mlsanityData to 14 Oct 2026

A Compliance-Preserving Retrieval System for Aircraft MRO Task Search

arXiv 2511.15383 ↗First posted 19 Nov 2025cs.CL cs.AI cs.ETcited by 0

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Abstract (arXiv)

Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals, a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources. We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers. The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers. Evaluation on 49k synthetic queries achieves >90% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9% top-10 success rate and 95% reduction in lookup time, from 6-15 minutes to 18 seconds per task. These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 19 Nov 2025.

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References · 4

  1. 2024M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillationcited by 1,073
  2. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 265
  3. 2023AviationGPT: A Large Language Model for the Aviation Domaincited by 9
  4. 2021Predictive Maintenance for General Aviation Using Convolutional Transformerscited by 6

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