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The paper introduces Guardian, a multi-LLM pipeline designed to aid missing-person investigations by extracting and processing relevant information. The system employs task-specialized LLMs coordinated by a consensus engine that resolves disagreements between model outputs. QLoRA fine-tuning on curated datasets further enhances the pipeline's performance, focusing on structured extraction and labeling.
A consensus-driven multi-LLM pipeline can improve information extraction for missing-person investigations, offering a practical approach to leveraging LLMs in high-stakes scenarios.
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.
