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

TL;DR

DarwinNet is a bio-inspired, self-evolving network architecture that enables protocols to adapt dynamically at runtime, improving resilience, security, and performance through autonomous evolution driven by intelligent agents.

Contribution

This paper introduces DarwinNet, a novel tri-layered, self-evolving network architecture that transitions protocols from static design to adaptive runtime growth using AI and bio-inspired principles.

Findings

DarwinNet achieves anti-fragility by evolving in response to environmental anomalies.

The Protocol Solidification Index (PSI) quantifies system maturity and evolution.

Experimental validation shows DarwinNet converges toward physical performance limits.

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.