arXiv AI June 8, 2026 By Haoyu Huang, Linlin Yang, Sheng Xu, Boyu Liu, Guodong Guo, Zhongqian Fu, Hang Zhou, Baochang Zhang

FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models

Read the original on arXiv AI →

arXiv:2606. 06547v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being written.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 9

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

arXiv:2602. 15327v2 Announce Type: replace-cross Abstract: Machine learning model performance improvements tend to arise from competition and application.

By Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade

llms benchmarks

arXiv Machine Learning

Jul 31

Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models

arXiv:2607. 27386v1 Announce Type: cross Abstract: Diffusion Language Models (DLMs) offer a compelling alternative to autoregressive (AR) generation by enabling bidirectional context and iterative refinement.

By Saurabh Yadav, Badri Narayana Patro, Vijay Srinivas Agneeswaran

llms diffusion safety

arXiv AI

Jun 29

Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks

arXiv:2606. 27474v1 Announce Type: cross Abstract: How should we evaluate generation systems that combine autoregressive (AR) and diffusion decoding?

By Aditi Gupta, Neel Mishra, Kushagra Trivedi, Pawan Kumar

llms diffusion benchmarks

arXiv AI

Jun 16

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

arXiv:2606. 16847v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality.

By Yizhen Yao, Qinglin Zhu, Runcong Zhao, Xiangxiang Dai, Yanzheng Xiang, Yulan He, Lin Gui

llms rag diffusion efficiency benchmarks

arXiv AI

2d ago

CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing

arXiv:2608. 13925v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) accelerate language generation by predicting multiple masks in a single forward pass.

By Yuji Ren, Chenkai Xu, Zhuocheng Gong, Jianguo Li, Zhijie Deng

llms diffusion efficiency safety

arXiv Machine Learning

Jun 4

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models

arXiv:2606. 04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context.

By Xin Yan, Aqiang Wang, Zhenglin Wan, Xingrui Yuand Ivor Tsang

llms diffusion efficiency

Related stories

Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

Beyond the Bidirectional Promise: Re-evaluating the Robustness of Diffusion Language Models

Speculative Refinement: A Hybrid Autoregressive Diffusion Decoding Strategy and Its Behavior Across Benchmarks

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

CForce: Boosting Parallel Decoding for dLLMs via Consistency Forcing

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models