26 citations · 132 across the 36 of their papers we have counts for
38 papers
GUI-Lens: Coarse-to-Fine Cropping for GUI Grounding with General-Purpose VLMs
Zichuan Fu, Shirong Wang, Wenlin Zhang +10
GUI grounding maps natural-language instructions to click locations and is essential for reliable GUI agents. The task remains difficult on high-resolution, densely populated inter…
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Zhengyi Zhao, Shubo Zhang, Huimin Wang +7
Large Reasoning Models (LRMs) have demonstrated impressive capabilities in many tasks, yet they struggle with reliably following multiple instructions, either by failing to satisfy…
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning
Yimin Deng, Zhenxi Lin, Yejing Wang +9
Diagnostic prediction and clinical reasoning are critical tasks in healthcare applications. While Large Language Models (LLMs) have shown strong capabilities in commonsense reasoni…
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models
Yimin Deng, Yejing Wang, Zhenxi Lin +8
Large language models have demonstrated strong reasoning capabilities in general knowledge question answering. However, their ability to handle temporal information remains limited…
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Zichuan Fu, Xian Wu, Guojing Li +7
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before…
Guaranteeing Knowledge Integration with Joint Decoding for Retrieval-Augmented Generation
Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7
Retrieval-Augmented Generation (RAG) significantly enhances Large Language Models (LLMs) by providing access to external knowledge. However, current research primarily focuses on r…
Attention Needs to Focus: A Unified Perspective on Attention Allocation
Zichuan Fu, Wentao Song, Guojing Li +6
The Transformer architecture, a cornerstone of modern Large Language Models (LLMs), has achieved extraordinary success in sequence modeling, primarily due to its attention mechanis…
Beyond Single Models: Mitigating Multimodal Hallucinations via Adaptive Token Ensemble Decoding
Jinlin Li, Yuran Wang, Yifei Yuan +5
Large Vision-Language Models (LVLMs) have recently achieved impressive results in multimodal tasks such as image captioning and visual question answering. However, they remain pron…
MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models
Fan Zhang, Zebang Cheng, Chong Deng +18
Recent advances in multimodal large language models (MLLMs) have catalyzed transformative progress in affective computing, enabling models to exhibit emergent emotional intelligenc…
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical Domain
Hong Huang, Weixiang Sun, Zhijian Wu +4
Recently, the rapid advancements of vision-language models, such as CLIP, leads to significant progress in zero-/few-shot anomaly detection (ZFSAD) tasks. However, most existing CL…
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs
Yimin Deng, Yuxia Wu, Yejing Wang +9
Temporal knowledge graph reasoning aims to predict future events with knowledge of existing facts and plays a key role in various downstream tasks. Previous methods focused on eith…
Model Merging for Knowledge Editing
Zichuan Fu, Xian Wu, Guojing Li +6
Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves. While existing knowledge editing approaches offer various s…
Training-free LLM Merging for Multi-task Learning
Zichuan Fu, Xian Wu, Yejing Wang +6
Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing (NLP) tasks. The release of open-source LLMs like LLaMA and Qwen…
MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Zhengyi Zhao, Shubo Zhang, Yuxi Zhang +10
Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches…
T: An Adaptive Test-Time Scaling Strategy for Contextual Question Answering
Zhengyi Zhao, Shubo Zhang, Zezhong Wang +7
Recent advances in Large Language Models (LLMs) have demonstrated remarkable performance in Contextual Question Answering (CQA). However, prior approaches typically employ elaborat…
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models
Jiashuai Liu, Yingjia Shang, Yingkang Zhan +7
With the widespread adoption of pathology foundation models in both research and clinical decision support systems, exploring their security has become a critical concern. However,…
Think or Not? Exploring Thinking Efficiency in Large Reasoning Models via an Information-Theoretic Lens
Xixian Yong, Xiao Zhou, Yingying Zhang +3
The recent rise of Large Reasoning Models (LRMs) has significantly improved multi-step reasoning performance, but often at the cost of generating excessively long reasoning chains.…
CellVerse: Do Large Language Models Really Understand Cell Biology?
Fan Zhang, Tianyu Liu, Zhihong Zhu +7
Recent studies have demonstrated the feasibility of modeling single-cell data as natural languages and the potential of leveraging powerful large language models (LLMs) for underst…
Sliding Window Attention Training for Efficient Large Language Models
Zichuan Fu, Wentao Song, Yejing Wang +7
Recent advances in transformer-based Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their quadratic computational complexity…
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
Derong Xu, Ziheng Zhang, Zhihong Zhu +7
The impressive capabilities of large language models (LLMs) have attracted extensive interests of applying LLMs to medical field. However, the complex nature of clinical environmen…
LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation
Qidong Liu, Xian Wu, Wanyu Wang +5
Sequential Recommender Systems (SRS), which model a user's interaction history to predict the next item of interest, are widely used in various applications. However, existing SRS…
LLM-ESR: Large Language Models Enhancement for Long-tailed Sequential Recommendation
Qidong Liu, Xian Wu, Yejing Wang +4
Sequential recommender systems (SRS) aim to predict users' subsequent choices based on their historical interactions and have found applications in diverse fields such as e-commerc…
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Sunan He, Yuxiang Nie, Hongmei Wang +21
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of m…
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
Jiageng Wu, Xian Wu, Yefeng Zheng +1
With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice ques…