{"ID":2839537,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15383","arxiv_id":"2511.15383","title":"A Compliance-Preserving Retrieval System for Aircraft MRO Task Search","abstract":"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 \u003e90% 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.","short_abstract":"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 env...","url_abs":"https://arxiv.org/abs/2511.15383","url_pdf":"https://arxiv.org/pdf/2511.15383v1","authors":"[\"Byungho Jo\"]","published":"2025-11-19T12:25:40Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.ET\",\"cs.IR\"]","methods":"[\"Large Language Model\"]","has_code":false}