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

View PDF HTML (experimental) 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. Subjects: Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Multiagent Systems (cs.MA); Networking and Internet Architecture (cs.NI) Cite as: arXiv:2604.01236 [cs.NE] (or arXiv:2604.01236v3 [cs.NE] for this version) https://doi.org/10.48550/arXiv.2604.01236 arXiv-issued DOI via DataCite Submission history From: Jinliang Xu [view email] [v1] Fri, 27 Mar 2026 01:49:19 UTC (707 KB) [v2] Mon, 13 Apr 2026 03:11:28 UTC (696 KB) [v3] Tue, 14 Apr 2026 09:07:49 UTC (281 KB)