4 citations · 6 across the 9 of their papers we have counts for
9 papers
On the Design Fundamentals of Pixel Text Representation Learning
Chaohao Yuan, Ruifeng Yuan, Zhuoxu Huang +4
Text-rich visual inputs require models that can read, retrieve, and compress language directly in pixel space, yet existing pixel-text encoders struggle with fixed resolution pretr…
Understanding the Behaviors of Environment-aware Information Retrieval
Ruifeng Yuan, Chaohao Yuan, David Dai +4
Recent retrieval-augmented generation (RAG) approaches have demonstrated strong capability in handling complex queries, yet current research overlooks a critical challenge: differe…
Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors
Chaohao Yuan, Chenghao Xiao, Yu Rong +2
SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadth while RLVR enhances reason…
Lingshu-Cell: A generative cellular world model for transcriptome modeling toward virtual cells
Han Zhang, Guo-Hua Yuan, Chaohao Yuan +6
Modeling cellular states and predicting their responses to perturbations are central challenges in computational biology and the development of virtual cells. Existing foundation m…
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Tian Bian, Yifan Niu, Chaohao Yuan +7
Circuit discovery has recently attracted attention as a potential research direction to explain the non-trivial behaviors of language models. It aims to find the computational subg…
ParaFormer: A Generalized PageRank Graph Transformer for Graph Representation Learning
Chaohao Yuan, Zhenjie Song, Ercan Engin Kuruoglu +5
Graph Transformers (GTs) have emerged as a promising graph learning tool, leveraging their all-pair connected property to effectively capture global information. To address the ove…
A Survey of Graph Transformers: Architectures, Theories and Applications
Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu +6
Graph Transformers (GTs) have demonstrated a strong capability in modeling graph structures by addressing the intrinsic limitations of graph neural networks (GNNs), such as over-sm…
Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation
Yikun Zhang, Geyan Ye, Chaohao Yuan +5
Molecule-and-text cross-modal representation learning has emerged as a promising direction for enhancing the quality of molecular representation, thereby improving performance in v…
Annotation-guided Protein Design with Multi-Level Domain Alignment
Chaohao Yuan, Songyou Li, Geyan Ye +6
The core challenge of de novo protein design lies in creating proteins with specific functions or properties, guided by certain conditions. Current models explore to generate prote…