7 papers
dMoE: dLLMs with Learnable Block Experts
Sicheng Feng, Zigeng Chen, Gongfan Fang +2
Diffusion Large Language Models (dLLMs) have recently emerged as a promising alternative to autoregressive models, offering competitive performance while naturally supporting paral…
Is Oracle Pruning the True Oracle?
Sicheng Feng, Keda Tao, Huan Wang
Oracle pruning, which selects unimportant weights by minimizing the pruned train loss, has served as the foundation for most neural network pruning methods for over thirty-five yea…
ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps
Sicheng Feng, Song Wang, Shuyi Ouyang +5
Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performa…
RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
Sicheng Feng, Kaiwen Tuo, Song Wang +3
Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even adva…
dVoting: Fast Voting for dLLMs
Sicheng Feng, Zigeng Chen, Xinyin Ma +2
Diffusion Large Language Models (dLLMs) represent a new paradigm beyond autoregressive modeling, offering competitive performance while naturally enabling a flexible decoding proce…
A Survey of Token Compression for Efficient Multimodal Large Language Models
Kele Shao, Keda Tao, Kejia Zhang +7
Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution im…