Researchers propose AlignVAR, a visual autoregressive framework for image super-resolution that addresses critical consistency problems in existing VAR models. The approach combines spatial consistency autoregression and hierarchical consistency constraints to achieve 10x faster inference with 50% fewer parameters than leading diffusion-based methods.
Researchers introduce SOLACE, a post-training framework that replaces external reward models with an internal self-confidence signal derived from how accurately a text-to-image model recovers injected noise. The method enables fully unsupervised optimization and shows measurable improvements in compositional generation, text rendering, and text-image alignment.
Researchers introduced MeanFlowSE, a generative speech enhancement model that eliminates the computational bottleneck of multistep inference by learning average velocity over finite intervals rather than instantaneous velocity fields. The single-step approach achieves comparable quality to multistep baselines on VoiceBank-DEMAND while requiring substantially lower computational cost and no knowledge distillation.
Researchers propose LaDiR (Latent Diffusion Reasoner), a framework that combines variational autoencoders and latent diffusion models to improve LLM reasoning. The approach encodes reasoning steps into continuous latent representations, enabling iterative refinement and parallel generation of diverse solutions beyond traditional autoregressive decoding.