xyc99/SpatiaLQA
SpatiaLQA Dataset Summary SpatiaLQA is a benchmark for evaluating spatial logical reasoning in vision-language models.It focuses on reasoning abilities in real-world indoor environments, including spatial relations among objects and logical dependencies in multi-step tasks. This dataset is introduced in the paper: SpatiaLQA: A Benchmark for Evaluating Spatial Logical Reasoning in Vision-Language ModelsAuthors: Yuechen Xie, Xiaoyan Zhang, Yicheng Shan, Hao Zhu, Rui… See the full description on the dataset page: https://huggingface.co/datasets/xyc99/SpatiaLQA.
| Author / organization | xyc99 |
|---|---|
| License (as declared) | mit |
| Downloads | 264 (as reported by Hugging Face) |
| Likes | 3 |
| Created | 5 March 2026 |
| Last updated at source | 9 March 2026 |
Popularity, last 2 days
264 now (-3 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: SpatiaLQA Dataset Summary SpatiaLQA is a benchmark for evaluating spatial logical reasoning in vision-language models.It focuses on reasoning abilities in real-world indoor environments, including spatial relations among objects and logical → SpatiaLQA Dataset Summary SpatiaLQA is a benchmark for evaluating spatial logical reasoning in vision-language models.It focuses on reasoning abilities in real-world indoor environments, including spatial relations among objects and logical
Changes detected by Silytics between refreshes of the source.
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.