11 citations · 18 across the 12 of their papers we have counts for
14 papers
From Parameters to Behaviors: A Survey of Model Fusion for Large Language Models
Shuo Cai, Yanggan Gu, Zihao Wang +8
Model fusion integrates the capabilities from source models into a single target model. As of June 2026, Hugging Face hosts more than 2M models. This growing pool provides a rich b…
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
Qi Peng, Jiatong Li, Sirui Huang +10
Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care. This survey examines recent progress…
FeatCal: Feature Calibration for Post-Merging Models
Yanggan Gu, Shuo Cai, Zihao Wang +7
Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often still underperforms task exper…
ICAT: Incident-Case-Grounded Adaptive Testing for Physical-Risk Prediction in Embodied World Models
Zhenglin Lai, Sirui Huang, Yuteng Li +3
Reference-image-conditioned video generators are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to anticipate physical hazards…
Debiasing Sequential Recommendation with Time-aware Inverse Propensity Scoring
Sirui Huang, Jing Long, Qian Li +2
Sequential Recommendation (SR) predicts users next interactions by modeling the temporal order of their historical behaviors. Existing approaches, including traditional sequential…
Cloud-Device Collaborative Agents for Sequential Recommendation
Jing Long, Sirui Huang, Huan Huo +3
Recent advances in large language models (LLMs) have enabled agent-based recommendation systems with strong semantic understanding and flexible reasoning capabilities. While LLM-ba…
Simplifying Graph Kernels for Efficient
Lin Wang, Shijie Wang, Sirui Huang +1
While kernel methods and Graph Neural Networks offer complementary strengths, integrating the two has posed challenges in efficiency and scalability. The Graph Neural Tangent Kerne…
HyperG: Hypergraph-Enhanced LLMs for Structured Knowledge
Sirui Huang, Hanqian Li, Yanggan Gu +3
Given that substantial amounts of domain-specific knowledge are stored in structured formats, such as web data organized through HTML, Large Language Models (LLMs) are expected to…
Capturing Nuanced Preferences: Preference-Aligned Distillation for Small Language Models
Yanggan Gu, Junzhuo Li, Sirui Huang +3
Aligning small language models (SLMs) with human values typically involves distilling preference knowledge from large language models (LLMs). However, existing distillation methods…
Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey
Yunkai Dang, Kaichen Huang, Jiahao Huo +11
The rapid development of Artificial Intelligence (AI) has revolutionized numerous fields, with large language models (LLMs) and computer vision (CV) systems driving advancements in…
Look Twice Before You Answer: Memory-Space Visual Retracing for Hallucination Mitigation in Multimodal Large Language Models
Xin Zou, Yizhou Wang, Yibo Yan +8
Despite their impressive capabilities, multimodal large language models (MLLMs) are prone to hallucinations, i.e., the generated content that is nonsensical or unfaithful to input…
LATEX-GCL: Large Language Models (LLMs)-Based Data Augmentation for Text-Attributed Graph Contrastive Learning
Haoran Yang, Xiangyu Zhao, Sirui Huang +2
Graph Contrastive Learning (GCL) is a potent paradigm for self-supervised graph learning that has attracted attention across various application scenarios. However, GCL for learnin…
Reasoning Factual Knowledge in Structured Data with Large Language Models
Sirui Huang, Yanggan Gu, Xuming Hu +3
Large language models (LLMs) have made remarkable progress in various natural language processing tasks as a benefit of their capability to comprehend and reason with factual knowl…
Refiner: Restructure Retrieval Content Efficiently to Advance Question-Answering Capabilities
Zhonghao Li, Xuming Hu, Aiwei Liu +3
Large Language Models (LLMs) are limited by their parametric knowledge, leading to hallucinations in knowledge-extensive tasks. To address this, Retrieval-Augmented Generation (RAG…