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README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+task_categories:
+- visual-question-answering
+language:
+- en
+tags:
+- abstract
+- visual
+- reasoning
+- real-world
+size_categories:
+- 10K<n<100K
+pretty_name: SpaCE-Eval
+
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: "data/*.parquet"
+
+---
+# SpaCE-Eval: A Benchmark for Real-World Multi-Modal Reasoning
+
+Welcome to the official codebase of SpaCE-Eval! 
+
+
+The [paper](https://openreview.net/forum?id=VAEkLS9VBr&noteId=QSQY2kkQHy) is accepted to ICLR 2026.
+
+
+Code can be downloaded at:
+https://github.com/xuyou-yang/SpaCE-Eval
+## About the Benchmark
+
+This benchmark provides a comprehensive evaluation of MLLMs across the following categories:
+- Spatial Reasoning
+- Commonsense Knowledge
+- Environment Interaction
+
+The dataset consists of newly created diagrams with image-question pairs, carefully curated through a standardized annotation and filtering pipeline.
+
+
+### Citation
+
+```bibtex
+@inproceedings{yang2026spaceeval,
+  title     = {SpaCE-Eval: A Benchmark for Real-World Multi-Modal Reasoning},
+  author    = {Yang, Xuyou and Zhao, Yucheng and Zhang, Wenxuan and Koh, Immanuel},
+  booktitle = {Proceedings of the International Conference on Learning Representations (ICLR)},
+  year      = {2026}
+
+}

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