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arXiv Computer Vision By Weichen Fan, Haiwen Diao, Penghao Wu, Siyuan Hu, Ziqi Huang, Tianqi Liu, Mingze Sun, Ziyi Wang, Zhongang Cai, Hanming Deng, Quan Wang, Lei Yang, Lewei Lu, Fangzhou Hong, Zhaoxi Chen, Dahua Lin, Ziwei Liu

From Pixels to Actions - Towards Native Vision-Language-Action Policies

Read the original on arXiv Computer Vision →

The paper introduces NEO-VLA, an encoder‑free vision‑language‑action framework that tokenizes images with lightweight patch convolutions and lets visual and language features evolve together throughout the backbone. By allowing a flow‑matching action expert to access instruction‑conditioned visual representations at multiple depths, NEO‑VLA consistently outperforms encoder‑based counterparts across diverse manipulation tasks and distribution shifts. Layer‑wise probing reveals that action‑critical signals peak early in the visual hierarchy, underscoring the benefit of accessing representations across the computational pipeline rather than relying on a fixed encoder output.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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