https:\/\/arxiv.org\/pdf\/2604.06425<\/a><\/p>\n Introduction
\nCan a single set of weights act as a \u201ccomputer\u201d? We term this abstraction a Neural Computer (NC): a neural system that unifies computation, memory, and I\/O in a learned runtime state. This usage is distinct from the Neural Turing Machine \/ Differentiable Neural Computer line (Graves et al., 2014, 2016): our concern is not differentiable external memory, but whether a learning machine can begin to assume the role of the running computer itself.<\/p>\n
To implement this idea, we instantiate NCs as video models. At this stage, video models are the most practical substrate for this prototype, though we expect the long-term solution to require a fundamentally new neural architecture (Section 4). This implementation draws on several technical lines. <\/p>\n
World models (Ha and Schmidhuber, 2018) show that neural networks can internalize environment dynamics and support predictive imagination, while high-capacity video generators such as Veo 3.1 (Google, 2025) and Sora 2 (OpenAI, 2025) show that such learned dynamics can be rendered into coherent frame sequences. <\/p>\n
Frontier interactive video models such as Genie 3 (Bruce et al., 2024) further extend this trajectory toward action-controllable generative environments. <\/p>\n
These lines provide practical machinery for current NC prototypes, but do not by themselves define the NC abstraction. In parallel, LLM-driven UI systems such as Imagine with Claude1 map
\nnatural-language inputs to structured interface updates. Yet these capabilities remain split across different systems objects: conventional computers execute explicit programs, agents act through external execution environments, and world models render or predict environment dynamics, while executable state still resides outside the model. <\/p>\n
NCs are motivated by this gap: they are not a smarter layer on top of the existing stack,
\nbut a proposal to make the model itself the running computer. <\/p>\n
The immediate question in this paper is whether early runtime primitives can be learned directly from raw interface I\/O without privileged access to program state.
\nThroughout this paper, NC denotes this proposed machine form, while CNC denotes its mature, general-purpose realization…. <\/p>\n","protected":false},"excerpt":{"rendered":"
*It’s a proposal — replacing the programmable computers of the past with Turing-equivalent systems that are based on visual neural-net models. https:\/\/arxiv.org\/pdf\/2604.06425 Introduction Can a single set of weights act as a \u201ccomputer\u201d? We term this abstraction a Neural Computer (NC): a neural system that unifies computation, memory, and I\/O in a learned runtime state. […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-6366","post","type-post","status-publish","format-standard","hentry","category-uncategorised"],"yoast_head":"\n
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