神經計算機

摘要

我們提出一個新前沿：神經計算機（NCs）——一種新興的機器形態，將計算、記憶與輸入/輸出統一於學習產生的運行時狀態中。有別於執行顯式程式的傳統計算機、在外部執行環境中行動的智能體，以及學習環境動態的世界模型，NCs的目標是讓模型本身成為運行的計算機。我們的長期願景是實現完全神經計算機（CNC）：這種新興機器形態的成熟通用化實現，具備穩定執行、顯式重編程與持久化能力複用等特性。作為初步探索，我們研究早期NC原語能否僅從收集的輸入/輸出軌跡中學習，而無需插樁程式狀態。具體而言，我們將NCs實例化為視訊模型，在命令行與圖形介面環境中根據指令、像素及使用者操作（若可用）生成螢幕畫面。這些實證表明，學習型運行時可掌握基礎介面原語，特別是輸入/輸出對齊與短時程控制，但常規複用、受控更新與符號穩定性仍是待解難題。我們圍繞這些挑戰規劃了CNC的發展路線圖。若能突破這些障礙，CNC或將建立超越現有智能體、世界模型與傳統計算機的新計算範式。

English

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.