8 citations · 17 across the 20 of their papers we have counts for
21 papers
Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis
Yujie Zheng, Zhuo Li, Shengtao Zhang +8
Deploying Large Language Models to data-scarce programming domains poses significant challenges, particularly for kernel synthesis on emerging Domain-Specific Architectures where a…
MemRL: Self-Evolving Agents via Runtime Reinforcement Learning on Episodic Memory
Shengtao Zhang, Jiaqian Wang, Ruiwen Zhou +11
The hallmark of human intelligence is the self-evolving ability to master new skills by learning from past experiences. However, current AI agents struggle to emulate this self-evo…
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…
OSCAR: Optimization-Steered Agentic Planning for Composed Image Retrieval
Teng Wang, Rong Shan, Jianghao Lin +8
Composed image retrieval (CIR) requires complex reasoning over heterogeneous visual and textual constraints. Existing approaches largely fall into two paradigms: unified embedding…
An Efficient Algorithm for Thresholding Monte Carlo Tree Search
Shoma Nameki, Atsuyoshi Nakamura, Junpei Komiyama +1
We introduce the Thresholding Monte Carlo Tree Search problem, in which, given a tree and a threshold , a player must answer whether the root node value of $\mathc…
ColorEcosystem: Powering Personalized, Standardized, and Trustworthy Agentic Service in massive-agent Ecosystem
Fangwen Wu, Zheng Wu, Jihong Wang +11
With the rapid development of (multimodal) large language model-based agents, the landscape of agentic service management has evolved from single-agent systems to multi-agent syste…
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…
VeriOS: Query-Driven Proactive Human-Agent-GUI Interaction for Trustworthy OS Agents
Zheng Wu, Heyuan Huang, Xingyu Lou +8
With the rapid progress of multimodal large language models, operating system (OS) agents become increasingly capable of automating tasks through on-device graphical user interface…
Quick on the Uptake: Eliciting Implicit Intents from Human Demonstrations for Personalized Mobile-Use Agents
Zheng Wu, Heyuan Huang, Yanjia Yang +6
As multimodal large language models advance rapidly, the automation of mobile tasks has become increasingly feasible through the use of mobile-use agents that mimic human interacti…
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…
MobileUse: A GUI Agent with Hierarchical Reflection for Autonomous Mobile Operation
Ning Li, Xiangmou Qu, Jiamu Zhou +6
Recent advances in Multimodal Large Language Models (MLLMs) have enabled the development of mobile agents that can understand visual inputs and follow user instructions, unlocking…
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,…
MARFT: Multi-Agent Reinforcement Fine-Tuning
Junwei Liao, Muning Wen, Jun Wang +1
Large Language Model (LLM)-based Multi-Agent Systems (LaMAS) offer a promising approach to collaborative tasks, but applying reinforcement learning to these systems remains challen…
Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaboration
Shao Zhang, Xihuai Wang, Wenhao Zhang +10
Agents built on large language models (LLMs) have excelled in turn-by-turn human-AI collaboration but struggle with simultaneous tasks requiring real-time interaction. Latency issu…
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…
HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Device Scenarios
Jun Wang, Jiamu Zhou, Muning Wen +7
Evaluating the performance of LLMs in multi-turn human-agent interactions presents significant challenges, particularly due to the complexity and variability of user behavior. In t…
Hammer: Robust Function-Calling for On-Device Language Models via Function Masking
Qiqiang Lin, Muning Wen, Qiuying Peng +8
Large language models have demonstrated impressive value in performing as autonomous agents when equipped with external tools and API calls. Nonetheless, effectively harnessing the…
Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation
Yiwei Shi, Muning Wen, Qi Zhang +3
Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear fe…
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…