8 citations · 24 across the 15 of their papers we have counts for
17 papers
Who Improves the Robot? Toward Physical Recursive Self-Improvement
Hai Zhou, Fan Yang, Zeyi Chen +27
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI)…
MCPGen: Benchmarking LLMs on Executable MCPWorkflow Development
Yingxuan Yang, Jiaqi Liu, Lirui Guan +4
We study whether LLMs can produce executable workflow artifacts that remain consistent across graph structure, tool implementation, schema bindings, and runtime wiring. In this set…
Online Sparse Regression with Expanding Observables
Ying Yang, Fang Yao
Online high-dimensional regression has gained increasing attention in recent years, yet existing methods typically assume that all candidate features, including important ones, are…
DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval
Siyuan Qi, Xinyuan Wang, Yingxuan Yang +5
Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods often couple semantic expansi…
Holos: A Web-Scale LLM-Based Multi-Agent System for the Agentic Web
Xiaohang Nie, Zihan Guo, Zicai Cui +20
As large language models (LLM)-driven agents transition from isolated task solvers to persistent digital entities, the emergence of the Agentic Web, an ecosystem where heterogeneou…
Understanding Agent Scaling in LLM-Based Multi-Agent Systems via Diversity
Yingxuan Yang, Chengrui Qu, Muning Wen +5
LLM-based multi-agent systems (MAS) have emerged as a promising approach to tackle complex tasks that are difficult for individual LLMs. A natural strategy is to scale performance…
PARL-MT: Learning to Call Functions in Multi-Turn Conversation with Progress Awareness
Huacan Chai, Zijie Cao, Maolin Ran +11
Large language models (LLMs) have achieved impressive success in single-turn function calling, yet real-world applications such as travel planning or multi-stage data analysis typi…
EnvX: Agentize Everything with Agentic AI
Linyao Chen, Zimian Peng, Yingxuan Yang +4
The widespread availability of open-source repositories has led to a vast collection of reusable software components, yet their utilization remains manual, error-prone, and disconn…
Agentic Web: Weaving the Next Web with AI Agents
Yingxuan Yang, Mulei Ma, Yuxuan Huang +15
The emergence of AI agents powered by large language models (LLMs) marks a pivotal shift toward the Agentic Web, a new phase of the internet defined by autonomous, goal-driven inte…
Agent Exchange: Shaping the Future of AI Agent Economics
Yingxuan Yang, Ying Wen, Jun Wang +1
The rise of Large Language Models (LLMs) has transformed AI agents from passive computational tools into autonomous economic actors. This shift marks the emergence of the agent-cen…
AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Yingxuan Yang, Huacan Chai, Shuai Shao +4
The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, exis…
A Survey of AI Agent Protocols
Yingxuan Yang, Huacan Chai, Yuanyi Song +11
The rapid development of large language models (LLMs) has led to the widespread deployment of LLM agents across diverse industries, including customer service, content generation,…
Understanding and Optimizing Agentic Workflows via Shapley value
Yingxuan Yang, Bo Huang, Siyuan Qi +14
Agentic workflows have become the dominant paradigm for building complex AI systems, orchestrating specialized components, such as planning, reasoning, action execution, and reflec…
LLM-based Multi-Agent Systems: Techniques and Business Perspectives
Yingxuan Yang, Qiuying Peng, Jun Wang +2
In the era of (multi-modal) large language models, most operational processes can be reformulated and reproduced using LLM agents. The LLM agents can perceive, control, and get fee…
P3: A Policy-Driven, Pace-Adaptive, and Diversity-Promoted Framework for data pruning in LLM Training
Yingxuan Yang, Huayi Wang, Muning Wen +4
In the rapidly advancing field of Large Language Models (LLMs), effectively leveraging existing datasets during fine-tuning to maximize the model's potential is of paramount import…
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
Ruiwen Zhou, Yingxuan Yang, Muning Wen +6
Numerous large language model (LLM) agents have been built for different tasks like web navigation and online shopping due to LLM's wide knowledge and text-understanding ability. A…
Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud
Tianyao Shi, Yingxuan Yang, Yunlong Cheng +3
Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers wa…