A Consensus-Driven Multi-LLM Pipeline for Missing-Person Investigations
arXiv:2603.08954v1 Announce Type: new 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.
arXiv:2603.08954v1 Announce Type: new 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.
Executive Summary
This article proposes the Guardian LLM Pipeline, a multi-model system for missing-person investigations, utilizing large language models (LLMs) for intelligent information extraction and processing. The pipeline coordinates task-specialized LLM models, invokes a consensus LLM engine to resolve disagreements, and employs QLoRA-based fine-tuning with curated datasets. The design aligns with prior work on weak supervision and LLM-assisted annotation, emphasizing conservative use of LLMs as extractors and labelers. The Guardian LLM Pipeline presents a promising solution for supporting early search planning in missing-person investigations, particularly within the critical first 72 hours. Its focus on conservative use of LLMs and structured processing enhances the reliability and audibility of the system. The article contributes to the growing area of LLM applications in law enforcement and missing-person investigations, warranting further research and development.
Key Points
- ▸ The Guardian LLM Pipeline utilizes a multi-model system for missing-person investigations, combining task-specialized LLM models and a consensus LLM engine.
- ▸ The pipeline employs QLoRA-based fine-tuning with curated datasets to strengthen its accuracy and reliability.
- ▸ The design aligns with prior work on weak supervision and LLM-assisted annotation, emphasizing conservative use of LLMs as extractors and labelers.
Merits
Strength
The Guardian LLM Pipeline's multi-model architecture ensures robustness and reliability in missing-person investigations, particularly within the critical first 72 hours.
Structured Processing
The system's focus on conservative use of LLMs as structured extractors and labelers enhances its audibility and reliability.
Curated Datasets
QLoRA-based fine-tuning with curated datasets strengthens the pipeline's accuracy and reliability.
Demerits
Limitation
The Guardian LLM Pipeline's reliance on LLMs may be limited by their potential biases and inaccuracies, requiring ongoing evaluation and fine-tuning.
Data Quality
The quality and availability of curated datasets may impact the pipeline's performance and accuracy, requiring continuous dataset curation and improvement.
Scalability
The Guardian LLM Pipeline's scalability and adaptability in handling large volumes of data and complex investigations may be a challenge, requiring further research and development.
Expert Commentary
The Guardian LLM Pipeline presents a significant advancement in the application of LLMs in missing-person investigations, addressing the critical challenge of early search planning. Its focus on conservative use of LLMs, structured processing, and curated datasets enhances the reliability and audibility of the system. While the pipeline's reliance on LLMs and curated datasets may pose limitations, the article's contribution to the growing area of LLM applications in law enforcement and missing-person investigations is substantial. Further research and development are necessary to address the pipeline's scalability, adaptability, and potential biases, ensuring its effective integration into existing law enforcement systems.
Recommendations
- ✓ Further research is needed to explore the Guardian LLM Pipeline's scalability and adaptability in handling large volumes of data and complex investigations.
- ✓ The development of more robust and diverse curated datasets is essential to strengthen the pipeline's accuracy and reliability.
- ✓ The integration of the Guardian LLM Pipeline into existing law enforcement systems should be conducted with careful consideration of its potential biases and inaccuracies, ensuring auditable and reliable performance.
