Sensetime (China)

NEO-VLA removes the fixed visual encoder from vision-language-action models: images are tokenized by lightweight patch convolutional layers and evolve with language through the backbone, and a flow-matching action expert draws on instruction-conditioned visual representations at different depths. In controlled comparisons the native architecture beats encoder-based counterparts; full fine-tuning narrows but does not erase the gap, and extra scale alone cannot recover it. Layer-wise probing shows action-critical signals often peak before the final visual representation.

NEO-VLA removes the fixed visual encoder from vision-language-action models: images are tokenized by lightweight patch convolutional layers and evolve with language through the backbone, and a flow-matching action expert draws on instruction-conditioned visual representations at different depths. In controlled comparisons the native architecture beats encoder-based counterparts; full fine-tuning narrows but does not erase the gap, and extra scale alone cannot recover it. Layer-wise probing shows action-critical signals often peak before the final visual representation.