cs.AIMar 9, 2026

A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations

Authors: Joshua Castillo, Ravi Mukkamala

Organizations: Old Dominion University, Norfolk VA 23529, USA

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Abstract

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.

Figures & tables

Figure 1: Guardian System Architecture with two distinct but interconnected systems
Figure 2: Guardian LLM Pipeline Overview and Consensus Routing
Figure 3: Centralized Consensus Mechanism and Conflict Resolution Workflow
Figure 4: Prompt Governance and Template-Based LLM Interaction
Figure 5: Excerpt from the Llama LLM JSON Output
Figure 6: Excerpt from the Gemini Consensus LLM JSON Output
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