45 citations · 50 across the 7 of their papers we have counts for
7 papers
DevicesWorld: Benchmarking Cross-Device Agents in Heterogeneous Environments
Huatao Li, Xinwei Geng, Yuheng Wang +9
LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes. However, real-world user goals of…
MagicAgent: Towards Generalized Agent Planning
Xuhui Ren, Shaokang Dong, Chen Yang +21
The evolution of Large Language Models (LLMs) from passive text processors to autonomous agents has established planning as a core component of modern intelligence. However, achiev…
Know What You Know: Metacognitive Entropy Calibration for Verifiable RL Reasoning
Qiannian Zhao, Chen Yang, Jinhao Jing +5
Large reasoning models (LRMs) have emerged as a powerful paradigm for solving complex real-world tasks. In practice, these models are predominantly trained via Reinforcement Learni…
Multi-turn Response Selection with Commonsense-enhanced Language Models
Yuandong Wang, Xuhui Ren, Tong Chen +3
As a branch of advanced artificial intelligence, dialogue systems are prospering. Multi-turn response selection is a general research problem in dialogue systems. With the assistan…
Explicit Knowledge Graph Reasoning for Conversational Recommendation
Xuhui Ren, Tong Chen, Quoc Viet Hung Nguyen +3
Traditional recommender systems estimate user preference on items purely based on historical interaction records, thus failing to capture fine-grained yet dynamic user interests an…
Joint Semantic and Structural Representation Learning for Enhancing User Preference Modelling
Xuhui Ren, Wei Yuan, Tong Chen +3
Knowledge graphs (KGs) have become important auxiliary information for helping recommender systems obtain a good understanding of user preferences. Despite recent advances in KG-ba…
Learning to Ask Appropriate Questions in Conversational Recommendation
Xuhui Ren, Hongzhi Yin, Tong Chen +3
Conversational recommender systems (CRSs) have revolutionized the conventional recommendation paradigm by embracing dialogue agents to dynamically capture the fine-grained user pre…