Neural Computers
We propose a new frontier: Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and world models, which learn environment dynamics, NCs aim to make the model itself the running computer. 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 early 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. These implementations show that learned runtimes can acquire early interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain open. We outline a roadmap toward CNCs around these challenges. If overcome, CNCs could establish a new computing paradigm beyond today's agents, world models, and conventional computers.
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Mingchen Zhuge
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Changsheng Zhao
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Haozhe Liu
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Zijian Zhou
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2026-04-13T07:34:24.196Z
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Zijian Zhou
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Shuming Liu
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2026-04-13T07:34:20.690Z
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Shuming Liu
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sming256
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Wenyi Wang
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Ernie Chang
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Gael Le Lan
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Junjie Fei
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2026-04-10T13:34:17.571Z
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Junjie Fei
FeiElysia
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FeiElysia
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Wenxuan Zhang
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Yasheng Sun
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Zhipeng Cai
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Zechun Liu
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Yunyang Xiong
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Yining Yang
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Yuandong Tian
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Yangyang Shi
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Vikas Chandra
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Jürgen Schmidhuber
Neural Computers
We propose a new frontier: Neural Computers (NCs) -- an emerging machine form that unifies computation, memory, and I/O in a learned runtime state. Unlike conventional computers, which execute explicit programs, agents, which act over external execution environments, and world models, which learn environment dynamics, NCs aim to make the model itself the running computer. 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 early 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. These implementations show that learned runtimes can acquire early interface primitives, especially I/O alignment and short-horizon control, while routine reuse, controlled updates, and symbolic stability remain open. We outline a roadmap toward CNCs around these challenges. If overcome, CNCs could establish a new computing paradigm beyond today's agents, world models, and conventional computers.
Neural Computers represent a new computing paradigm where models function as runtime systems, learning to execute tasks through I/O traces rather than explicit programming.
Neural Computers learned runtime state Completely Neural Computer video models I/O traces screen frames interface primitives I/O alignment short-horizon control routine reuse symbolic stability