Update dataset card: License, task category, paper link, and abstract (#2)

- Update dataset card: License, task category, paper link, and abstract (ecb6e3286c41afae5da4df69f7ef998924022f6b)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,10 +1,13 @@
 ---
-license: mit
+language:
+- en
+license: cc-by-nc-4.0
+size_categories:
+- 1K<n<10K
 task_categories:
+- image-text-to-text
 - visual-question-answering
 - multiple-choice
-language:
-- en
 tags:
 - vision-language
 - multimodal
@@ -15,8 +18,6 @@ tags:
 - graph-theory
 - semantic-equivalence
 - VLM
-size_categories:
-- 1K<n<10K
 dataset_info:
   features:
   - name: task
@@ -140,15 +141,15 @@ configs:
 *[CSSLab](https://csslab.cs.toronto.edu/), Department of Computer Science, University of Toronto*  
 *[COLM '25] Second Conference on Language Modeling*
 
-- **Paper**: [OpenReview](https://openreview.net/pdf?id=lI4LgGv4sX)
-- **Leaderboard**: [SEAM Benchmark](https://lilv98.github.io/SEAM-Website/)
+- **Paper**: [Paper](https://huggingface.co/papers/2508.18179)
+- **Project Page / Leaderboard**: [SEAM Benchmark](https://lilv98.github.io/SEAM-Website/)
 - **Code**: [GitHub](https://github.com/CSSLab/SEAM)
 
 ![Overview](https://lilv98.github.io/SEAM-Website/static/images/main.png)
 
 ## Abstract
 
-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 with 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 notations 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.
+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.
 
 ## Key Features
 
@@ -239,13 +240,12 @@ Each sample contains:
 
 SEAM enables three types of evaluation:
 
-1. **Language**: Models receive only textual notation
-2. **Vision**: Models receive only visual representation  
-3. **Vision-Language**: Models receive both notation and image
+1.  **Language**: Models receive only textual notation
+2.  **Vision**: Models receive only visual representation  
+3.  **Vision-Language**: Models receive both notation and image
 
 The semantic equivalence across modalities allows for direct comparison of reasoning capabilities and cross-modal agreement analysis.
 
-
 ## Citation
 
 ```bibtex
@@ -266,5 +266,4 @@ The semantic equivalence across modalities allows for direct comparison of reaso
       primaryClass={cs.AI},
       url={https://arxiv.org/abs/2508.18179}, 
 }
-```
-
+```