5 papers
Understanding and Improving Length Generalization in Hierarchical Sparse Attention Models
Jiaqi Leng, Xiang Hu, Junxiong Wang +3
Effectively processing long contexts is a critical challenge for language models. While standard Transformers are limited by quadratic complexity and poor length extrapolation, alt…
LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Model
Inclusion AI, Tiwei Bie, Haoxing Chen +15
We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a natively integrated framework. Its…
TwinFlow: Realizing One-step Generation on Large Models with Self-adversarial Flows
Zhenglin Cheng, Peng Sun, Jianguo Li +1
Recent advances in large multi-modal generative models have demonstrated impressive capabilities in multi-modal generation, including image and video generation. These models are t…
Flash Multi-Head Feed-Forward Network
Minshen Zhang, Xiang Hu, Jianguo Li +2
We explore Multi-Head FFN (MH-FFN) as a replacement of FFN in the Transformer architecture, motivated by the structural similarity between single-head attention and FFN. While mult…
Every Token Counts: Generalizing 16M Ultra-Long Context in Large Language Models
Xiang Hu, Zhanchao Zhou, Ruiqi Liang +3
This work explores the challenge of building ``Machines that Can Remember'', framing long-term memory as the problem of efficient ultra-long context modeling. We argue that this re…