Yanfang Ye
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View article: 3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation
3D4D: An Interactive, Editable, 4D World Model via 3D Video Generation Open
We introduce 3D4D, an interactive 4D visualization framework that integrates WebGL with Supersplat rendering. It transforms static images and text into coherent 4D scenes through four core modules and employs a foveated rendering strategy …
View article: Food4All: A Multi-Agent Framework for Real-time Free Food Discovery with Integrated Nutritional Metadata
Food4All: A Multi-Agent Framework for Real-time Free Food Discovery with Integrated Nutritional Metadata Open
Food insecurity remains a persistent public health emergency in the United States, tightly interwoven with chronic disease, mental illness, and opioid misuse. Yet despite the existence of thousands of food banks and pantries, access remain…
View article: Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use
Interpretable Graph-Language Modeling for Detecting Youth Illicit Drug Use Open
Illicit drug use among teenagers and young adults (TYAs) remains a pressing public health concern, with rising prevalence and long-term impacts on health and well-being. To detect illicit drug use among TYAs, researchers analyze large-scal…
View article: MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference
MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference Open
Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks, and LLM-based agents further extend these abilities to various practical workflows. While recent progress shows that multi-agent systems (MAS) can…
View article: AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering
AgentRouter: A Knowledge-Graph-Guided LLM Router for Collaborative Multi-Agent Question Answering Open
Large language models (LLMs) and agent-based frameworks have advanced rapidly, enabling diverse applications. Yet, with the proliferation of models and agentic strategies, practitioners face substantial uncertainty in selecting the best co…
View article: Scalable Graph Generative Modeling via Substructure Sequences
Scalable Graph Generative Modeling via Substructure Sequences Open
Graph neural networks (GNNs) have been predominantly driven by message-passing, where node representations are iteratively updated via local neighborhood aggregation. Despite their success, message-passing suffers from fundamental limitati…
View article: PsyScam: A Benchmark for Psychological Techniques in Real-World Scams
PsyScam: A Benchmark for Psychological Techniques in Real-World Scams Open
Over the years, online scams have grown dramatically, with nearly 50% of global consumers encountering scam attempts each week. These scams cause not only significant financial losses to individuals and businesses, but also lasting psychol…
View article: EfficientLLM: Efficiency in Large Language Models
EfficientLLM: Efficiency in Large Language Models Open
Large Language Models (LLMs) have driven significant progress, yet their growing parameter counts and context windows incur prohibitive compute, energy, and monetary costs. We introduce EfficientLLM, a novel benchmark and the first compreh…
View article: The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections
The Obvious Invisible Threat: LLM-Powered GUI Agents' Vulnerability to Fine-Print Injections Open
A Large Language Model (LLM) powered GUI agent is a specialized autonomous system that performs tasks on the user's behalf according to high-level instructions. It does so by perceiving and interpreting the graphical user interfaces (GUIs)…
View article: AutoFEA: Enhancing AI Copilot by Integrating Finite Element Analysis Using Large Language Models with Graph Neural Networks
AutoFEA: Enhancing AI Copilot by Integrating Finite Element Analysis Using Large Language Models with Graph Neural Networks Open
Large Language Models (LLMs) have demonstrated significant potential across various applications, but their use as AI copilots in complex and specialized tasks is often hindered by AI hallucinations, where models generate outputs that seem…
View article: MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation
MOPI-HFRS: A Multi-objective Personalized Health-aware Food Recommendation System with LLM-enhanced Interpretation Open
View article: MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models
MASS: Mathematical Data Selection via Skill Graphs for Pretraining Large Language Models Open
High-quality data plays a critical role in the pretraining and fine-tuning of large language models (LLMs), even determining their performance ceiling to some degree. Consequently, numerous data selection methods have been proposed to iden…
View article: LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection
LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection Open
As the market for illicit drugs remains extremely profitable, major online platforms have become direct-to-consumer intermediaries for illicit drug trafficking participants. These online activities raise significant social concerns that re…
View article: Beyond Message Passing: Neural Graph Pattern Machine
Beyond Message Passing: Neural Graph Pattern Machine Open
Graph learning tasks often hinge on identifying key substructure patterns -- such as triadic closures in social networks or benzene rings in molecular graphs -- that underpin downstream performance. However, most existing graph neural netw…
View article: Can LLMs Convert Graphs to Text-Attributed Graphs?
Can LLMs Convert Graphs to Text-Attributed Graphs? Open
View article: NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning Open
View article: LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection
LLM-Empowered Class Imbalanced Graph Prompt Learning for Online Drug Trafficking Detection Open
View article: PsyScam: A Benchmark for Psychological Techniques in Real-World Scams
PsyScam: A Benchmark for Psychological Techniques in Real-World Scams Open
View article: Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees Open
Foundation models are pretrained on large-scale corpora to learn generalizable patterns across domains and tasks -- such as contours, textures, and edges in images, or tokens and sentences in text. In contrast, discovering such generalitie…
View article: NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning
NGQA: A Nutritional Graph Question Answering Benchmark for Personalized Health-aware Nutritional Reasoning Open
Diet plays a critical role in human health, yet tailoring dietary reasoning to individual health conditions remains a major challenge. Nutrition Question Answering (QA) has emerged as a popular method for addressing this problem. However, …
View article: Can LLMs Convert Graphs to Text-Attributed Graphs?
Can LLMs Convert Graphs to Text-Attributed Graphs? Open
Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data, graph neural networks (GNNs) have become a popular tool. …
View article: Training MLPs on Graphs without Supervision
Training MLPs on Graphs without Supervision Open
Graph Neural Networks (GNNs) have demonstrated their effectiveness in various graph learning tasks, yet their reliance on neighborhood aggregation during inference poses challenges for deployment in latency-sensitive applications, such as …
View article: GFT: Graph Foundation Model with Transferable Tree Vocabulary
GFT: Graph Foundation Model with Transferable Tree Vocabulary Open
Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in t…
View article: CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications
CLEAR: Towards Contextual LLM-Empowered Privacy Policy Analysis and Risk Generation for Large Language Model Applications Open
The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However, many users remain una…
View article: Careful About What App Promotion Ads Recommend! Detecting and Explaining Malware Promotion via App Promotion Graph
Careful About What App Promotion Ads Recommend! Detecting and Explaining Malware Promotion via App Promotion Graph Open
In Android apps, their developers frequently place app promotion ads, namely advertisements to promote other apps. Unfortunately, the inadequate vetting of ad content allows malicious developers to exploit app promotion ads as a new distri…
View article: Why am I seeing this: Democratizing End User Auditing for Online Content Recommendations
Why am I seeing this: Democratizing End User Auditing for Online Content Recommendations Open
Personalized recommendation systems tailor content based on user attributes, which are either provided or inferred from private data. Research suggests that users often hypothesize about reasons behind contents they encounter (e.g., "I see…
View article: TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation
TTT-Unet: Enhancing U-Net with Test-Time Training Layers for Biomedical Image Segmentation Open
Biomedical image segmentation is crucial for accurately diagnosing and analyzing various diseases. However, Convolutional Neural Networks (CNNs) and Transformers, the most commonly used architectures for this task, struggle to effectively …
View article: Graph Cross Supervised Learning via Generalized Knowledge
Graph Cross Supervised Learning via Generalized Knowledge Open
The success of GNNs highly relies on the accurate labeling of data. Existing methods of ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing nodes in the graphs. However, in reality, new nodes always c…
View article: ViT-1.58b: Mobile Vision Transformers in the 1-bit Era
ViT-1.58b: Mobile Vision Transformers in the 1-bit Era Open
Vision Transformers (ViTs) have achieved remarkable performance in various image classification tasks by leveraging the attention mechanism to process image patches as tokens. However, the high computational and memory demands of ViTs pose…
View article: Invited: Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
Invited: Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm Open