Collaborative Spatial Learning with Multi-LLM Agents in Networked Social Experiments
Collective problem solving often requires that group members consider the tradeoff between exploitation of known solutions and exploration for new ones, where information of known solutions can be disseminated among individual members through communication networks. The Mason--Watts experiment (PNAS 2012) showed that human groups in shorter-path networks outperform those in longer-path networks on a two-dimensional search task. In this work, we focus on the investigation of such network-efficiency effects in the setting of a group of large language model (LLM) agents. Specifically, we consider groups of sixteen LLM agents playing the Mason--Watts experiment on the eight Mason--Watts netwo...
| Authors | Hao He, Chris J. Kuhlman, Xinwei Deng |
|---|---|
| Primary category | cs.AI |
| Published | 16 July 2026 |
| Last updated at source | 16 July 2026 |
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