7 papers
Step-Level Preference Learning for Generative Agents in Social Simulations
Wenchang Gao, Pingyue Sheng, Lanlan Qiu +7
Large language model (LLM)-based generative agents simulate human behavior through long-horizon decision-making processes that comprise intermediate steps such as planning, memory…
SidConArena: An Environment Evaluating Agents in Open-Ended,Positive-Sum Bargaining Game
Yeqi Feng, Yuxin Chen, Tianxing He
Evaluating LLM agents requires dynamic environments that go beyond static reasoning and zero-sum games. Real-world economic interaction is often open-ended and mixed-motive: agents…
VirtualCrime: Evaluating the Criminal Potential and Agentic Behaviors of Large Language Models via Sandbox Simulation
Yilin Tang, Yu Wang, Lanlan Qiu +4
Large language models (LLMs) have shown strong capabilities in multi-step decision-making, and are increasingly integrated into real-world agentic applications. While existing safe…
SimCity: Multi-Agent Urban Development Simulation with Rich Interactions
Yeqi Feng, Yucheng Lu, Hongyu Su +2
Large Language Models (LLMs) open new possibilities for constructing realistic and interpretable macroeconomic simulations. We present SimCity, a multi-agent framework that leverag…
A Visualized Framework for Event Cooperation with Generative Agents
Yuyang Tian, Shunqiang Mao, Wenchang Gao +2
Large Language Models (LLMs) have revolutionized the simulation of agent societies, enabling autonomous planning, memory formation, and social interactions. However, existing frame…
LLMs vs. Chinese Anime Enthusiasts: A Comparative Study on Emotionally Supportive Role-Playing
Lanlan Qiu, Xiao Pu, Yeqi Feng +1
Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing conversations and providing emotional support as separate research directions. However, there…
Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles
Xiao Pu, Tianxing He, Xiaojun Wan
Prompt compression condenses contexts while maintaining their informativeness for different usage scenarios. It not only shortens the inference time and reduces computational costs…