LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources

• URL: http://arxiv.org/abs/2604.06571v1
• Date: Wed, 08 Apr 2026 01:35:56 GMT
• Title: LLM-based Schema-Guided Extraction and Validation of Missing-Person Intelligence from Heterogeneous Data Sources
• Abstract summary: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles.<n>Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning.<n>This paper introduces the Guardian Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation.
• Score: 0.7734726150561088
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles. Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning workflows. This paper introduces the Guardian Parser Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation suitable for operational review and downstream spatial modeling. The proposed system integrates (i) multi-engine PDF text extraction with Optical Character Recognition (OCR) fallback, (ii) rule-based source identification with source-specific parsers, (iii) schema-first harmonization and validation, and (iv) an optional Large Language Model (LLM)-assisted extraction pathway incorporating validator-guided repair and shared geocoding services. We present the system architecture, key implementation decisions, and output design, and evaluate performance using both gold-aligned extraction metrics and corpus-level operational indicators. On a manually aligned subset of 75 cases, the LLM-assisted pathway achieved substantially higher extraction quality than the deterministic comparator (F1 = 0.8664 vs. 0.2578), while across 517 parsed records per pathway it also improved aggregate key-field completeness (96.97\% vs. 93.23\%). The deterministic pathway remained much faster (mean runtime 0.03 s/record vs. 3.95 s/record for the LLM pathway). In the evaluated run, all LLM outputs passed initial schema validation, so validator-guided repair functioned as a built-in safeguard rather than a contributor to the observed gains. These results support controlled use of probabilistic AI within a schema-first, auditable pipeline for high-stakes investigative settings.

Related papers

• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• DISA: Offline Importance Sampling for Distribution-Matching LLM-RL [56.9445657766829]
  We introduce DISA, which moves this calibration problem outside the RL loop.<n> DISA draws proposal trajectories offline, estimates the partition function via importance sampling, and freezes the resulting partition-function estimate.<n>On two open-weight backbones across six math and three code benchmarks, DISA matches or exceeds the online-coupled distribution-matching baseline Flow.
  arXiv  Detail & Related papers  (2026-05-17T07:14:44Z)
• Towards Multi-Agent Autonomous Reasoning in Hydrodynamics [0.06999740786886537]
  We present a multi-agent system (MAS) prototype for hydrodynamics in which specialized agents are coordinated through a Layer Execution Graph (LEG)<n>A planner agent constructs query-specific execution topologies from natural-language routings that capture domain knowledge without hard-coding it as rigid control logic.<n>A reporter agent synthesizes the final response, while the runtime logs provenance for every tool invocation to support auditability.
  arXiv  Detail & Related papers  (2026-05-01T21:17:55Z)
• GLM-OCR Technical Report [65.42028025507491]
  GLM-OCR is an efficient compact model designed for real-world document understanding.<n>It combines a CogViT visual encoder with a GLM language decoder, achieving a strong balance between computational efficiency and recognition performance.<n>Extensive evaluations on public benchmarks and industrial scenarios show that GLM-OCR achieves competitive or state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-03-11T15:55:47Z)
• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation [0.0]
  Large Language Models (LLMs) offer a promising approach to automate RDF knowledge graph generation.<n>We evaluate multiple LLMs and prompting strategies for automated RDF extraction using a controlled framework.<n>Our analysis shows that Few-Shot learning is the most effective strategy, with Llama achieving a 99.35% F1 score and 100% valid RDF output.
  arXiv  Detail & Related papers  (2026-02-06T06:30:35Z)
• ARIAL: An Agentic Framework for Document VQA with Precise Answer Localization [1.580774794371876]
  We present ARIAL, a framework that orchestrates specialized tools to achieve both precise answer extraction and reliable spatial grounding.<n>We evaluate ARIAL on four benchmarks (DocVQA, FUNSD, CORD, and SROIE) using both textual accuracy (ANLS) and spatial precision.<n>Our work demonstrates how agentic orchestration of specialized tools can simultaneously improve performance and interpretability.
  arXiv  Detail & Related papers  (2025-11-22T21:09:28Z)
• Relational Deep Dive: Error-Aware Queries Over Unstructured Data [9.0236658372663]
  ReDD (Relational Deep Dive) is a framework that dynamically discovers query-specific schemas, populates relational tables, and ensures error-aware extraction with provable guarantees.<n>Main contribution is SCAPE, a statistically calibrated method for error detection with coverage guarantees, and SCAPE-HYB, a hybrid approach that optimize the trade-off between accuracy and human correction costs.
  arXiv  Detail & Related papers  (2025-11-04T16:30:55Z)
• MCP-Orchestrated Multi-Agent System for Automated Disinformation Detection [84.75972919995398]
  This paper presents a multi-agent system that uses relation extraction to detect disinformation in news articles.<n>The proposed Agentic AI system combines four agents: (i) a machine learning agent (logistic regression), (ii) a Wikipedia knowledge check agent, and (iv) a web-scraped data analyzer.<n>Results demonstrate that the multi-agent ensemble achieves 95.3% accuracy with an F1 score of 0.964, significantly outperforming individual agents and traditional approaches.
  arXiv  Detail & Related papers  (2025-08-13T19:14:48Z)
• CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward [50.97588334916863]
  We develop CompassVerifier, an accurate and robust lightweight verifier model for evaluation and outcome reward.<n>It demonstrates multi-domain competency spanning math, knowledge, and diverse reasoning tasks, with the capability to process various answer types.<n>We introduce VerifierBench benchmark comprising model outputs collected from multiple data sources, augmented through manual analysis of metaerror patterns to enhance CompassVerifier.
  arXiv  Detail & Related papers  (2025-08-05T17:55:24Z)
• HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis [55.2480439325792]
  HySemRAG is a framework that combines Extract, Transform, Load (ETL) pipelines with Retrieval-Augmented Generation (RAG)<n>System addresses limitations in existing RAG architectures through a multi-layered approach.
  arXiv  Detail & Related papers  (2025-08-01T20:30:42Z)
• GPT, But Backwards: Exactly Inverting Language Model Outputs [7.736516533045638]
  We present the Sparse One-hot Discrete Adam (SODA) algorithm, a search-based inversion method that can accurately reconstruct the input text.<n>Experiments demonstrate for the first time that exact language model inversion is possible on both natural language and random inputs.
  arXiv  Detail & Related papers  (2025-07-02T13:20:30Z)
• Evaluating the Use of LLMs for Documentation to Code Traceability [3.076436880934678]
  Large Language Models can establish trace links between various software documentation and source code.<n>We create two novel datasets from two open-source projects (Unity Catalog and Crawl4AI)<n>Results show that the best-performing LLM achieves F1-scores of 79.4% and 80.4% across the two datasets.
  arXiv  Detail & Related papers  (2025-06-19T16:18:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.