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arXiv · 2604.16517

SmoGVLM: A Small, Graph-enhanced Vision-Language Model

Debjyoti Mondal·Rituraj Singh·Subhadarshi Panda
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Abstract

Large vision-language models (VLMs) achieve strong performance on multimodal tasks but often suffer from hallucination and poor grounding in knowledge-intensive reasoning. We propose SmoGVLM, a small, graph-enhanced VLM that integrates structured knowledge with visual and textual modalities, using Graph Neural Networks. We investigate the effects of our method across a range of model sizes, from tiny (1.3B) to large (13B) models. Our results demonstrate that, when trained using our approach, a small model can achieve performance gains upto 16.24%, and surpass its larger counterparts, outperforming larger VLMs and strong fine-tuned baselines. These results highlight the potential of structured knowledge augmentation for efficient, smaller-scale multimodal reasoning systems.

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Debjyoti Mondal, Rituraj Singh, Subhadarshi Panda. 2026-04-15. SmoGVLM: A Small, Graph-enhanced Vision-Language Model. https://arxiv.org/abs/2604.16517

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