Rohan Paul@rohanpaul_ai · XMeta论文"Neural Computers"实现概念突破：模型可直接从屏幕与动作轨迹中学习计算机运行时行为，无需依赖底层计算机执行步骤。传统AI代理仅负责决策，而计算与存储由外部系统完成；该研究让模型本身成为承载状态、更新界面、生成输出的主体。这意味着计算、内存与I/O可能融合为单一的学习运行时状态，模型将"计算机"内化为自身动态。实验显示，CLI与GUI模型已能学习终端渲染和光标行为，预示软件、内存与执行的边界将显著模糊。
This Meta paper is this week's most important paper indeed.
They showed that a model can learn some of the runtime behavior of a computer directly from screen-and-action traces， instead of relying on a normal computer underneath to carry out every step.
The big deal is the change in where computation lives.
In normal AI agents， the model decides what to do， but the actual computer still does the computing， stores the memory， and updates the interface. In this paper， the authors are asking whether the model itself can become the thing that holds state， updates the world， and produces the next screen. That is the conceptual leap.
The claim is that computation， memory， and input-output might eventually collapse into one learned runtime state， so the model is no longer controlling a computer from outside but carrying the computer inside its own dynamics.