SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models

Joseph Tang, Difan Jiao, Blair Yang, Ashton Anderson

SEAM: Semantically Equivalent Across Modalities Benchmark for Vision-Language Models: 4 upvotes on Hugging Face Daily Papers, #13 of 17 papers on 2025-08-28. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

Evaluating whether vision-language models (VLMs) reason consistently across representations is challenging because modality comparisons are typically confounded by task differences and asymmetric information. We introduce SEAM, a benchmark that pairs semantically equivalent inputs across four domains that have existing standardized textual and visual notations. By employing distinct notation systems across modalities, in contrast to OCR-based image-text pairing, SEAM provides a rigorous comparative assessment of the textual-symbolic and visual-spatial reasoning capabilities of VLMs. Across 21 contemporary models, we observe systematic modality imbalance: vision frequently lags language in overall performance, despite the problems containing semantically equivalent information, and cross-modal agreement is relatively low. Our error analysis reveals two main drivers: textual perception failures from tokenization in domain notation and visual perception failures that induce hallucinations. We also show that our results are largely robust to visual transformations. SEAM establishes a controlled, semantically equivalent setting for measuring and improving modality-agnostic reasoning.

Paper page on Hugging Face · arXiv

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