NeuroFlow claims 55.8x video inference speedup on SigLIP 2

Ynnk-Research published NeuroFlow, a PyTorch implementation for EMA-Gated Temporal Sequence Compression in Vision Transformers. This technology aims to optimize video inference by reducing computational load by up to 55.8x by identifying and eliminating redundant 'stationary asphalt' tokens before the encoder, while maintaining embedding fidelity. The toolkit includes multiple architectures, with Architecture C offering a training-free option that achieves 71.55% zero-shot top-1 accuracy at 84% token sparsity without modifying model weights.