标题： 为智能未来增强冯·诺依曼架构 显示英文标题

标题： Augmenting Von Neumann's Architecture for an Intelligent Future

摘要： 这项工作提出了一种新颖的计算机架构，该架构在冯·诺依曼模型的基础上扩展了一个专用推理单元（RU），以实现原生的人工通用智能能力。 RU作为专用协处理器，执行符号推理、多智能体协调和混合符号-神经计算作为基本的架构原语。 这种硬件嵌入的方法使自主代理能够在系统规模的计算基质中直接执行目标导向的规划、动态知识操作和内省推理。 该架构包含一个针对推理的指令集架构、并行符号处理流水线、面向智能体的内核抽象以及统一的内存层次结构，可无缝集成认知和数值工作负载。 通过在硬件、操作系统和智能体运行时层面上的系统性协同设计，该架构建立了一个计算基础，使得推理、学习和适应成为内在的执行特性，而非软件抽象，可能促进通用智能机器的发展。 显示英文摘要

摘要： This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and introspective reasoning directly within the computational substrate at system scale. The architecture incorporates a reasoning-specific instruction set architecture, parallel symbolic processing pipelines, agent-aware kernel abstractions, and a unified memory hierarchy that seamlessly integrates cognitive and numerical workloads. Through systematic co-design across hardware, operating system, and agent runtime layers, this architecture establishes a computational foundation where reasoning, learning, and adaptation emerge as intrinsic execution properties rather than software abstractions, potentially enabling the development of general-purpose intelligent machines.