A new arXiv preprint reviews how diffusion-based large language models can be applied to code generation, an area currently dominated by left-to-right autoregressive decoding. The authors examine the limitations of standard autoregressive generation and assess whether diffusion approaches offer advantages for producing source code. The work is a replacement submission (v3) to the cs.AI category.
A new arXiv paper introduces DenMark, a semantic watermarking approach that embeds signals in the meaning of generated text rather than in specific token choices. This design aims to survive paraphrasing and other edits that preserve meaning, which typically break surface-level watermarks. The work focuses on diffusion language models, a class largely unaddressed by existing semantic watermarking methods built for autoregressive models.
A new arXiv paper introduces LLaDA-UI, a method that adapts diffusion large language models to vision-language agents that operate graphical user interfaces. Diffusion decoding generates tokens in parallel blocks and in arbitrary order, which the authors argue suits latency-sensitive GUI tasks. The work positions interface agents as a testbed for this alternative to standard left-to-right generation.
A new arXiv paper introduces DA-DLM, a method for diffusion language models that explicitly captures relationships between tokens during generation. Existing diffusion models denoise masked text by predicting several tokens at once under an assumption of conditional independence, which the authors say weakens coherence. The proposed approach aims to restore those inter-token dependencies.
A revised arXiv paper examines why masked diffusion language models can produce incoherent text blocks: they decode every position in a block in parallel from separate marginal distributions. The authors propose a coupled-noise distillation approach intended to let such models generate a whole block in a single step while keeping the tokens mutually consistent. The work is a research preprint and has not been peer-reviewed.
A new arXiv paper proposes SAC-Copula, a watermarking scheme designed for diffusion language models, which generate text through iterative parallel unmasking rather than left-to-right decoding. The authors argue that existing sampling-based watermarking techniques, which add independent noise at each position, are poorly suited to this parallel process. Their approach instead uses smooth, correlated Gumbel fields intended to preserve output quality while embedding a detectable signal.