VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding

• URL: http://arxiv.org/abs/2601.05125v1
• Date: Thu, 08 Jan 2026 17:15:15 GMT
• Title: VERSE: Visual Embedding Reduction and Space Exploration. Clustering-Guided Insights for Training Data Enhancement in Visually-Rich Document Understanding
• Authors: Ignacio de Rodrigo, Alvaro J. Lopez-Lopez, Jaime Boal,
• Abstract summary: VERSE helps uncover the visual features associated with error-prone clusters.<n>On-premise models such as Donut and Idefics2, when optimized with VERSE, surpass the performance of solutions like GPT-4 and Pixtral.
• Score: 2.7273279761148976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work introduces VERSE, a methodology for analyzing and improving Vision-Language Models applied to Visually-rich Document Understanding by exploring their visual embedding space. VERSE enables the visualization of latent representations, supporting the assessment of model feasibility. It also facilitates the identification of problematic regions and guides the generation of synthetic data to enhance performance in those clusters. We validate the methodology by training on the synthetic MERIT Dataset and evaluating on its real-world counterpart, MERIT Secret. Results show that VERSE helps uncover the visual features associated with error-prone clusters, and that retraining with samples containing these features substantially boosts F1 performance without degrading generalization. Furthermore, we demonstrate that on-premise models such as Donut and Idefics2, when optimized with VERSE, match or even surpass the performance of SaaS solutions like GPT-4 and Pixtral.

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