Tag
提出了异步流映射语言模型（aFMLM），通过token级时间调度将预测顺序编码进单步生成的隐藏计算中，在Sudoku和GSM8K代码生成任务上刷新了一步生成的最先进性能。
OBB-PBD extends Parallel Box Decoding from horizontal to oriented bounding boxes via double-angle parameterization, validating the design with shuffle controls and leakage probes, and improving rotated IoU on the aerial vehicle visual grounding benchmark from 0.1253 to 0.303.
The paper presents Lafa, an 8B-parameter blockwise continuous flow language model trained with under 30B tokens that matches or surpasses autoregressive quality while achieving 2.54x inference throughput via self-speculative decoding and blockwise parallel decoding.
该论文提出ORL（On-policy Residual Learning）框架，通过熵收缩目标与异步残差捕获等系统级协同设计，使扩散语言模型（DLM）在相近质量下将吞吐量提升至约6.9×，在单请求解码速度上超越自回归模型的推测解码方案。
Yohei Nakajima 转发介绍 Next-Concept Modeling (NCM)：一种快速、并行、由粗到细的文本生成新范式，在摘要等下游任务上相比同规模扩散语言模型最快可达 31 倍加速。
DiffDiffusion提出一种受扩散模型启发的LLM框架，通过预测编辑路径而非重新生成整个代码段来实现高效代码编辑，在公共基准上比自回归基线快2.78倍，并在生产环境中将P50模型延迟降低39.0%。
Introduces Gumbel Straight Flow, a method for distilling autoregressive language models into one-step flow maps that enable parallel sampling.
FastGuide 是一种自适应的并行与自回归混合解码方法，通过复用奖励模型反向传播计算、利用 KV 缓存和稀疏注意力重计算来加速扩散大语言模型的梯度奖励引导，在三个奖励基准上实现最高 4.4× 加速同时保持相近生成质量。
This paper proposes LUDI, a less uniform diffusion language modeling framework that fixes over-uniform training objectives and condition-target confusion in uniform diffusion LMs, enabling a 7B-scale UDLM with 3x-token-per-step speedup over autoregressive decoding and competitive complex reasoning performance.
GroundAnything 提出一个 4B 参数的视觉定位基础模型，通过块状去噪协调并行解码与精确定位，在 30 个 grounding 基准上取得同类模型新 SOTA（72.42%），并借助自投机解码实现 4.51 倍加速。
WaveFront Decoding introduces a training-free self-speculative decoding framework for looped language models that reduces latency by concurrently batching drafting and verification, achieving up to 4.81x speedup on Huginn-3.5B.
Flash-dLLM is a training-free inference acceleration framework for diffusion LLMs that uses IO-aware KV caching and parallel decoding to achieve significant speedups and memory efficiency improvements.
The paper introduces a two-stage LLM pipeline using fine-tuned Qwen3-4B with Hyper-Parallel Decoding to extract purchase-discriminative attributes in e-commerce, achieving 85% accuracy with 92% cost reduction.
Alibaba PAI releases LoRA checkpoints for accelerating MiniMax-H3 video generation using Parallel Decoding Distillation, enabling efficient inference in 8 steps.
The paper introduces Consistency Forcing (CForce), a distillation technique for diffusion large language models that improves parallel decoding by aligning early-stage predictions with later stages, enhancing speed-quality trade-offs.
Proposes Ripple-Pivot Search, a training-free decoding method for diffusion large language models that proactively commits mid-entropy pivot positions to reduce uncertainty and accelerate parallel decoding, achieving 4-10x speedup.
PaDoc introduces a layout-grounded parallel decoding method for end-to-end document parsers, decoupling layout and content decoding to reduce decoding depth and improve throughput. It achieves state-of-the-art results on OmniDocBenchFull and significantly speeds up inference compared to sequential baselines.
NVIDIA introduces Parallel Decoding Distillation (PDD) for accelerating image and video generation, enabling high-quality outputs with fewer neural function evaluations on models like LTX-2.3 and Wan2.1-14B.
Parallel Decoding Distillation (PDD) is a trajectory-based distillation method that accelerates image and video generation by predicting multiple denoising steps per network evaluation, achieving state-of-the-art performance with 4-8 NFEs on models like LTX-2.3, Wan14B, and Qwen-Image.
Proposes DC-Leap, a training-free framework that accelerates diffusion large language models by introducing dynamic contiguous verification and draft-guided decoding, achieving up to 105× speedup with comparable generation quality.