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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.