arXiv:2610.00497v1 Announce Type: cross
Abstract: We present Gumbel Straight Flow (GSF), a continuous flow map language model that leverages the noise-data coupling of a pretrained autoregressive lan...
By Yeongmin Kim, Arnaud Doucet, Andrew Campbell, Valentin De Bortoli, Thomas Mensink, David Ruhe
arXiv:2608.22898v1 Announce Type: new
Abstract: Diffusion language models (DLMs) alleviate the inherent latency bottleneck of autoregressive (AR) large language models (LLMs), but their degraded gene...
By Hyeongsoo Lim, Jinyoung Kim, Eunseo Seo, Minho Jang, Jiwon Yoon
arXiv:2602. 19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation.
By David Li, Nikita Gushchin, Dmitry Abulkhanov, Eric Moulines, Ivan Oseledets, Maxim Panov, Alexander Korotin
PlaidQ is a 0.7B continuous diffusion language model designed for code generation. By distilling its iterative refinement trajectory into only a few denoising steps—or even a single step—PlaidQ achieves competitive performance with discrete diffusion models while dramatically reducing inference time. The study demonstrates that continuous diffusion can be effectively compressed, enabling efficient and accurate code generation with minimal computational overhead.
By Fred Zhangzhi Peng, Kaiwen Zheng, Anru R. Zhang
Discrete Beckmann Transport Models (DBTM) are introduced as a new type of discrete diffusion and flow model that can generate language in a single step by mapping any point to a fixed point on the simplex vertices. Unlike previous approaches, DBTM eliminates the need for a pretrained teacher model and time conditioning by minimizing a conservation equation directly from data. The model can be partially trained and iterated until convergence, and it can be extended to a partial‑context interpolant that refines outputs with additional function evaluations. Experiments on language modeling and reasoning tasks show that DBTM achieves higher quality and accuracy than existing discrete diffusion and continuous flow baselines.
By Sophia Tang, Shiyi Wang
The paper introduces Untied Self-Conditioning, a sampler that corrects a train–inference mismatch in flow‑matching language models. By dampening redundant directions in the self‑conditioning input and approximating a step‑average prediction from history, the method improves generation quality without retraining. On LangFlow and ELF‑B datasets, it dramatically lowers perplexity and is preferred in the majority of pairwise comparisons.
By Bocheng Li, Linli Xu