Do robot VLAs really need a separate vision encoder? From Pixels to Actions: Towards Native Vision-Language-Action Policies NEO-VLA is a 2B encoder-free VLA built directly on pixels and words. 🧱 Highlight 1: a unified backbone with no encoder Instead of CLIP or SigLIP, a light convolutional patch embedder feeds image tokens into the same 40-layer decoder as the text. On LIBERO average with a frozen backbone, Qwen3-VL-2B scores 79.5 and NEO-VLA 90.9. 🔍 Highlight 2: the action expert reads intermediate layers A flow-matching expert attends to keys and values from middle layers of the backbone. With a frozen backbone, reading early layers is strongest (94.9); after fine-tuning, a layer mix reaches 95.3. But wiring the expert to an encoder-based model's intermediate features drops frozen performance from 79.5 to 64.9, so the pretraining seems to matter. 🧠 Highlight 3: co-training with understanding data Action-only fine-tuning gives 10.7% on RoboTwin 2.0 and loses the ability to output text. Mixing in understanding data reaches 69.4% and keeps VQA scores within about 1.5 points. LIBERO hits 97.4, but it's weak when objects or goals change (LIBERO-Pro: position 3.0, task 6.3), and the real-robot evaluation is small: 3 tasks, 20 trials each. #VLA# #Robotics#