arXiv AI August 28, 2026 By Fan Yuan, Yuchen Yan, Yifan Jiang, Haoran Zhao, Tao Feng, Jinyan Chen, Yanwei Lou, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts

Read the original on arXiv AI →

The paper introduces GSM8K‑V, a new benchmark that converts the GSM8K grade‑school math word problems into multi‑image sequences while preserving semantic equivalence. It requires vision‑language models to extract quantities through visual perception and reconstruct reasoning chains by integrating implicit cues across scenes. Evaluation of 34 VLMs shows a large modality gap: most models score above 90 % on text, but the best reaches only 59 % on GSM8K‑V, highlighting a distinct challenge in implicit visual inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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