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的人工定义规则，难以适应现代自主智能体涌现的边界情形与概率化推理需求，导致严重的协议僵化与结构脆弱性。为解决上述局限，本文提出DarwinNet——一种受生物启发的自进化网络架构，其将通信协议从“设计时”静态范式转变为“运行时”生长范式。DarwinNet采用三层框架结构：不可变物理锚点层（L0）、基于WebAssembly的流体皮层（L1）以及LLM驱动的达尔文皮层（L2），通过双循环“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）来量化系统从高延迟智能推理（慢速思维）向近原生执行（快速思维）演化过程中的进化成熟度。基于Crow-AMSAA模型的可靠性增长框架验证表明，DarwinNet通过将环境异常视为自主进化的催化剂实现了抗脆弱性。实验结果证实，DarwinNet能有效收敛至物理性能极限，同时通过零信任沙箱确保内生安全，为下一代智能自优化网络提供了可行路径。