7 papers
Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration
Bowei He, Minda Hu, Zenan Xu +7
Search-integrated reasoning enables language agents to transcend static parametric knowledge by actively querying external sources. However, training these agents via reinforcement…
ConMax: Confidence-Maximizing Compression for Efficient Chain-of-Thought Reasoning
Minda Hu, Zexuan Qiu, Zenan Xu +3
Recent breakthroughs in Large Reasoning Models (LRMs) have demonstrated that extensive Chain-of-Thought (CoT) generation is critical for enabling intricate cognitive behaviors, suc…
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
Huan-ang Gao, Jiayi Geng, Wenyue Hua +24
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…
From General to Targeted Rewards: Surpassing GPT-4 in Open-Ended Long-Context Generation
Zhihan Guo, Jiele Wu, Wenqian Cui +4
Current research on long-form context in Large Language Models (LLMs) primarily focuses on the understanding of long-contexts, the Open-ended Long Text Generation (Open-LTG) remain…
WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback
Minda Hu, Tianqing Fang, Jianshu Zhang +7
Web agents powered by Large Language Models (LLMs) show promise for next-generation AI, but their limited reasoning in uncertain, dynamic web environments hinders robust deployment…
A Survey of Personalized Large Language Models: Progress and Future Directions
Jiahong Liu, Zexuan Qiu, Zhongyang Li +7
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing sty…