arXiv AI
Oct 21

Large Flow Language Models

arXiv:2610.28559v1 Announce Type: cross Abstract: Continuous flow language models offer a path to parallel generation with tools for control and acceleration unavailable to discrete diffusion, yet ef...

By Arnav Chavan, Chanhyuk Lee, Udbhav Bamba, Jaehoon Yoo, Seunghoon Hong, Aditi Raghunathan, Jinwoo Kim
arXiv Computation and Language
6d ago

Cola2: Co-Adaptive Hierarchical Language Modeling with Continuous Semantic Compression and Balanced Discrete Generation

Cola2 is a hierarchical language modeling framework that jointly optimizes continuous semantic compression, reconstruction–generation balance, and continuous–discrete co‑adaptation. It dynamically segments text into semantic spans, controls compression via negative feedback, and trains a block‑causal masked diffusion decoder with clean, noisy, and dropped latent conditions. Experiments on five downstream tasks with 210 billion training tokens show competitive scaling, and an unconverged checkpoint with 346 billion source tokens outperforms Llama‑3‑8B on HellaSwag and StoryCloze.

By Hongcan Guo, Yian Zhao, Feng Wang, Shen Nie, Guoqiang Wei, Yan Zeng, Hengshuang Zhao
arXiv Computation and Language
Aug 28

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

The paper introduces Dependency-Aware Revocable Decoding (DARD), a training‑free framework for diffusion large language models that separates tokens into masked, candidate, and unmasked states. DARD verifies candidate tokens using a selective context that excludes less reliable tokens and adaptively regulates their influence on subsequent decoding. Experiments on 12 textual and multimodal benchmarks across three open‑source dLLMs show that DARD improves the speed‑quality Pareto frontier, achieving a 2.71× speedup and a 4.35‑point CIDEr gain over Saber on Flickr30K.

By Wooje Park, Insu Lee, Minyoung Noh, Jaeyun Jang, Sungmin Lee, Kyuhong Shim, Byonghyo Shim
arXiv AI
Oct 12

One-Step Text Generation by Seq-Drifting

The paper introduces Seq-Drifting, a new framework that enables diffusion language models to generate an entire text sequence in a single forward pass. By applying a drift field to continuous token representations, the model learns a direct mapping from Gaussian noise to text, using attraction to guide outputs toward contextually plausible tokens and repulsion to prevent repetition and collapse. Experiments show that Seq-Drifting achieves a perplexity of 36.01 on LM1B and performs competitively in unconditional, conditional, translation, summarization, and reasoning tasks.

By Jiayu Liu, Ruizhi Zhao, Tianyun Ji, Yee Whye Teh, Zhenya Huang, Ning Miao