The next generation of autonomous AI systems will be constrained not only by model capability, but by how intelligence is structured across heterogeneous hardware. Current paradigms -- cloud-centric AI, on-device inference, and edge-cloud pipelines -- treat planning, reasoning, and execution as a monolithic process, leading to unnecessary latency, energy consumption, and fragmented behavioral continuity. We introduce the Tri-Spirit Architecture, a three-layer cognitive framework that decomposes intelligence into planning (Super Layer), reasoning (Agent Layer), and execution (Reflex Layer), each mapped to distinct compute substrates and coordinated via an asynchronous message bus. We formalize the system with a parameterized routing policy, a habit-compilation mechanism that promotes repeated reasoning paths into zero-inference execution policies, a convergent memory model, and explicit safety constraints. We evaluate the architecture in a reproducible simulation of 2000 synthetic tasks against cloud-centric and edge-only baselines. Tri-Spirit reduces mean task latency by 75.6 percent and energy consumption by 71.1 percent, while decreasing LLM invocations by 30 percent and enabling 77.6 percent offline task completion. These results suggest that cognitive decomposition, rather than model scaling alone, is a primary driver of system-level efficiency in AI hardware.

翻译：下一代自主AI系统将不仅受限于模型能力，更受限于智能如何在异构硬件上的结构化组织。当前范式——以云端为中心的AI、端侧推理及边缘-云流水线——将规划、推理与执行视为单一体化流程，导致不必要的延迟、能耗及行为连续性碎片化。我们提出"三灵架构"(Tri-Spirit Architecture)，一种将智能分解为规划层(Super Layer)、推理层(Agent Layer)与执行层(Reflex Layer)的三层认知框架，各层分别映射至不同计算基底并通过异步消息总线协调。我们通过参数化路由策略、将重复推理路径提升为零推理执行策略的"习惯编译"机制、收敛记忆模型及显式安全约束对该系统进行形式化定义。在基于2000个合成任务的可复现仿真中，我们将本架构与云端中心化及纯边缘基线进行对比评估。三灵架构使平均任务延迟降低75.6%，能耗降低71.1%，同时减少30%的LLM调用次数，并实现77.6%的离线任务完成率。这些结果表明，认知分解（而非单纯模型缩放）是AI硬件系统级效率的核心驱动力。