2 papers
cs.CV2026
Circle-RoPE: Cone-like Decoupled Rotary Positional Embedding for Large Vision-Language Models
Chengcheng Wang, Jianyuan Guo, Hongguang Li +4
Rotary Position Embedding (RoPE) is widely adopted in large language models, but when applied to vision-language models (VLMs) it couples text and image position indices and can in…
cs.CL2026
VersatileFFN: Achieving Parameter Efficiency in LLMs via Adaptive Wide-and-Deep Reuse
Ying Nie, Kai Han, Hongguang Li +5
The rapid scaling of Large Language Models (LLMs) has achieved remarkable performance, but it also leads to prohibitive memory costs. Existing parameter-efficient approaches such a…