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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.