5 papers
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…
Solving the Granularity Mismatch: Hierarchical Preference Learning for Long-Horizon LLM Agents
Heyang Gao, Zexu Sun, Erxue Min +4
Large Language Models (LLMs) as autonomous agents are increasingly tasked with solving complex, long-horizon problems. Aligning these agents via preference-based offline methods li…
Cog-Rethinker: Hierarchical Metacognitive Reinforcement Learning for LLM Reasoning
Zexu Sun, Yongcheng Zeng, Erxue Min +3
Contemporary progress in large language models (LLMs) has revealed notable inferential capacities via reinforcement learning (RL) employing verifiable reward, facilitating the deve…
YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models
Lei Wang, Heyang Gao, Xiaohe Bo +2
Leveraging large language model (LLM) based agents to simulate human social behaviors has recently gained significant attention. In this paper, we introduce a novel social simulato…
GenSim: A General Social Simulation Platform with Large Language Model based Agents
Jiakai Tang, Heyang Gao, Xuchen Pan +11
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. Whi…