DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

Abstract

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.

Figures (7)

• Figure 1: The Paradigm Shift: Contrasting the Fragility of Static Protocol Rules with the Anti-fragility of DarwinNet’s Fluid Evolution.
• Figure 2: The DarwinNet Inter-node Interaction Framework: Illustrating the vertical functional decoupling from Intent (L2) to Anchor (L0) and the horizontal bifurcation of the Slow Negotiation Path and the Fast Execution Path.
• Figure 3: The Bionic Dual-Loop Feedback System of DarwinNet: A transition from rigid rules to organic adaptation.
• Figure 4: Reliability growth analysis using the Crow-AMSAA Power Law model, demonstrating the diminishing frequency of protocol mismatch events as the system learns and adapts.
• Figure 5: PSI convergence over cumulative communication cycles, illustrating the transition from agent-driven reasoning to solidified native execution.