cs.NEMar 27, 2026

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

Authors: Jinliang Xu, Bingqi Li

Organizations: China Academy of Information and Communications Technology, Beijing, China

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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 design-time static paradigm to a 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 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 & tables

Fig. 1: The Paradigm Shift: Contrasting the Fragility of Static Protocol Rules with the Anti-fragility of DarwinNet’s Fluid Evolution.
Fig. 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.
Fig. 3: The Bionic Dual-Loop Feedback System of DarwinNet: A transition from rigid rules to organic adaptation.
Fig. 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.
Fig. 5: PSI convergence over cumulative communication cycles, illustrating the transition from agent-driven reasoning to solidified native execution.
Fig. 6: System adaptability and anti-fragility under environmental shock, showing a temporary dip in PSI followed by rapid autonomous recovery through protocol re-mutation.
Fig. 7: Latency performance evolution on a logarithmic scale, highlighting the transition from the high-latency Evolutionary Tax (LLM-based reasoning) to the near-native Efficiency Dividend (solidified execution).

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