| title | SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models |
|---|---|
| authors | Cheng Yin, Wang Xu, Junpeng Yang, Sikyuen Tam, Hanyu Liu, Yuan Yao, Xiangrui Zeng, Junbo Cui, Yequan Wang, Zhouping Yin, Yankai Lin |
| date | 2026-09-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.05533 |
| affiliation | OpenBMB |
| upvotes | 13 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | Vision-language-action models |
| abstract | Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier. Existing memory mechanisms: retrieval banks, learned compressors, recurrent states must decide what to keep from the past before knowing what a future decision will require. This was motivated by the assumption that minute-scale history is too large to process directly, which modern VLM backbones no longer make true. In this work, we introduce SimpleMemVLA, a VLA without a dedicated memory module. It keeps the sampled history intact and passes it to the backbone in the timestamped video format the backbone was pretrained to process; the hidden states of a generated sub-task then form the only channel from history to a standard flow-matching action head. Since consecutive decisions share most of their history, prefilling the shared prefix during action execution keeps latency close to a single-frame VLA. SimpleMemVLA sets a new state of the art on four memory benchmarks without cost on general-purpose control. Holding the backbone and training setup fixed, it outperforms retrieval, compression and recurrent-state mechanisms by a wide margin, and causal interventions confirm that the policy genuinely reads its history. Code available at https://github.com/wadeKeith/SimpleMemVLA |
cite
@misc{yin2026simplememvla,
title={SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models},
author={Cheng Yin and Wang Xu and Junpeng Yang and Sikyuen Tam and Hanyu Liu and Yuan Yao and Xiangrui Zeng and Junbo Cui and Yequan Wang and Zhouping Yin and Yankai Lin},
year={2026},
eprint={2609.05533},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.05533}
}