从意图到算法：传输网络的验证式算法发现

中文摘要 AI 辅助

意图驱动网络将期望结果与设备级配置解耦，但大多数系统仍将意图映射到预先选择的算法参数上。大语言模型（LLM）为自动化算法设计创造了机会，然而无限制生成的代码不适合传输网络控制，因为可行性、可复现性和鲁棒性必须独立于模型强制执行。我们提出了VERA-TN，一个验证引导的框架，将网络意图编译为有界的算法设计规范。目标架构使用LLM作为类型化请求排序和路径排序程序上的语义变异算子；生成的逻辑与可信分配器保持分离，该分配器强制执行路径有效性、延迟、容量和单路径约束。我们在明确假设下证明了可行性保持，并在精确参考模型中为字典序延迟平局判定建立了充分界限。发布的概念验证实例化了相同接口，采用有界的十参数数值候选和确定性重放，而非完整的实时LLM/AST研究。在从TEFNET24派生的28节点层次结构上的150个认证留出案例中，进化搜索达到平均优先级效用比0.958，而等预算随机搜索为0.952，优先级贪婪路由为0.940。相对于随机搜索的增益虽小但统计上可检测（Holm调整后p=0.0083）。该候选相对于MILP-C未改善拥塞，且故障感知训练的效果在0.05水平上不显著（p=0.051）。在官方国家拓扑上的八次发现运行和在12个未见的大都市区域拓扑上的重放显示没有稳定的意图特定特化。这些结果支持信任边界和数值进化主张，但未确立LLM生成的益处。

英文摘要

Intent-based networking decouples desired outcomes from device-level configuration, but most systems still map intents to parameters of an algorithm selected in advance. Large language models (LLMs) create an opportunity to automate algorithm design, yet unrestricted generated code is unsuitable for transport-network control because feasibility, reproducibility, and robustness must be enforced independently of the model. We present VERA-TN, a verification-guided framework that compiles a network intent into a bounded algorithm-design specification. The target architecture uses an LLM as a semantic variation operator over typed request-ordering and path-ranking programs; generated logic remains separated from a trusted allocator that enforces path validity, latency, capacity, and single-path constraints. We prove feasibility preservation under explicit assumptions and establish a sufficient bound for the lexicographic latency tie-break in the exact reference model. The released proof-of-concept instantiates the same interface with a bounded ten-parameter numerical candidate and deterministic replay, rather than a completed live-LLM/AST study. Across 150 certified held-out cases on a 28-node TEFNET24-derived hierarchy, evolutionary search reaches a mean priority-utility ratio of 0.958, compared with 0.952 for equal-budget random search and 0.940 for priority-greedy routing. The gain over random search is small but statistically detectable (Holm- adjusted p = 0.0083). The candidate does not improve congestion relative to MILP-C, and the effect of failure-aware training is inconclusive at the 0.05 level (p = 0.051). Eight discovery runs on the official national topology and replay on 12 unseen metro-regional topologies show no stable intent-specific specialization. These results support the trust-boundary and numerical-evolution claims but do not establish a benefit from LLM generation.

发表机构

• CTTC（电信技术中心）

机构由 AI 辅助整理，请以论文原文为准。