Discovering Adaptive Transmission Programs for Collective Innovation
Read the original on arXiv AI →The paper presents a method for designing state‑aware transmission protocols that guide how information and resources are shared among agents in a collective discovery task. Using LLM‑guided evolutionary search, the authors evolve protocols that outperform existing baselines by up to 37%, and show that the advantage stems from conditioning on content and agent states rather than just network topology. The evolved protocols also generalize across different domains and agent populations, indicating that such protocols can be discovered in silico and may inform AI‑assisted coordination systems for human collective intelligence.
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