VVS accelerates visual AR image generation by partially skipping verifications in speculative decoding, achieving 2.8x fewer target forward passes while preserving competitive quality.
Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901
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PhysiGen reduces interpenetration in text-driven 3D human interaction generation by simplifying meshes to geometric primitives for fast collision detection and guiding optimization with collision regions.
Re²MoGen generates open-vocabulary motions via MCTS-enhanced LLM keyframe planning, pose-prior optimization with dynamic temporal matching fine-tuning, and physics-aware RL post-training, claiming SOTA performance.
Neuro-Oracle distills longitudinal MRI changes into trajectory vectors via a 3D Siamese encoder, retrieves similar cases, and generates LLM-based prognoses, achieving AUC 0.834-0.905 on a resection-type proxy task versus 0.793 for single-timepoint baseline.
LLaMo scales pretrained LLMs for unified motion-language tasks by encoding motion into continuous causal latents and adding a flow-matching head for real-time autoregressive generation and captioning.
Linear attention delivers significant computational savings in multimodal transformers and follows the same scaling laws as softmax attention on ViT models trained on LAION-400M with ImageNet-21K zero-shot validation.