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LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph learning remains unexplored. Graph learning is a natural setting for such systems because useful evidence may arise from heterogeneous local, long-range, global structural...
OMG-VLM leverages a pretrained VLM as a shared backbone and introduces structure-aware graph adapters that integrate neighborhood information while remaining compatible with the VLM's native embedding space, enabling effective learning over text-attributed, image-attributed, and multimodal-attributed graphs within a si...
GraphVerse is introduced, a unified benchmark that jointly evaluates perception, visual reasoning, and text-based graph reasoning in MLLMs under both single-image and paired-image settings and proposes VGR-Score, a process-sensitive metric that evaluates reasoning quality beyond final-answer accuracy.
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