Computer Science > Artificial Intelligence
arXiv:2603.08954 (cs)
[Submitted on 9 Mar 2026]
Title:A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
View a PDF of the paper titled A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations, by Joshua Castillo and Ravi Mukkamala
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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.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Distributed, Parallel, and Cluster Computing (cs.DC); Information Retrieval (cs.IR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.08954 [cs.AI] |
| (or arXiv:2603.08954v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08954
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View a PDF of the paper titled A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations, by Joshua Castillo and Ravi Mukkamala
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