arXiv:2601.21647v2 Announce Type: replace-cross Abstract: Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for...
The paper introduces a lightweight evolutionary heuristic scheduler for Diffusion Large Language Models (dLLMs) that optimizes denoising trajectories using CMA-ES. It addresses failure modes of existing confidence-based schedulers—EOS Overflow and Proximal Bias—by integrating multiple heuristic features with a contextual mean-field embedding, requiring only 393 trainable parameters. Evaluations on LLaDA and Dream across reasoning and planning benchmarks show consistent outperformance over strong baselines and recent state‑of‑the‑art methods.
arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.
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:2607.00588v2 Announce Type: replace Abstract: Continuous diffusion language models offer an alternative to autoregressive generation, but their generations may suffer from repetition. We find t...
The paper introduces FINDERS, a diffusion‑language‑model framework that replaces static user encoders with a dynamic routing system. FINDERS uses four operations—denoise, factorize, route, and compress—to create scenario‑adaptive user representations by organizing denoising states into transferable cross‑modal semantics and modality‑specific factors. The authors also build BehaviorQA, a large corpus with explicit behavioral rationales, and demonstrate through extensive experiments and online A/B tests that FINDERS outperforms existing methods across ten industrial scenarios.
The paper introduces Dependency-Aware Revocable Decoding (DARD), a training‑free framework for diffusion large language models that separates tokens into masked, candidate, and unmasked states. DARD verifies candidate tokens using a selective context that excludes less reliable tokens and adaptively regulates their influence on subsequent decoding. Experiments on 12 textual and multimodal benchmarks across three open‑source dLLMs show that DARD improves the speed‑quality Pareto frontier, achieving a 2.71× speedup and a 4.35‑point CIDEr gain over Saber on Flickr30K.
The paper introduces PILL, a new infilling technique for diffusion language models that eliminates the need for a preset initial length and reduces inference overhead. PILL uses probing-based length-free decoding, cutting down on extra forward passes and speeding up generation. Experiments across five diffusion models and eight benchmarks show PILL outperforms the strongest baseline with higher pass rates and BLEU-2 scores while running 1.82× faster.
arXiv:2609.07160v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored...
arXiv:2602. 11133v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative refinement, a process that is often computationally inefficient because many tokens reach stability long before the final denoising step.
arXiv:2507. 08390v5 Announce Type: replace Abstract: Discrete diffusion models have recently emerged as strong alternatives to autoregressive language models, matching their performance through large-scale training.
DiffResearch is an open multi‑agent deep research framework that replaces the traditional autoregressive language model backbone with a diffusion language model (dLLM) across all stages of research, from planning to writing. In experiments on 100 Deep Research Bench tasks, the diffusion backbone achieves roughly six times faster end‑to‑end latency, largely due to significant speedups in planning and long‑form generation. The framework also introduces dFast, a decomposition mode that reads intermediate denoising states to generate subqueries, yielding a median 1.99× speedup without compromising report quality and achieving the best overall score on the RACE rubric. whyItMatters":"The study demonstrates that diffusion language models can dramatically reduce latency in deep research systems, enabling faster interactive use while maintaining or improving output quality."