6 citations · 10 across the 24 of their papers we have counts for
25 papers
User Representation via Cross Multi-source Behavior Pre-training for Mobile Games
Chengqi Yang, Yiran Qiao, Feng Liu +7
User representation pre-training has become a fundamental paradigm for alleviating data sparsity in downstream personalization tasks. However, existing studies predominantly focus…
Communication Policy Evolution for Proactive LLM Agents
Xinbei Ma, Jiyang Qiu, Yao Yao +10
LLM agents have rapidly evolved into autonomous systems, yet a persistent information gap remains between users and agents: communication is costly, while users' identical preferen…
Retrospective Progress-Aware Self-Refinement for LLM Agent Training
Xinbei Ma, Congmin Zheng, Jiyang Qiu +11
Long-horizon LLM-based agents receive rich environmental observations during interaction, yet outcome rewards provide limited explicit supervision about how individual actions adva…
TopoClaw: A Human-Centric and Topology-Aware Agent Operating System
Heyuan Huang, Yeyi Guan, Jihong Wang +7
Large language models (LLMs) have evolved AI assistants into autonomous reasoning engines that maintain context, invoke tools, and pursue long-horizon tasks. This has spurred Agent…
Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering
Chenyu Zhou, Huacan Chai, Wenteng Chen +18
Large language model (LLM) agents are increasingly built less by changing model weights than by reorganizing the runtime around them. Capabilities that earlier systems expected the…
Plan-MCTS: Plan Exploration for Action Exploitation in Web Navigation
Weiming Zhang, Jihong Wang, Jiamu Zhou +9
Large Language Models (LLMs) have empowered autonomous agents to handle complex web navigation tasks. While recent studies integrate tree search to enhance long-horizon reasoning,…
Adaptive Milestone Reward for GUI Agents
Congmin Zheng, Xiaoyun Mo, Xinbei Ma +10
Reinforcement Learning (RL) has emerged as a mainstream paradigm for training Mobile GUI Agents, yet it struggles with the temporal credit assignment problem inherent in long-horiz…
ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution
Jihong Wang, Jiamu Zhou, Weiming Zhang +7
With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, prima…
Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning
Zheng Wu, Xingyu Lou, Xinbei Ma +5
Large Language Model (LLM)-based agents significantly extend the utility of LLMs by interacting with dynamic environments. However, enabling agents to continually learn new tasks w…
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…
ColorAgent: Building A Robust, Personalized, and Interactive OS Agent
Ning Li, Qiqiang Lin, Zheng Wu +19
With the advancements in hardware, software, and large language model technologies, the interaction between humans and operating systems has evolved from the command-line interface…
ColorBench: Benchmarking Mobile Agents with Graph-Structured Framework for Complex Long-Horizon Tasks
Yuanyi Song, Heyuan Huang, Qiqiang Lin +9
The rapid advancement of multimodal large language models has enabled agents to operate mobile devices by directly interacting with graphical user interfaces, opening new possibili…
HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
Yu Cui, Feng Liu, Jiawei Chen +6
Recent years have witnessed a surge of research on leveraging large language models (LLMs) for sequential recommendation. LLMs have demonstrated remarkable potential in inferring u…
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…
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…
OThink-R1: Intrinsic Fast/Slow Thinking Mode Switching for Over-Reasoning Mitigation
Shengjia Zhang, Junjie Wu, Jiawei Chen +7
Human cognition operates through two complementary modes: fast intuitive thinking and slow deliberate thinking. Vanilla large language models (LLMs) predominantly follow the fast-t…
Field Matters: A Lightweight LLM-enhanced Method for CTR Prediction
Yu Cui, Feng Liu, Jiawei Chen +6
Click-through rate (CTR) prediction is a fundamental task in modern recommender systems. In recent years, the integration of large language models (LLMs) has been shown to effectiv…
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
Bohao Wang, Feng Liu, Jiawei Chen +7
Large language models (LLMs), known for their comprehension capabilities and extensive knowledge, have been increasingly applied to recommendation systems (RS). Given the fundament…
TD3: Tucker Decomposition Based Dataset Distillation Method for Sequential Recommendation
Jiaqing Zhang, Mingjia Yin, Hao Wang +5
In the era of data-centric AI, the focus of recommender systems has shifted from model-centric innovations to data-centric approaches. The success of modern AI models is built on l…
Adaptive Conditional Expert Selection Network for Multi-domain Recommendation
Kuiyao Dong, Xingyu Lou, Feng Liu +4
Mixture-of-Experts (MOE) has recently become the de facto standard in Multi-domain recommendation (MDR) due to its powerful expressive ability. However, such MOE-based method typic…
DIIT: A Domain-Invariant Information Transfer Method for Industrial Cross-Domain Recommendation
Heyuan Huang, Xingyu Lou, Chaochao Chen +5
Cross-Domain Recommendation (CDR) have received widespread attention due to their ability to utilize rich information across domains. However, most existing CDR methods assume an i…
HMDN: Hierarchical Multi-Distribution Network for Click-Through Rate Prediction
Xingyu Lou, Yu Yang, Kuiyao Dong +5
As the recommendation service needs to address increasingly diverse distributions, such as multi-population, multi-scenario, multitarget, and multi-interest, more and more recent w…
LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendation
Bohao Wang, Feng Liu, Changwang Zhang +8
Sequential Recommenders generate recommendations based on users' historical interaction sequences. However, in practice, these collected sequences are often contaminated by noisy i…