mlsanityData to 7 Oct 2026

Validated Intent Compilation for Constrained Routing in LEO Mega-Constellations

arXiv 2604.07264 ↗First posted 8 Apr 2026cs.CR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 8 Apr 2026.

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References · 5

  1. 2017Graph Attention Networkscited by 8,854
  2. 2024NetLLM: Adapting Large Language Models for Networkingcited by 76
  3. 2023Large Language Models for Networking: Applications, Enabling Techniques, and Challengescited by 28
  4. 2022RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluationcited by 15
  5. 2020Refining Network Intents for Self-Driving Networkscited by 3

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  1. 2026STR-Agent: An LLM-Driven Agent for QoS-Aware Routing in LEO Satellite Networks
  2. 2026TokenRouter: Efficient Serving System for Token-Level LLM Routing
  3. 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Design
  4. 2026Evaluating Small Language Models for Front-Door Routing: A Harmonized Benchmark and Synthetic-Traffic Experiment
  5. 2026Vision Language Models for Optimization-Driven Intent Processing in Autonomous Networks
  6. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
  7. 2026Stepped MoE: Segment-Level Routing with Configurable Inference Complexity
  8. 2026Neurosymbolic Routing for Reliable Reasoning on Resource-Constrained Edge Devices