In one sentence: XPeng's TuringViT tackles all three long-standing problems of vision Transformers — attention, data, and resolution — at once. Hybrid linear attention + VISTA-Curation data governance + native dynamic resolution, using just one-tenth of SigLIP2's training data to reach SOTA, with inference throughput at 1536² resolution running 2–3x faster than mainstream open-source baselines. The vision encoder is becoming the new battlefield in the VLM/VLA era. After LLMs ate the text side clean, the first stop for every multimodal system is this "retina" — it determines whether on-device can run and whether cross-task can unify. On July 21, XPeng released TuringViT (arXiv:2606.24253, site turingvit.github.io), pulling the vision encoder from "brute-force scaling" back to "accountable engineering economics". The three axes Turing linear attention: standard softmax attention scales quadratically with vision tokens, hitting a cost ceiling at high resolution and multi-view video frames. TuringViT proposes a "5 linear-attention layers + 1 multi-head attention layer" hybrid Turing block, letting linear attention handle the main global aggregation, periodically inserting a small number of MHA layers to preserve token-level interaction precision, paired with sequence-length-aware normalization and input-dependent gating to avoid detail smoothing. VISTA-Curation data governance: mainstream ViTs rely on tens of billions of image-text pairs, but data noise is high, alignment is poor, and the marginal return from piling on quantity diminishes. TuringViT doesn't race on quantity, it races on per-sample supervision value — multi-model, multi-prompt candidate caption generation + unified contrastive-pool scoring in three steps of filtering. The result: using only 0.85B image-text pairs (10% of SigLIP2-L's training data), it hits 83.6% average accuracy on six zero-shot benchmarks including ImageNet-1K, beating open-source baselines trained on 10B data. Native dynamic resolution training: MIM-distillation initialization → constrained dynamic resolution (long edge 256–512) → native resolution released → image-text-video mixed training, paired with 2D RoPE — different-sized inputs need no extra interpolation. Key numbers: at 1536² resolution, TuringViT-18L's inference throughput is 3.04x that of Seed1.5-ViT, 2.16x that of SigLIP2-ViT-L — a latency dividend directly available to vehicle and robot embedded compute. The vision encoder has reached the same inflection point LLMs did in 2026: the marginal return from piling on data is visibly decaying, and the real leverage is breaking attention's quadratic blowup + an order-of-magnitude leap in data governance + native multi-resolution pretraining. TuringViT isn't the end, but it represents the direction of vision encoders moving from "scaling races" to "structure + data + training combo punches".