mlsanityData to 7 Oct 2026

A Retrieval-Augmented Language Assistant for Unmanned Aircraft Safety Assessment and Regulatory Compliance

arXiv 2603.09999 ↗First posted 16 Feb 2026cs.CL cs.AI cs.CEcited by 0

On the map: Retrieval-augmented generation → Legal and financial RAG

Abstract (arXiv)

This paper presents the design and validation of a retrieval-based assistant that supports safety assessment, certification activities, and regulatory compliance for unmanned aircraft systems. The work is motivated by the growing complexity of drone operations and the increasing effort required by applicants and aviation authorities to apply established assessment frameworks, including the Specific Operations Risk Assessment and the Pre-defined Risk Assessment, in a consistent and efficient manner. The proposed approach uses a controlled text-based architecture that relies exclusively on authoritative regulatory sources. To enable traceable and auditable outputs, the assistant grounds each response in retrieved passages and enforces citation-driven generation. System-level controls address common failure modes of generative models, including fabricated statements, unsupported inferences, and unclear provenance, by separating evidence storage from language generation and by adopting conservative behavior when supporting documentation is insufficient. The assistant is intentionally limited to decision support; it does not replace expert judgment and it does not make autonomous determinations. Instead, it accelerates context-specific information retrieval and synthesis to improve document preparation and review while preserving human responsibility for critical conclusions. The architecture is implemented using established open-source components, and key choices in retrieval strategy, interaction constraints, and response policies are evaluated for suitability in safety-sensitive regulatory environments. The paper provides technical and operational guidance for integrating retrieval-based assistants into aviation oversight workflows while maintaining accountability, traceability, and regulatory compliance.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 26 of its 43 references that are in the map. First posted on arXiv 16 Feb 2026.

Builds on

26 of 43 references.

References · 26

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14 more
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  3. 2023Large language models in healthcare and medical domain: A reviewcited by 126
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  12. 2025Cross-Format Retrieval-Augmented Generation in XR with LLMs for Context-Aware Maintenance Assistancecited by 3
  13. 2025AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Modelscited by 3
  14. 2025LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MROcited by 1

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