Neural Computers
Abstract
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.
Cite
@article{arxiv.2604.06425,
title = {Neural Computers},
author = {Mingchen Zhuge and Changsheng Zhao and Haozhe Liu and Zijian Zhou and Shuming Liu and Wenyi Wang and Ernie Chang and Gael Le Lan and Junjie Fei and Wenxuan Zhang and Yasheng Sun and Zhipeng Cai and Zechun Liu and Yunyang Xiong and Yining Yang and Yuandong Tian and Yangyang Shi and Vikas Chandra and Jürgen Schmidhuber},
journal= {arXiv preprint arXiv:2604.06425},
year = {2026}
}
Comments
Github (data pipeline): https://github.com/metauto-ai/NeuralComputer; Blogpost: https://metauto.ai/neuralcomputer/index_eng.html