Add model card, robotics pipeline tag, and links to paper/code

This PR improves the model card for **EventVLA**:
- Adds `pipeline_tag: robotics` to the YAML metadata.
- Links to the paper ([arXiv:2606.20092](https://huggingface.co/papers/2606.20092)), project page, and the official GitHub repository.
- Provides a description of the model and repository structure to make it easier for users to navigate the checkpoints.
- Adds the official BibTeX citation.

Please let me know if you would like any changes!

README.md

@@ -1,3 +1,42 @@
 ---
 license: apache-2.0
+pipeline_tag: robotics
 ---
+
+# EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies
+
+EventVLA is an end-to-end vision-language-action framework designed for long-horizon robotic manipulation tasks. It introduces an event-driven visual evidence memory mechanism to address memory bottlenecks when task-relevant cues become occluded or unobservable over time.
+
+- **Project Page:** [ganlin-yang.github.io/EventVLA.github.io](https://ganlin-yang.github.io/EventVLA.github.io/)
+- **Repository:** [GitHub - InternRobotics/EventVLA](https://github.com/InternRobotics/EventVLA)
+- **Paper:** [EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies](https://huggingface.co/papers/2606.20092)
+- **Dataset:** [RoboTwin-MeM on Hugging Face](https://huggingface.co/datasets/ganlinyang/RoboTwin-MeM)
+
+## Model Description
+
+EventVLA addresses the memory constraints of standard Vision-Language-Action (VLA) policies by employing a dynamic Keyframe Evidence Memory (KEM) module alongside foundational visual anchors. KEM predicts future keyframe probabilities from latent embeddings to store sparse, task-critical visual events, preserving visual evidence before it is lost or obscured.
+
+This repository contains the trained weights evaluated on two benchmarks:
+* **RoboTwin-MeM**: Checkpoints for evaluation on the eight RoboTwin-MeM tasks.
+* **RMBench**: Checkpoints for evaluation on the RMBench benchmark.
+
+Each release directory contains:
+- `pytorch_model.pt`
+- `config.yaml`
+- `dataset_statistics.json`
+- `summary.jsonl`
+
+Please refer to the [official GitHub repository](https://github.com/InternRobotics/EventVLA) for instructions on installation, training, and evaluation.
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@article{yang2026eventvla,
+  title={EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies},
+  author={Yang, Ganlin and Tu, Zhangzheng and Yang, Yuqiang and Mao, Sitong and Dong, Junyi and Chen, Tianxing and Peng, Jiaqi and Xiong, Jing and Cao, Jiafei and Dai, Jifeng and others},
+  journal={arXiv preprint arXiv:2606.20092},
+  year={2026}
+}
+```