can acquire elementary interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain challenging. We outline a roadmap toward CNCs, to establish a new computing paradigm beyond today's agents and conventional computers.\n    \n\n## Submission history\n\nFrom: Mingchen Zhuge [\n[view email](https://arxiv.org/show-email/e741ae62/2604.06425)]\n\n**Tue, 7 Apr 2026 20:01:05 UTC (27,484 KB)**\n\n[\\[v1\\]](https://arxiv.org/abs/2604.06425v1)\n**[v2]** Thu, 16 Apr 2026 21:05:33 UTC (27,474 KB)\n\n### Additional Features\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\nIArxiv Recommender\n\n*(*[What is IArxiv?](https://iarxiv.org/about))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/neural-computers", "canonical_source": "https://arxiv.org/abs/2604.06425", "published_at": "2026-10-23 13:53:08+00:00", "updated_at": "2026-10-23 14:13:14.225345+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "ai-research", "ai-agents", "computer-vision"], "entities": ["arXiv", "Mingchen Zhuge", "Neural Computers", "Completely Neural Computer"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/neural-computers", "markdown": "https://wpnews.pro/news/neural-computers.md", "text": "https://wpnews.pro/news/neural-computers.txt", "jsonld": "https://wpnews.pro/news/neural-computers.jsonld"}}