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This paper introduces Temporal-Spatial Parallel Decoding (TSPD) and Confidence Extrapolation (CE) to accelerate inference in diffusion-based large language models by dynamically deciding when tokens have converged and forecasting logit trends, reducing unnecessary denoising steps while preserving output quality.
NVIDIA introduces LocateAnything, a unified generative grounding and detection framework that uses Parallel Box Decoding to improve decoding throughput and localization accuracy. This work will be presented at CVPR 2026.
LocateAnything proposes Parallel Box Decoding for unified visual grounding and object detection, decoding geometric elements as atomic units to improve throughput and localization accuracy, supported by a large-scale dataset of 138M samples.
NVIDIA introduces Nemotron-Labs Diffusion, a family of diffusion language models that generate text in parallel and iteratively refine it, offering faster generation and the ability to revise previous tokens.
NVIDIA released Nemotron-Labs-Diffusion, a family of diffusion language models that generate multiple tokens in parallel, enabling faster inference and better GPU utilization, with sizes from 3B to 14B including vision-language variants.
This paper introduces WINO and WINO+, methods that enable revokable parallel decoding in diffusion LLMs and distill efficient denoising trajectories, significantly improving the quality-speed trade-off.
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.
Introduces Orthrus, a method that injects a trainable diffusion attention module into a frozen autoregressive transformer to achieve up to 7.8× tokens per forward pass and ~6× wall-clock speedup on MATH-500, with provably identical output distribution to the base Qwen3-8B model. The approach requires minimal additional parameters and training, and avoids the TTFT penalty of external drafters.
This paper introduces LEAP, a training-free method to accelerate inference in Diffusion Language Models (dLLMs) by detecting early-converging tokens, reducing denoising steps by 30% without losing accuracy.
The Fast Byte Latent Transformer (BLT-D) has been accepted to ICML 2026, introducing a text diffusion method for parallel byte-level decoding to overcome the speed limitations of traditional byte-level language models.
LACE introduces a lattice attention mechanism that enables concurrent reasoning paths in LLMs to share intermediate insights and correct errors during inference, improving reasoning accuracy by over 7 points compared to standard isolated parallel sampling.
This paper introduces STOP (Super Token for Pruning), a lightweight method that learns to prune unpromising reasoning paths early during parallel decoding by appending learnable tokens and reading KV cache states, achieving 70% token reduction while improving performance on AIME and GPQA benchmarks.