---
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}
}
```