arXiv AI
Jun 2
IDLM: Inverse-distilled Diffusion Language Models
arXiv:2602. 19066v2 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) have recently achieved strong results in text generation.
D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding
arXiv:2603. 19146v2 Announce Type: replace Abstract: Discrete diffusion models are promising alternatives to autoregressive approaches for text generation, yet their decoding methods remain under-studied.
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arXiv Machine Learning
Jun 25
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
arXiv:2601. 17917v3 Announce Type: replace Abstract: Diffusion Large Language Models (dLLMs) offer a compelling paradigm for natural language generation, leveraging parallel decoding and bidirectional attention to achieve superior global coherence compared to autoregressive models.
arXiv AI
Jun 3
Plan, Verify and Fill: A Structured Parallel Decoding Approach for Diffusion Language Models
arXiv:2601. 12247v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) present a promising non-sequential paradigm for text generation, distinct from standard autoregressive (AR) approaches.
arXiv AI
Jun 9
Unified Energy for Invariant and Independent Decoding in Diffusion Language Models
arXiv:2606. 09159v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) enable parallel text generation by iteratively denoising a full sequence, offering attractive flexibility compared to auto-regressive (AR) decoding.
arXiv Machine Learning
Jun 5
Self-Augmenting Retrieval for Diffusion Language Models
arXiv:2606. 06474v1 Announce Type: cross Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel.
arXiv Machine Learning
Jun 5
A Survey on Diffusion Language Models
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
arXiv Machine Learning
Jul 8
Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge
arXiv:2606. 08048v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation quality compared to autoregressive (AR) models.
arXiv AI
Jun 19
Diffusion Language Models: An Experimental Analysis
arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.
Hugging Face Trending Papers
Jun 3
SAID: Accelerating Diffusion-Based Language Models via Scaffold-Aware Iterative Decoding
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.
arXiv Machine Learning
Jun 16
Mean-Field Parallel Decoding for Discrete Diffusion Language Models
arXiv:2606. 15805v1 Announce Type: new Abstract: Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding.
Hugging Face Trending Papers
Jul 27
Rethinking the Generation Order of Block Diffusion Language Models
Diffusion language models enable flexible arbitrary-order generation, but existing sampling methods are mostly designed for early masked diffusion models (MDMs). In this work, we study sampling for recent block diffusion language models (BDLMs).
arXiv AI
Jul 20
DiffuMamba: High-Throughput Diffusion LMs with Mamba Backbone
arXiv:2511. 15927v4 Announce Type: replace-cross Abstract: Diffusion language models (DLMs) have emerged as a promising alternative to autoregressive (AR) generation, yet their reliance on Transformer backbones limits inference efficiency due to quadratic attention or KV-cache overhead.