FRAGMENT: Factorized Graph Representations for Document Generation and Editing via Entity-Aware Transformations

FRAGMENT is a two‑stage generative framework that models documents as typed relational graphs, factorizing the joint distribution into structure and content components. The first stage, the Architect, uses a causally masked Transformer to autoregressively generate graph topology and spatial relations, while the second stage, the Builder, employs a GATv2-based graph attention network to add bounding boxes, text, and visual style attributes. The framework provides explicit likelihood models for anomaly scoring and supports prompt‑conditioned, entity‑aware editing via cross‑attention.

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• llms
• rag
• fine-tuning