Темы могут пересекаться: одна работа учитывается в каждой своей теме, но только один раз в общем числе. Связь с темой не доказывает качество или воспроизводимость.
Оригинальная аннотация: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. A natural next step is to extend them from Single-Block Diffusion (SingleBD) to Multi-Block Diffusion (MultiBD), where a running-set of consecutive blocks is decoded concurrently for inter-block parallelism. However, existing BD-LMs are mostly trained under teacher forcing, where the model observes only one noisy block conditioned on a clean prefix. While the recent diffusion forcing strategy introduces visibility among multiple noisy blocks, its training states stil
Полезно для: Не указано · Ограничение: Не указано
2026-06-27T21:21:53+00:00 · Ускорение инференса · Источник
Оригинальная аннотация: Diffusion language models (DLMs) have recently emerged as a promising alternative to conventional autoregressive language models. By generating multiple tokens in parallel during each denoising step, they offer higher inference throughput while maintaining competitive quality. However, realizing these throughput gains while meeting latency SLOs in a serving system requires addressing challenges introduced by DLMs' unique characteristics. These include navigating the speed-quality tradeoff created by confidence-based denoising, choosing appropriate parallelization levels across model insta
Полезно для: Не указано · Ограничение: Не указано
2026-06-09T08:06:22+00:00 · Ускорение инференса, Длинный контекст · Источник
Оригинальная аннотация: Diffusion large language models (dLLMs) re-encode the entire prefix at every denoising step, causing recomputation that scales quadratically with context length and becomes prohibitive for long-context scenarios. We propose Prefilling-dLLM, a training-free prefill-decode disaggregation framework for dLLMs that partitions the prefix into N chunks, caches their KV representations once, and selects the top-K most relevant chunks with intra-chunk token sparsity for decoding, showing that sparse prefilling can outperform dense attention while reducing per-step complexity from quadratic in t
Полезно для: Не указано · Ограничение: Не указано
2026-06-03T14:56:42+00:00 · Маскированная / дискретная диффузия, Ускорение инференса · Источник
Оригинальная аннотация: Diffusion large language models (DLLMs) enable non-autoregressive generation by iteratively denoising corrupted token sequences with bidirectional context. Despite their ability to update multiple positions in parallel, inference remains costly due to the many denoising steps required for high-quality generation. We propose SAID, a Scaffold-Aware Iterative Decoding framework that accelerates DLLMs by reallocating computation across tokens. SAID first spends denoising computation on scaffold tokens to establish the coarse semantic structure, and then completes predictable detail tokens with few
Полезно для: Не указано · Ограничение: Не указано
2026-06-01T17:46:46+00:00 · Маскированная / дискретная диффузия, Ускорение инференса · Источник
Оригинальная аннотация: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference through parallel or blockwise decoding. However, their masked language modeling formulation remains incompatible with standard token-level speculative decoding, one of the most effective acceleration techniques for AR models. In AR decoding, the causal mask preserves temporally valid token-level contexts, enabling a target model to verify multiple drafted tokens in a single forward pass. In contrast, dLLMs rely on mask tokens and bidirectional attentio
Полезно для: Не указано · Ограничение: Не указано
Даты публичного снимка
Сбор, зафиксированный в снимке
Самая новая публикация по данным снимка
Построение снимка
Это сохранённые сведения, а не время последней попытки сборщика или гарантия полноты корпуса.