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Graph-Guided Neural Visual Geometry for Aerial Registration (G3AR), a graph-guided framework for scalable dense neural geometry that improves pose error and runtime in matched VGGT- and Pi3-backed comparisons, while its DA3 variant achieves the lowest pose error among evaluated neural-geometry methods.
Masked diffusion language models can now achieve 21.8x better compute efficiency than autoregressive models, thanks to binary encoding and index shuffling.
