将用户指令自动转为安全路由策略,保障低轨星座网络稳定运行
Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations
- 用图神经网络实现高速精准路由,推理速度提升17倍
- 自然语言转约束指令准确率达98.4%,可识别复杂意图
- 全链路验证零误放行,对恶意攻击和结构错误100%检测
运营低轨巨型星座需将高层操作意图(如“在极地链路延迟低于80毫秒时重路由金融流量”)转化为底层路由约束,这一过程需兼具自然语言理解与网络领域知识。本文提出端到端系统:(1) 基于GNN的代价-到目标路由器将Dijkstra级路由能力压缩为152K参数的图注意力网络,实现99.8%包交付率与17倍推理加速;(2) 利用少量示例提示+验证反馈修复循环的LLM意图编译器,实现98.4%编译率与87.6%语义匹配率,在240条意图基准测试中(193条可行,47条不可行)表现优异;(3) 八轮确定性验证器具备构造性可行性证明能力,在所有47条不可行意图上实现0%误接受(含30条标注+17条由第8轮发现),在240次结构破坏测试与15次针对性对抗攻击中均实现100%检测率。四类受限路由场景的端到端评估显示无约束违规。进一步表明极地避让场景中的性能差距主要由拓扑可达性上限决定,而非路由质量,并证实该编译器在组合意图上比规则基线高46.2个百分点。
原文摘要 · Abstract (English)
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.
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