5 papers
ProcureGym: A Multi-Agent Markov Game Framework for Modeling National Volume-based Drug Procurement
Jia Wang, Qian Xu, Xuanwen Ding +4
In this paper, we introduce ProcureGym, an data-driven multi-agent simulation platform that models China's National Volume-Based drug Procurement (NVBP) as a Markov Game. Based on…
Towards a Science of Collective AI: LLM-based Multi-Agent Systems Need a Transition from Blind Trial-and-Error to Rigorous Science
Jingru Fan, Dewen Liu, Yufan Dang +15
Recent advancements in Large Language Models (LLMs) have greatly extended the capabilities of Multi-Agent Systems (MAS), demonstrating significant effectiveness across a wide range…
AutoJudger: An Agent-Driven Framework for Efficient Benchmarking of MLLMs
Xuanwen Ding, Chengjun Pan, Zejun Li +3
Evaluating multimodal large language models (MLLMs) is increasingly expensive, as the growing size and cross-modality complexity of benchmarks demand significant scoring efforts. T…
From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents
Xinyi Mou, Xuanwen Ding, Qi He +8
Traditional sociological research often relies on human participation, which, though effective, is expensive, challenging to scale, and with ethical concerns. Recent advancements i…
Recent Advances of Foundation Language Models-based Continual Learning: A Survey
Yutao Yang, Jie Zhou, Xuanwen Ding +5
Recently, foundation language models (LMs) have marked significant achievements in the domains of natural language processing (NLP) and computer vision (CV). Unlike traditional neu…