6 papers
LLM-Based Social Simulations Require a Boundary
Zengqing Wu, Run Peng, Takayuki Ito +2
This position paper argues that LLM-based social simulations require clear boundaries to make meaningful contributions to social science. While Large Language Models (LLMs) offer p…
Emergent Language as an Approach to Conscious AI
Zengqing Wu, Chuan Xiao
The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminat…
Not All Flips Are Conformity: Decomposing Stance Convergence in Multi-Agent LLM Debate
Xiqi Hao, Zengqing Wu, Yu-Xuan Qiu +4
Multi-agent debate (MAD) is a promising strategy for improving LLM reasoning, but when agents converge on a shared answer, it is unclear whether that convergence reflects genuine d…
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems
Zengqing Wu, Takayuki Ito
Consensus formation is pivotal in multi-agent systems (MAS), balancing collective coherence with individual diversity. Conventional LLM-based MAS primarily rely on explicit coordin…
Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generation
Jiawei Wang, Renhe Jiang, Chuang Yang +5
This paper introduces a novel approach using Large Language Models (LLMs) integrated into an agent framework for flexible and effective personal mobility generation. LLMs overcome…
Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents
Zengqing Wu, Run Peng, Shuyuan Zheng +6
Large Language Models (LLMs) have increasingly been utilized in social simulations, where they are often guided by carefully crafted instructions to stably exhibit human-like behav…