most citedHeterogeneous Graph-Based Multimodal Brain Network Learning

3 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2025

TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making

Kechen Jiao, Zhirui Fang, Jiahao Liu +9

Using effective generalization capabilities of vision language models (VLMs) in context-specific dynamic tasks for embodied artificial intelligence remains a significant challenge.…

cs.AI2025

HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation

Haoran Luo, Haihong E, Guanting Chen +9

Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing gr…

cs.LG2025

A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models

Mengyang Sun, Yihao Wang, Tao Feng +3

In order to streamline the fine-tuning of foundation models, Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domai…

cs.CL2025★ 1 cited

KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search

Haoran Luo, Haihong E, Yikai Guo +7

Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language model…

cs.LG2021★ 3 cited

Heterogeneous Graph-Based Multimodal Brain Network Learning

Gen Shi, Yifan Zhu, Wenjin Liu +2

Graph neural networks (GNNs) provide powerful insights for brain neuroimaging technology from the view of graphical networks. However, most existing GNN-based models assume that th…