Translating historical governance into LLM multi-agent systems shows institutional topology drives collective performance gaps over 57 points, with optimal forms shifting by model capability and task.
This layered design separates institutional norms (layer 1) from execution context (layers 2–4), allowing the same soul prompt to be reused across different runtime configurations
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When Agents Evolve, Institutions Follow
Translating historical governance into LLM multi-agent systems shows institutional topology drives collective performance gaps over 57 points, with optimal forms shifting by model capability and task.