ArXiv TLDR

Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

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2604.07264

Yuanhang Li

cs.CRcs.AI

TLDR

This paper presents a validated intent compilation system using LLMs and GNNs for constrained routing in LEO mega-constellations, ensuring safety and efficiency.

Key contributions

  • A GNN cost-to-go router achieves Dijkstra-quality routing with 17x faster inference and 99.8% packet delivery.
  • An LLM intent compiler translates natural language to constraints with 98.4% compilation and 87.6% semantic match.
  • An 8-pass deterministic validator ensures 0% unsafe acceptance for infeasible intents and detects 100% corruption.

Why it matters

This system bridges the semantic gap between high-level operator intents and low-level network configurations in LEO constellations. It provides critical safety guarantees, making it suitable for operational deployment and improving network management efficiency.

Original Abstract

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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