Papers by Zengqing Wu
The Hidden Strength of Disagreement: Unraveling the Consensus-Diversity Tradeoff in Adaptive Multi-Agent Systems (2025.emnlp-main)
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| Challenge: | Conventional LLM-based MAS rely on explicit coordination, e.g., prompts or voting, risking premature homogenization. |
| Approach: | They propose to preserve partial diversity by combining in-context learning with explicit coordination to form consensus in dynamic environments. |
| Outcome: | The proposed model outperforms explicit consensus models on three scenarios showing that partial deviation from group norms boosts exploration, robustness, and performance. |
Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents (2024.findings-emnlp)
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Zengqing Wu, Run Peng, Shuyuan Zheng, Qianying Liu, Xu Han, Brian Kwon, Makoto Onizuka, Shaojie Tang, Chuan Xiao
| Challenge: | Large Language Models (LLMs) are increasingly used in social simulations, where they are guided by carefully crafted instructions to exhibit human-like behaviors. |
| Approach: | They propose to use Large Language Models (LLMs) as agents to simulate the gradual transition from non-cooperative to cooperative behaviors of agents. |
| Outcome: | The proposed model can simulate the gradual transition from non-cooperative to cooperative behaviors in three competitive scenarios. |