In plain words: Unlike separate chips, memory, and screens, this folds computation, memory, and input/output into a learned state, shown as video models predicting the next screen from instructions, pixels, and clicks. It learned skills like input-output matching and short control, but reusing routines and staying reliable failed.
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.
Mingchen Zhuge, Changsheng Zhao, Haozhe Liu, Zijian Zhou, Shuming Liu, Wenyi Wang, Ernie Chang, Gael Le Lan, Junjie Fei, Wenxuan Zhang, Yasheng Sun, Zhipeng Cai, et al.
arXiv:2604.06425 · cs.LG, cs.AI · submitted Apr 7, 2026 · updated Apr 16, 2026
abstract · pdf · html · Github (data pipeline): https://github.com/metauto-ai/NeuralComputer; Blogpost: https://metauto.ai/neuralcomputer/index_eng.html