8 citations · 16 across the 21 of their papers we have counts for
9 papers · 1 filter
EvolveScaler: Synthesizing Information-Evolution Contexts via Executable State Machines and Natural-Language Rendering
Ziliang Zhao, Zenan Xu, Shuting Wang +6
In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later events can revise or revoke earlier information, changing what remains va…
HUSH-Bench: Measuring Memory-Use Boundaries for Sensitive History in Conversational Agents
Lingxiang Xu, Jiaoyun Yang, Min Hu +1
Long-term memory helps conversational agents maintain continuity across sessions, while relevance and current-turn warrant remain distinct decisions. We study this boundary under a…
PlanningBench: Generating Scalable and Verifiable Planning Data for Evaluating and Training Large Language Models
Ziliang Zhao, Zenan Xu, Shuting Wang +7
Planning is a fundamental capability for large language models (LLMs) because such complex tasks require models to coordinate goals, constraints, resources, and long-term consequen…
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…
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…
RL-GPT: Integrating Reinforcement Learning and Code-as-policy
Shaoteng Liu, Haoqi Yuan, Minda Hu +5
Large Language Models (LLMs) have demonstrated proficiency in utilizing various tools by coding, yet they face limitations in handling intricate logic and precise control. In embod…
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Hongru Wang, Huimin Wang, Lingzhi Wang +6
Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question…