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This article explores whether multi-agent AI systems could lead to groupthink, where agents converge in thinking and hinder the emergence of exceptional individual agents, questioning the trade-off between many interacting agents versus a single super-intelligent agent.
This paper uses Deep Belief Networks and Bidirectional GRUs to classify directional trajectories near criticality in the three-state majority-vote model, achieving near-perfect separation of distinct dynamical regimes.
This paper presents multi-agent simulations of the emergence of morphological alternation patterns (like 'go/went') in language, using an AI Historical Linguist (LLM-driven) to evaluate plausibility of evolved morphologies against real languages.