3 citations · 4 across the 5 of their papers we have counts for
6 papers
HARP: Training-Free Dual-Profile Agentic Communication for LLM-Based Recommendation
Zhen Tao, Xun Zhou, Xuhui Chen +2
In LLM-based recommendation, memory acts as a user-level agent in a memory-augmented backbone. Yet users and items remain asymmetric: only the user is given a structured profile; i…
Seg-Agent: Test-Time Multimodal Reasoning for Training-Free Language-Guided Segmentation
Chao Hao, Jun Xu, Ji Du +6
Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target regions based on natural language in…
GROVER: Graph-guided Representation of Omics and Vision with Expert Regulation for Adaptive Spatial Multi-omics Fusion
Yongjun Xiao, Dian Meng, Xinlei Huang +4
Effectively modeling multimodal spatial omics data is critical for understanding tissue complexity and underlying biological mechanisms. While spatial transcriptomics, proteomics,…
On the Cross-type Homophily of Heterogeneous Graphs: Understanding and Unleashing
Zhen Tao, Ziyue Qiao, Chaoqi Chen +3
Homophily, the tendency of similar nodes to connect, is a fundamental phenomenon in network science and a critical factor in the performance of graph neural networks (GNNs). While…
Single-View Graph Contrastive Learning with Soft Neighborhood Awareness
Qingqiang Sun, Chaoqi Chen, Ziyue Qiao +2
Most graph contrastive learning (GCL) methods heavily rely on cross-view contrast, thus facing several concomitant challenges, such as the complexity of designing effective augment…
PRAGA: Prototype-aware Graph Adaptive Aggregation for Spatial Multi-modal Omics Analysis
Xinlei Huang, Zhiqi Ma, Dian Meng +5
Spatial multi-modal omics technology, highlighted by Nature Methods as an advanced biological technique in 2023, plays a critical role in resolving biological regulatory processes…