AI Agents 相关度: 7/10

Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

Yuanhang Li
arXiv: 2604.07264v1 发布: 2026-04-08 更新: 2026-04-08

AI 摘要

构建LEO卫星网络中意图驱动的安全路由系统,实现意图编译和验证。

主要贡献

  • 提出了一个端到端的系统,将自然语言意图转化为低级别路由约束。
  • 使用GNN实现高效的路由计算,加速推理。
  • 构建LLM意图编译器,将自然语言意图转化为可验证的约束表示。

方法论

利用LLM进行意图编译,GNN进行路由计算,并设计了确定性的验证器保证安全性。

原文摘要

Operating LEO mega-constellations requires translating high-level operator intents ("reroute financial traffic away from polar links under 80 ms") into low-level routing constraints -- a task that demands both natural language understanding and network-domain expertise. We present an end-to-end system comprising three components: (1) a GNN cost-to-go router that distills Dijkstra-quality routing into a 152K-parameter graph attention network achieving 99.8% packet delivery ratio with 17x inference speedup; (2) an LLM intent compiler that converts natural language to a typed constraint intermediate representation using few-shot prompting with a verifier-feedback repair loop, achieving 98.4% compilation rate and 87.6% full semantic match on feasible intents in a 240-intent benchmark (193 feasible, 47 infeasible); and (3) an 8-pass deterministic validator with constructive feasibility certification that achieves 0% unsafe acceptance on all 47 infeasible intents (30 labeled + 17 discovered by Pass 8), with 100% corruption detection across 240 structural corruption tests and 100% on 15 targeted adversarial attacks. End-to-end evaluation across four constrained routing scenarios confirms zero constraint violations with both routers. We further demonstrate that apparent performance gaps in polar-avoidance scenarios are largely explained by topological reachability ceilings rather than routing quality, and that the LLM compiler outperforms a rule-based baseline by 46.2 percentage points on compositional intents. Our system bridges the semantic gap between operator intent and network configuration while maintaining the safety guarantees required for operational deployment.

标签

LLM GNN 路由 意图编译 卫星网络

arXiv 分类

cs.CR cs.AI