Add dataset card for MBench with metadata and usage instructions

This PR initializes the dataset card for MBench. It includes:
- YAML metadata specifying the `text-to-video` task category and the MIT license.
- Links to the paper, project page, and the official GitHub repository.
- A Python code snippet demonstrating how to use the MBench API for loading and evaluating, as extracted from the GitHub README.
- Citation information.

README.md

@@ -0,0 +1,52 @@
+---
+license: mit
+task_categories:
+- text-to-video
+tags:
+- video-world-models
+- evaluation
+- benchmark
+---
+
+# MBench: A Comprehensive Benchmark on Memory Capability for Video World Models
+
+[**Project Page**](https://peanutup.github.io/MBench-project/) | [**Paper**](https://huggingface.co/papers/2606.00793) | [**GitHub**](https://github.com/study-overflow/MBench) | [**Leaderboard**](https://huggingface.co/spaces/study-overflow/MBench_Leaderboard)
+
+**MBench** is a comprehensive benchmark dedicated to quantifying and evaluating the memory capability of video world models. It systematically decomposes the memory capability of video world models into three hierarchical and complementary core dimensions: entity consistency, environment consistency, and causal consistency, which are further refined into 12 quantifiable sub-dimensions for comprehensive characterization of long-term memory.
+
+The benchmark evaluates models under two complementary settings:
+- **MBench-A**: Action-conditioned, for action-conditioned world models.
+- **MBench-T**: Text-segment-conditioned, for long-video text continuation models.
+
+## Python API Usage
+
+Below is a quick example of how to load MBench data and evaluate metrics using the official Python API:
+
+```python
+from mbench.core.pipeline import run_eval
+from mbench.core.registry import metric_registry, adapter_registry, aggregator_registry
+import mbench.cli; mbench.cli._bootstrap()    # register all built-in plugins
+
+adapter = adapter_registry.get("mbench-dir")
+items = adapter.load("data/MBench-A-Setup", subsets=["environment"], limit=4)
+metric = metric_registry.get("mbencha.environment.spatial_epipolar")
+aggregator = aggregator_registry.get("mean")
+
+summary = run_eval(items, [metric], aggregator, "runs/python_api_demo")
+```
+
+For more detailed setup instructions (including CLI execution, data preparation, and evaluation of specific dimensions), please refer to the [GitHub Repository](https://github.com/study-overflow/MBench).
+
+## Citation
+
+```bibtex
+@article{zhang2026mbench,
+  title         = {MBench: A Comprehensive Benchmark on Memory Capability for Video World Models},
+  author        = {Zhang, Shengjun and Zhang, Zhang and Huang, Simin and Tang, Zhenyu and Wang, Hanyang and Dai, Chensheng and Chen, Min and Li, Yifan and Li, Yuxin and Chen, Yingjie and Liu, Hao and Li, Chen and Duan, Yueqi},
+  year          = {2026},
+  eprint        = {2606.00793},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV},
+  url           = {https://arxiv.org/abs/2606.00793}
+}
+```