9 papers
ARCO: Adaptive Rubrics with Co-Evolution for Multi-Step LLM-Based Agents
Zihang Tian, Jingsen Zhang, Rui Li +3
Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or bad. Rubric-based rewards impro…
LLM Agents as Social Scientists: A Human-AI Collaborative Platform for Social Science Automation
Lei Wang, Yuanzi Li, Jinchao Wu +4
Traditional social science research often requires designing complex experiments across vast methodological spaces and depends on real human participants, making it labor-intensive…
Prompt and Parameter Co-Optimization for Large Language Models
Xiaohe Bo, Rui Li, Zexu Sun +5
Prompt optimization and fine-tuning are two major approaches to improve the performance of Large Language Models (LLMs). They enhance the capabilities of LLMs from complementary pe…
Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective
Rui Li, Zeyu Zhang, Xiaohe Bo +4
Multi-agent architectures built on large language models (LLMs) have demonstrated the potential to realize swarm intelligence through well-crafted collaboration. However, the subst…
HAPS: Hierarchical LLM Routing with Joint Architecture and Parameter Search
Zihang Tian, Rui Li, Jingsen Zhang +3
Large language model (LLM) routing aims to exploit the specialized strengths of different LLMs for diverse tasks. However, existing approaches typically focus on selecting LLM arch…
CAM: A Constructivist View of Agentic Memory for LLM-Based Reading Comprehension
Rui Li, Zeyu Zhang, Xiaohe Bo +5
Current Large Language Models (LLMs) are confronted with overwhelming information volume when comprehending long-form documents. This challenge raises the imperative of a cohesive…