🤖 AI Summary
This work addresses the high latency, excessive energy consumption, and behavioral incoherence arising from the tight coupling of planning, reasoning, and execution in current AI systems. To overcome these limitations, the authors propose Tri-Spirit, a novel architecture that decouples cognitive processing into three distinct layers—planning (Super Layer), reasoning (Agent Layer), and execution (Reflex Layer)—mapped onto heterogeneous hardware and coordinated via an asynchronous message bus. The framework introduces several key innovations, including a habit compilation mechanism, a convergence-based memory model, parameterized routing strategies, and explicit safety constraints. Experimental results demonstrate that Tri-Spirit reduces average task latency by 75.6%, cuts energy consumption by 71.1%, decreases large language model invocation frequency by 30%, and enables 77.6% of tasks to be completed efficiently in offline settings.
📝 Abstract
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.