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DiffuSpace released Clover-35B-A3B-Preview, an open-source block-diffusion language model with a 35B MoE backbone (~3B active params), 128K context, 680 tok/s on GB300, 73.6% SWE-bench Verified, and Apache 2.0 licensing.
Mercury 3 是目前发布的最强扩散式大语言模型，宣称以 1,000+ tokens/s 的速度运行，比竞争对手快 4–8 倍，并在性能上超越 GPT-6 Luna 和 Claude Haiku 5.5，实现 40 倍扩展而不降低速度。
Inception Labs 发布 Mercury 3，这是迄今最强的扩散式 LLM，在多项基准上超越 GPT-6 Luna 和 Claude Haiku 5.5，同时以 1000+ tokens/秒的速度快 4-8 倍。模型规模比 Mercury 1 大 40 倍以上，支持 100 万 token 上下文，并以极具竞争力的价格面向生产环境开放。
研究人员提出 ORL（On-Policy Residual Learning）与自研推理引擎 dlmrt，使扩散语言模型在单张 RTX 5090 上达到 2,788 tokens/s，8×B200 上达 7,708 tokens/s，端到端吞吐提升最高 6.9 倍且质量不降，模型、训练代码与推理引擎全部开源。
AutoIndexer提出了一种通过"Chain of Edits"训练因果生成模型的新解码机制，使模型能像文本编辑器一样在生成后通过插入、替换和删除灵活修改先前输出，同时在标准LLM基准上不损失性能。
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。
Inception Labs releases Mercury 2.5, a diffusion-based language model with improved intelligence, speed, and cost-efficiency for production use in search, voice, and coding.
Mercury 2.5 is announced as the most capable diffusion language model, with a 40% increase in intelligence over Mercury 2, operating at over 1,100 tokens/sec on NVIDIA GPUs, and optimized for production with low latency and cost.
This paper investigates why diffusion LLMs fail at reasoning tasks: unconstrained token commitment freezes answers early and collapses to answer-only outputs. The authors identify commitment order as the root cause and propose a training-free, frontier-gated decoding intervention that recovers performance while preserving parallel decoding.
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.
inclusionAI released LLaDA2.2-flash, a 100B MoE diffusion LLM built for agents with Levenshtein Editing, 128K context, and up to 2.3× higher throughput, achieving strong scores on agentic benchmarks like τ²-Bench and PinchBench under Apache 2.0.
This paper proposes Dynamic-dLLM, a training-free framework that accelerates diffusion large language models by dynamically allocating cache-update budgets and calibrating decoding thresholds, achieving over 3x speedup on models like LLaDA and Dream while maintaining performance.
Introduces d-OPSD, the first on-policy self-distillation framework for diffusion large language models, using suffix conditioning and step-level supervision to outperform RLVR and SFT baselines on reasoning benchmarks.
This paper presents llada.cpp, an NPU-aware inference framework for accelerating diffusion large language models (dLLMs) on smartphones. It introduces three techniques—Multi-Block Speculative Decoding, Dual-Path Progressive Revision, and Swap-Optimized Memory Runtime—to align dLLM inference with mobile NPU characteristics, achieving 17-42x latency reduction over CPU baseline.
TIDE is a lossless inference system for diffusion large language models that leverages temporal stability of expert activations to reduce I/O overhead and computation, achieving up to 1.4-1.5x throughput improvements on single GPU-CPU systems.
This paper introduces Parallel Speculative Decoding (PSD), a training-free framework that accelerates diffusion LLM inference by jointly improving spatial and temporal efficiency, achieving up to 5.5× tokens per forward pass with comparable quality to greedy decoding.
dLLM is an open-source library that converts any autoregressive LLM into a diffusion LLM, enabling parallel decoding and faster text generation.
This article questions why major LLM providers are not investing in Diffusion LLMs despite recent advancements like Mercury 2. It explores potential fundamental issues or hardware bottlenecks hindering broader adoption.
R²-dLLM introduces spatio-temporal redundancy reduction techniques that cut diffusion LLM decoding steps by up to 75% while preserving generation quality, addressing a key deployment bottleneck.