The Society of HiveMind: Multi-agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence
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Author / Producer
Date
2026
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
Multi-agent systems address issues of accessibility and scalability of artificial intelligence (AI) foundation models, which are often represented by large language models. We develop a framework – the “Society of HiveMind” (SOHM) – that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories. On the one hand, we find that the SOHM provides a negligible benefit on tasks that mainly require real-world knowledge. On the other hand, we remark a significant improvement on tasks that require intensive logical reasoning, indicating that multi-agent systems are capable of increasing the reasoning capabilities of the collective compared to the individual agents. Our findings demonstrate the potential of combining a multitude of diverse AI foundation models to form a swarm intelligence capable of self-improvement through interactions with a given environment.
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Publication status
published
External links
Book title
Advances in Swarm Intelligence
Journal / series
Volume
16011
Pages / Article No.
243 - 254
Publisher
Springer
Event
16th International Conference on Swarm Intelligence (ICSI 2025)