1 citations · 1 across the 1 of their papers we have counts for
5 papers
Learning in the Recurrent State: Gradient Descent with Linear Recurrent Networks
Yudou Tian, Neeraj Mohan Sushma, Harshvardhan Mestha +3
Linear recurrent networks (LRNNs) offer linear-time sequence modeling, but standard recurrent updates do not directly expose the supervised products needed for in-context gradient…
RF-GPT: Teaching AI to See the Wireless World
Hang Zou, Yu Tian, Bohao Wang +4
Large language models (LLMs) and multimodal models have become powerful general-purpose reasoning systems. However, radio-frequency (RF) signals, which underpin wireless systems, a…
PairUni: Pairwise Training for Unified Multimodal Language Models
Jiani Zheng, Zhiyang Teng, Kunpeng Qiu +6
Unified Vision-Language Models (UVLMs) perform both understanding and generation within a single architecture. Since these models rely on heterogeneous data and supervision, balanc…
Composable Visual Tokenizers with Generator-Free Diagnostics of Learnability
Bingchen Zhao, Qiushan Guo, Ye Wang +3
We introduce CompTok, a training framework for learning visual tokenizers whose tokens are enhanced for compositionality. CompTok uses a token-conditioned diffusion decoder. By emp…
MMaDA-Parallel: Multimodal Large Diffusion Language Models for Thinking-Aware Editing and Generation
Ye Tian, Ling Yang, Jiongfan Yang +10
While thinking-aware generation aims to improve performance on complex tasks, we identify a critical failure mode where existing sequential, autoregressive approaches can paradoxic…