activity
20242026
most citedLightGNN: Simple Graph Neural Network for Recommendation

20 citations · 22 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2026

Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget

Guoxuan Chen, Chufeng Xiao, Haoran Yang +30

We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers compet…

cs.LG2026

UniPool: Learning Expert-to-Layer Ownership from Brief Global Access

Minbin Huang, Han Shi, Chuanyang Zheng +5

Most Mixture-of-Experts (MoE) transformers preassign each expert to one layer for the whole of training. We study whether a brief global-access phase can learn a compatible expert-…

cs.CV2025

Answer-Consistent Chain-of-thought Reinforcement Learning For Multi-modal Large Langauge Models

Minbin Huang, Runhui Huang, Chuanyang Zheng +4

Recent advances in large language models (LLMs) have demonstrated that reinforcement learning with verifiable rewards (RLVR) can significantly enhance reasoning abilities by direct…

cs.IR2025★ 2 cited

Pre-training for Recommendation Unlearning

Guoxuan Chen, Lianghao Xia, Chao Huang

Modern recommender systems powered by Graph Neural Networks (GNNs) excel at modeling complex user-item interactions, yet increasingly face scenarios requiring selective forgetting…

cs.CL2025

Self-Adjust Softmax

Chuanyang Zheng, Yihang Gao, Guoxuan Chen +7

The softmax function is crucial in Transformer attention, which normalizes each row of the attention scores with summation to one, achieving superior performances over other altern…

cs.IR2025★ 20 cited

LightGNN: Simple Graph Neural Network for Recommendation

Guoxuan Chen, Lianghao Xia, Chao Huang

Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation through their ability to conduct high-order representation smoothing, effectivel…

cs.CL2024

SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator

Guoxuan Chen, Han Shi, Jiawei Li +7

Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable chall…