arXiv:2602.00612v3 Announce Type: replace
Abstract: Diffusion Large Language Models (dLLMs) have demonstrated promising generative capabilities and are increasingly used to produce formal languages d...
By Yitong Zhang, Yongmin Li, Yuetong Liu, Jia Li, Xiaoran Jia, Zherui Li, Ge Li
arXiv:2603.01243v3 Announce Type: replace
Abstract: Large language models (LLMs) are powerful tools that have found applications beyond human-machine interfaces and chatbots. Beside free-form generat...
By Ayoub Hammal, Pierre Zweigenbaum, Caio Corro
arXiv:2608. 19529v1 Announce Type: cross Abstract: Many real-world AI systems represent entities, behaviors, and structured information using discrete machine-native symbols rather than natural language.
By Su Yan, Rakesh Iyer
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.
By Tianyi Li, Mingda Chen, Bowei Guo, Zhiqiang Shen
arXiv:2607. 29378v1 Announce Type: cross Abstract: Large language models (LLMs) generate text by auto-regressively sampling the next token.
By Pirzada Suhail, Nagasai Saketh Naidu, Atanu R Sinha, Amit Sethi
arXiv:2606. 19264v1 Announce Type: new Abstract: The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a probabilistically coherent manner poses a difficult inference problem.
By Sanghyeok Choi, Henry Gouk, Esmeralda S. Whitammer