Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-defined rules that fail to adapt to the emergent edge cases and probabilistic reasoning of modern autonomous agents. To address these limitations, this paper proposes DarwinNet, a bio-inspired, self-evolving network architecture that transitions communication protocols from a \textit{design-time} static paradigm to a \textit{runtime} growth paradigm. DarwinNet utilizes a tri-layered framework-comprising an immutable physical anchor (L0), a WebAssembly-based fluid cortex (L1), and an LLM-driven Darwin cortex (L2)-to synthesize high-level business intents into executable bytecode through a dual-loop \textit{Intent-to-Bytecode} (I2B) mechanism. We introduce the Protocol Solidification Index (PSI) to quantify the evolutionary maturity of the system as it collapses from high-latency intelligent reasoning (Slow Thinking) toward near-native execution (Fast Thinking). Validated through a reliability growth framework based on the Crow-AMSAA model, experimental results demonstrate that DarwinNet achieves anti-fragility by treating environmental anomalies as catalysts for autonomous evolution. Our findings confirm that DarwinNet can effectively converge toward physical performance limits while ensuring endogenous security through zero-trust sandboxing, providing a viable path for the next generation of intelligent, self-optimizing networks.

翻译：传统网络架构因依赖静态、人工定义的规则，无法适应现代自主智能体涌现的极端情况与概率推理，导致严重的协议僵化与结构脆弱性。针对这些局限，本文提出一种受生物启发的自进化网络架构——达尔文网络（DarwinNet），将通信协议从“设计时”静态范式转变为“运行时”生长范式。该架构采用三层框架：不可变的物理锚点层（L0）、基于WebAssembly的动态皮层层（L1）以及LLM驱动的达尔文皮层层（L2），通过双循环的“意图到字节码”（I2B）机制，将高层业务意图合成为可执行字节码。我们引入协议固化指数（PSI）量化系统的进化成熟度——从高延迟智能推理（慢思考）逐渐收敛为近原生执行（快思考）。基于Crow-AMSAA模型的可靠性增长框架验证结果表明：达尔文网络通过将环境异常视为自主进化的催化剂，实现了反脆弱性。实验证实，该架构能在收敛至物理性能极限的同时，借助零信任沙箱保障内生安全，为下一代智能自优化网络提供了可行路径。