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Research Article | Open Access

Mindstorms in natural language-based societies of mind

Center of Excellence for Generative AI, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Dalle Molle Institute for Artificial Intelligence Research, Lugano, Switzerland
Stanford University, California, USA
Oxford University, Oxford, UK
Harvard University, Cambridge, USA
ETH AI Center, Zurich, Switzerland
Beihang University, Beijing, China
CS & VCIP, Nankai University, Tianjin, China

* Mingchen Zhuge, Haozhe Liu, Francesco Faccio, and Dylan R. Ashley contributed equally to this work.

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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.

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Keywords

mindstormsociety of mind (SOM)large language models (LLMs)multimodal learninglearning to think

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Computational Visual Media
Volume 11 Issue 1,
February 2025
Pages 29-81
DOI: 10.26599/CVM.2025.9450460

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Zhuge M, Liu H, Faccio F, et al. Mindstorms in natural language-based societies of mind. Computational Visual Media, 2025, 11(1): 29-81. https://doi.org/10.26599/CVM.2025.9450460

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Received: 30 July 2024
Accepted: 16 September 2024
Published: 28 February 2025
© The Author(s) 2025.

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