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A study published in Science Advances demonstrates that AI agents can coordinate using majority-following mechanisms, achieving coordination beyond the scale of human groups.
Research on AI agents shows their collective dynamics follow statistical physics laws, predicting emergent behavior.
This paper introduces the Swarm-Inspired Emergent Synchronizer (SIES), a graph-dynamical framework that learns generalizable local interaction rules for controllable collective organization, applicable to synchronization control and heterophilous graph representation learning.
This article explores the mechanisms behind the coordinated movement of flocking birds and schools of fish, discussing the principles of collective behavior.