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Bibliographic Details
Main Authors:	Zhuge, Mingchen, Zhao, Changsheng, Liu, Haozhe, Zhou, Zijian, Liu, Shuming, Wang, Wenyi, Chang, Ernie, Lan, Gael Le, Fei, Junjie, Zhang, Wenxuan, Sun, Yasheng, Cai, Zhipeng, Liu, Zechun, Xiong, Yunyang, Yang, Yining, Tian, Yuandong, Shi, Yangyang, Chandra, Vikas, Schmidhuber, Jürgen
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2604.06425
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Table of Contents:

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.

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