The first 72 hours of a missing-person investigation are critical for successful recovery. Guardian is an end-to-end system designed to support missing-child investigation and early search planning. This paper presents the Guardian LLM Pipeline, a multi-model system in which LLMs are used for intelligent information extraction and processing related to missing-person search operations. The pipeline coordinates end-to-end execution across task-specialized LLM models and invokes a consensus LLM engine that compares multiple model outputs and resolves disagreements. The pipeline is further strengthened by QLoRA-based fine-tuning, using curated datasets. The presented design aligns with prior work on weak supervision and LLM-assisted annotation, emphasizing conservative, auditable use of LLMs as structured extractors and labelers rather than unconstrained end-to-end decision makers.

翻译：失踪人员调查的前72小时对于成功寻回至关重要。Guardian是一个端到端系统，旨在支持失踪儿童调查与早期搜寻规划。本文提出Guardian LLM流程，这是一个多模型系统，其中LLM被用于执行与失踪人员搜寻行动相关的智能信息提取与处理。该流程协调跨任务专用LLM模型的端到端执行，并调用共识LLM引擎以对比多个模型输出并解决分歧。通过使用精选数据集进行基于QLoRA的微调，该流程得到进一步强化。所提出的设计与先前关于弱监督和LLM辅助标注的研究保持一致，强调将LLM作为结构化提取器和标注工具进行保守、可审计的使用，而非不受约束的端到端决策器。