| Abstract Scope |
AI is moving beyond prediction to become a partner in invention. While today’s models excel at interpolating within known data, true discovery requires stepping outside existing truths. We present superintelligent discovery engines built on multi-agent swarms: diverse AI agents that interact, compete and cooperate to generate structured novelty. Guided by Gödel’s insight that no closed system is complete, these swarms create gradients of difference - much like temperature gradients in thermodynamics - that sustain flow, invention, and surprise. Case studies in protein design show how swarms escape data biases, invent novel structures, and weave long-range coherence. Large Language Models (LLMs), Vision-Language Models (VLMs), and Reasoning-Language Models (RLMs) are critical components for materials discovery and manufacturing, enabling autonomous design loops where models hypothesize, test, and refine. It allows us to move from big data to big insight, pointing toward to AI that composes knowledge across science, engineering, and even the arts. |