8 citations · 17 across the 20 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…