MVGraph: Benchmarking LVLMs on Evidence Composition across Multi-View Visual Graph
We introduce MVGraph, a benchmark for evaluating large vision-language models (LVLMs) on multi-graph reasoning where the evidence required by a query is distributed across multiple interrelated visual graph views. Although such evidence commonly resides in source-specific graphs, temporal snapshots, or local observations, existing graph reasoning benchmarks remain confined to a single self-contained structure, assessing only graph perception and reasoning while leaving the alignment and composition of evidence across views underexplored. MVGraph comprises 8k instances spanning 8 \textit{graph-theoretic} tasks, abstracted from two multi-graph scenarios (partial and temporal graph views). E...
| Authors | Yingjie Zhu, Han Cheng, Xuefeng Bai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 9 October 2026 |
| Last updated at source | 9 October 2026 |
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