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This paper introduces Side-by-Side Interleaved Reasoning, a method for controlling disclosure timing in autoregressive models to improve accuracy and efficiency. It demonstrates improved performance on benchmarks using Qwen3 models by interleaving private reasoning with partial disclosures.
SDVG adapts speculative decoding to autoregressive video diffusion, using an image-quality router to achieve up to 2.09× speed-up with 95.7% quality retention on MovieGenVideoBench.
This paper investigates how 1D coarse-to-fine token structures in autoregressive models improve test-time search efficiency compared to classical 2D grid tokenization. The authors show that such ordered tokens enable better test-time scaling and even training-free text-to-image generation when guided by image-text verifiers.
OpenAI presents a simple data augmentation technique that enables autoregressive language models to perform fill-in-the-middle (FIM) text generation without harming left-to-right performance, with extensive ablations and best practices provided for training such models.
Researchers explore a data generation pipeline using domain randomization and procedurally generated objects to train a deep neural network for robotic grasp planning. The proposed autoregressive model achieves >90% success on unseen objects in simulation and 80% in the real world, despite being trained only on random simulated objects.
OpenAI researchers present a Variational Lossy Autoencoder (VLAE) that combines VAEs with neural autoregressive models (RNN, MADE, PixelRNN/CNN) to learn controllable global representations, achieving state-of-the-art results on MNIST, OMNIGLOT, and Caltech-101 Silhouettes density estimation tasks.