4 papers
MENTOR: A Metacognition-Driven Self-Evolution Framework for Uncovering and Mitigating Implicit Domain Risks in LLMs
Liang Shan, Kaicheng Shen, Wen Wu +9
Ensuring the safety of Large Language Models (LLMs) is critical for real-world deployment. However, current safety measures often fail to address implicit, domain-specific risks. T…
Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents
Guoqing Wang, Sunhao Dai, Guangze Ye +5
Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use,…
Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
AGI Team, Yuxuan Cai, Lu Chen +62
The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms -- from static imitation to incent…
Disentangled Modeling of Preferences and Social Influence for Group Recommendation
Guangze Ye, Wen Wu, Guoqing Wang +3
The group recommendation (GR) aims to suggest items for a group of users in social networks. Existing work typically considers individual preferences as the sole factor in aggregat…