20260521.0002v1PositionReleased: April 7, 20261 Views

Neural 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|Zechun Liu|Yunyang Xiong|Yining Yang|Yuandong Tian|Yangyang Shi|Vikas Chandra|Jürgen Schmidhuber

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.

Keywords

Neural ComputersCompletely Neural ComputersWorld ModelsAI AgentsVideo Generation ModelsHuman-Computer InteractionNeural Runtime SystemsInteractive Generative Modeling

External Source

This is an externally sourced paper. It was originally published independently.