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.
Figures & tables
Fig. 1: Overall System Architecture of the Guardian Parser Pack (Dual-Path Extraction with Shared Services.)
Fig. 2: Core Parsing and Reasoning Pipeline (Extraction, Harmonization, Validation, and Repair Loop).
Fig. 3: Example Missing Person Document-Page 1
Fig. 4: Example Missing Person Document-Page 2
Fig. 5: LLM Parser Path Output for the Example Document
Fig. 6: Rule-based Parser Path Output for the Example Document
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.
Enterprise workflows increasingly rely on agents for schema-guided extraction: given a document and a user-defined schema, the agent faithfully follows the schema to produce the correct output with source evidence as grounding metadata. We present ExtractBench, a benchmark for schema-guided extraction and, to our knowledge, the first to score value accuracy, record completeness at scale, grounding, and measured cost together. The evaluation system contains 4,869 pages across 370 enterprise documents, 8 business domains, and 67 document types, with clear tags differentiating their challenge scenarios. The scalable schema and ground-truth curation pipeline combines independent-system agreement for real documents, known values for synthetic lists, and human verification for forms. We report order-insensitive value F1 for value accuracy, plus two grounding metrics for source traceability: word- and page-level F1. Commercial VLMs perform well on short documents but often truncate record lists on long ones, while coding agents retain higher accuracy at much higher cost. LlamaExtract Agentic Plus ranks first on all three metrics, with accuracy comparable to coding agents at a fraction of the cost. Dataset and evaluation code are available on HuggingFace and GitHub.
Person descriptions in public-safety text can be compact and regular, or distributed across heterogeneous narratives in which evidence boundaries and person ownership are ambiguous. We present POSID, a target-task-unsupervised framework that decomposes person-attribute extraction into candidate localization, provenance-controlled extraction scope, structured recovery and traversal, and person association, without supervised training or fine-tuning on the target task. We evaluate six localization configurations under two traversal policies across three real-use cohorts and two controlled synthetic diagnostics. On structurally regular descriptions, the strongest configurations reach 1.000/.972 Attr-Only (AO)/Attr-Value (AV) F1 on Compact500. Across the real-use cohorts, every RE-containing configuration benefits from conservative early stopping, while semantic-only retrieval can benefit from continued traversal when localization is less constrained. On the heterogeneous multi-person Challenge14 cohort, the best automatic AO and AV results are .500 and .382; replacing automatic candidates with verified evidence raises the frozen extractor to .522/.410 under early stopping, showing that localization is one of the material bottlenecks. We further introduce provenance-aware scoring for collective and underspecified evidence and a separate person-aware diagnostic for ownership and fragmentation. Together, the results establish a measurable operating regime for inspectable person-attribute extraction and distinguish errors due to localization, traversal, structured recovery, and attribution. Code: https://github.com/ksolaiman/hart-posid.
KMA Solaiman
Department of Computer Science, Purdue University, West Lafayette, IN, USA