18 citations · 37 across the 29 of their papers we have counts for
31 papers
BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
Sijing Wu, Dongyuan Li, Miaoting Huang +4
Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease. Existing methods ty…
HarnessSafe: Evaluating Safety Across Persistent Carriers in Agent Harnesses
Xiao Zhang, Yusheng Wang, Yuhao Fei +5
Modern agent harnesses persist state across tasks and sessions through persistent carriers like memory, skills, tools, and shared artifacts. However, this capability creates delaye…
Can LLM design high-quality experiments? A Comprehensive and Systematic Benchmark on Autonomous Experimental Design
Zejun Liu, Jian Wu, Ru Peng +4
AI for Research (AI4Research) leverages AI to automate and improve scientific workflows. While experimental design is a critical stage of the research process, prior work has focus…
HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation
Yaozu Wu, Wei-Chieh Huang, Jizhou Guo +11
Large language models increasingly operate in settings where humans are active collaborators rather than passive task providers. We introduce HAS-Framework, a graph-based framework…
PaperClaw: Harnessing Agents for Autonomous Research and Human-in-the-Loop Refinement
Weiwei Ye, Hangchen Liu, Dongyuan Li +1
Large language models have become capable reasoners and tool users that write and run code and search the literature, which makes automating the research process itself a realistic…
TimeClaw: A Time-Series AI Agent with Exploratory Execution Learning
Hangchen Liu, Dongyuan Li, Renhe Jiang +3
Time series analysis underpins forecasting, monitoring, and decision making in domains such as finance and weather, where solving a task often requires both numerical accuracy and…
Node Role-Guided LLMs for Dynamic Graph Clustering
Dongyuan Li, Ying Zhang, Yaozu Wu +1
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing how complex real-world systems evolve over time. However, existing methods are…
Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
Dongyuan Li, Shun Zheng, Chang Xu +2
Time series forecasting has attracted significant attention in the field of AI. Previous works have revealed that the Channel-Independent (CI) strategy improves forecasting perform…
ELLMob: Event-Driven Human Mobility Generation with Self-Aligned LLM Framework
Yusong Wang, Chuang Yang, Jiawei Wang +5
Human mobility generation aims to synthesize plausible trajectory data, which is widely used in urban system research. While Large Language Model-based methods excel at generating…
See2Refine: Vision-Language Feedback Improves LLM-Based eHMI Action Designers
Ding Xia, Xinyue Gui, Mark Colley +5
Automated vehicles lack natural communication channels with other road users, making external Human-Machine Interfaces (eHMIs) essential for conveying intent and maintaining trust…
Event-Aware Prompt Learning for Dynamic Graphs
Xingtong Yu, Ruijuan Liang, Renhe Jiang +4
Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph learning, dynamic graph neural net…
Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety
Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu +12
Deep research frameworks have shown promising capabilities in synthesizing comprehensive reports from web sources. While deep research possesses significant potential to address co…
PSG-Agent: Personality-Aware Safety Guardrail for LLM-based Agents
Yaozu Wu, Jizhou Guo, Dongyuan Li +9
Effective guardrails are essential for safely deploying LLM-based agents in critical applications. Despite recent advances, existing guardrails suffer from two fundamental limitati…
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
Yangning Li, Weizhi Zhang, Yuyao Yang +17
Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inf…
A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +10
Recent improvements in large language models (LLMs) have led many researchers to focus on building fully autonomous AI agents. This position paper questions whether this approach i…
EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing
Fan Gao, Dongyuan Li, Ding Xia +4
Chinese essay writing and its evaluation are critical in educational contexts, yet the capabilities of Large Language Models (LLMs) in this domain remain largely underexplored. Exi…
Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity
Shiyin Tan, Dongyuan Li, Renhe Jiang +3
Popularity bias occurs when popular items are recommended far more frequently than they should be, negatively impacting both user experience and recommendation accuracy. Existing d…
Automating eHMI Action Design with LLMs for Automated Vehicle Communication
Ding Xia, Xinyue Gui, Fan Gao +3
The absence of explicit communication channels between automated vehicles (AVs) and other road users requires the use of external Human-Machine Interfaces (eHMIs) to convey message…
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey
Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu +17
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant…
A Unified Retrieval Framework with Document Ranking and EDU Filtering for Multi-document Summarization
Shiyin Tan, Jaeeon Park, Dongyuan Li +2
In the field of multi-document summarization (MDS), transformer-based models have demonstrated remarkable success, yet they suffer an input length limitation. Current methods apply…
Revisiting Dynamic Graph Clustering via Matrix Factorization
Dongyuan Li, Satoshi Kosugi, Ying Zhang +3
Dynamic graph clustering aims to detect and track time-varying clusters in dynamic graphs, revealing the evolutionary mechanisms of complex real-world dynamic systems. Matrix facto…
League: Leaderboard Generation on Demand
Jian Wu, Jiayu Zhang, Dongyuan Li +5
This paper introduces Leaderboard Auto Generation (LAG), a novel and well-organized framework for automatic generation of leaderboards on a given research topic in rapidly evolving…
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances
Yaozu Wu, Dongyuan Li, Yankai Chen +7
Autonomous Driving Systems (ADSs) are revolutionizing transportation by reducing human intervention, improving operational efficiency, and enhancing safety. Large Language Models (…
Diffusion Models in 3D Vision: A Survey
Zhen Wang, Dongyuan Li, Yaozu Wu +3
In recent years, 3D vision has become a crucial field within computer vision, powering a wide range of applications such as autonomous driving, robotics, augmented reality, and med…