arXiv:2605. 10938v2 Announce Type: replace-cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2606. 00094v1 Announce Type: cross Abstract: Image generative models aim to sample data points from the underlying data manifold, a task that requires learning and decoding a dense, low-dimensional, and compact parameterization space.
arXiv:2606. 20076v1 Announce Type: cross Abstract: Latent Diffusion Models (LDMs) have become dominant in visual synthesis, but their quality-compute trade-off is largely constrained by the tokenizer's fixed compression ratio.
Diffusion Language Models (DLMs) have recently achieved substantial progress in natural language generation tasks. Recent research demonstrates that adaptive token generation ordering can significantly improve performance in mathematical reasoning and code synthesis applications.
arXiv:2602. 05951v2 Announce Type: replace-cross Abstract: Flow matching has recently emerged as a promising alternative to diffusion-based generative models, particularly for text-to-image generation.
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
arXiv:2601. 05713v2 Announce Type: replace-cross Abstract: Understanding how large language models (LLMs) represent natural language is a central challenge in natural language processing (NLP) research.