@article{Zhuge2025, 
author = {Mingchen Zhuge and Haozhe Liu and Francesco Faccio and Dylan R. Ashley and Róbert Csordás and Anand Gopalakrishnan and Abdullah Hamdi and Hasan Abed Al Kader Hammoud and Vincent Herrmann and Kazuki Irie and Louis Kirsch and Bing Li and Guohao Li and Shuming Liu and Jinjie Mai and Piotr Piękos and Aditya A. Ramesh and Imanol Schlag and Weimin Shi and Aleksandar Stanić and Wenyi Wang and Yuhui Wang and Mengmeng Xu and Deng-Ping Fan and Bernard Ghanem and Jürgen Schmidhuber},
title = {Mindstorms in natural language-based societies of mind},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {1},
pages = {29-81},
keywords = {multimodal learning, large language models (LLMs), mindstorm, society of mind (SOM), learning to think},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450460},
doi = {10.26599/CVM.2025.9450460},
abstract = {Inspired by Minsky’s Society of Mind, Schmidhuber’s Learning to Think, and other more recent works, this paper proposes and advocates for the concept of natural language-based societies of mind (NLSOMs). We imagine these societies as consisting of a collection of multimodal neural networks, including large language models, which engage in a “mindstorm” to solve problems using a shared natural language interface. Here, we work to identify and discuss key questions about the social structure, governance, and economic principles for NLSOMs, emphasizing their impact on the future of AI. Our demonstrations with NLSOMs—which feature up to 129 agents—show their effectiveness in various tasks, including visual question answering, image captioning, and prompt generation for text-to-image synthesis.}
}