SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
Summary
SimpleMemVLA introduces a simple memory mechanism for Vision-Language-Action models by feeding intact timestamped video history into a pretrained VLM backbone, achieving state-of-the-art results on long-horizon manipulation tasks without dedicated memory modules.
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Paper page - SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models
Source: https://huggingface.co/papers/2609.05533 Authors:
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Abstract
SimpleMemVLA achieves long-horizon manipulation by feeding intact timestamped video history directly into a pretrained VLM backbone and using hidden states to inform a flow-matching action head, outperforming dedicated memory modules.
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, aVLAwithout a dedicated memory module. It keeps the sampled history intact and passes it to the backbone in thetimestamped videoformat the backbone was pretrained to process; thehidden statesof a generated sub-task then form the only channel from history to a standardflow-matching action head. Since consecutive decisions share most of their history, prefilling the shared prefix during action execution keeps latency close to a single-frameVLA. SimpleMemVLAsets a new state of the art on fourmemory benchmarkswithout cost on general-purpose control. Holding the backbone and training setup fixed, it outperforms retrieval, compression and recurrent-state mechanisms by a wide margin, andcausal interventionsconfirm that the policy genuinely reads its history. Code available at https://github.com/wadeKeith/SimpleMemVLA
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