Abstract
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.
Abstract (translated)
本文介绍了设计和验证一种基于检索的辅助系统，该系统支持无人驾驶航空系统的安全性评估、认证活动及法规遵从性。这项工作是受到无人机操作日益复杂化以及申请人和航空监管机构在一致且高效地应用现有评估框架（如具体操作风险评估和预定义风险评估）方面所需努力不断增加这一情况所驱动的。 提出的解决方案采用了一种基于受控文本架构，该架构仅依赖于权威法规来源。为了确保输出可追溯性和可审计性，辅助系统将每个响应都依据检索到的段落来生成，并强制执行引用驱动的产生机制。在系统层面采取控制措施以应对生成模型常见的故障模式，包括伪造陈述、缺乏支持的推理以及模糊出处等问题，通过将证据存储与语言生成相分离并采用保守行为（当支持文件不足时）来解决这些问题。 该辅助工具被有意限于决策支持，它不会取代专家判断也不作出自主决定。相反，它加快了特定情境下的信息检索和合成过程，以提高文档准备和审查效率，同时保留人类对关键结论的责任。架构使用已建立的开源组件实现，并评估在检索策略、交互限制以及响应政策方面的重要选择是否适合安全敏感的监管环境。 本文提供了将基于检索的辅助系统整合到航空监督工作流程中的技术与运营指导，以维持问责制、可追溯性和法规遵从性。
URL
https://arxiv.org/abs/2603.09999
