Research artifacts for Model Consolidation and the Family-Balanced Shared-Core Optimizer for Multi-Source Identity-Document Fraud Detection

This record contains the redistributable research materials supporting “Model Consolidation and the Family-Balanced Shared-Core Optimizer for Multi-Source Identity-Document Fraud Detection.” Version 1 comprises a conventional ZIP archive with one root directory, a top-level README, a file inventory, a SHA-256 checksum list, and a byte-identical copy of the article’s Supplementary Material. The archive provides a single command-line entry point (artifact.py) for integrity verification, executable demonstrations, workflow discovery, public RQ1 and RQ2 analyses, and independent FB-SCO retraining on a user’s own lawful document collection using precomputed 512-dimensional features. Training preserves the reported shared-core and corpus-source-local parameter ownership and supports calibrated checkpoint inference. The record contains frozen configurations, aggregate and non-identifying measurements, source tables, provenance records, and a claim–evidence map. It excludes protected identity-document images, restricted row-level records, credentials, and pretrained weights. Exact manuscript replay requires independent lawful access to the cited datasets and models, as specified in FULL_DATASET_VALIDATION.md; independent retraining is distinguished explicitly from exact replay. Original code and documentation are licensed as identified within the archive. AIForge-Doc-derived aggregate and protocol files retain CC BY-NC-SA 4.0.