Formalizes AI agency and belief systems using condensed mathematics and categorical limit constructions.
ANIMA Theory is a specialized framework designed to model agentic behavior as a mathematical limit of skill applications. By leveraging Scholze-Clausen condensed mathematics and active inference, it provides a rigorous structure for understanding how AI agents reach equilibrium, manage prediction markets, and navigate complex world models. This skill is ideal for developers and researchers working on high-level autonomous agency, compositional world modeling, and the application of advanced topological concepts to machine learning systems.
주요 기능
01Provides GF(3) typed effects for tracking agentic phases (Before/At/Beyond)
02Calculates Enumeration Entropy to identify fixed points of agency
03Defines impact through categorical equivalence class transitions
04Models prediction markets as categorical belief ANIMAs and liquidity media
05Implements Scholze-Clausen condensed mathematics for belief stabilization
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사용 사례
01Designing autonomous agents with mathematically verifiable equilibrium states
02Applying advanced topological mathematics to AI-driven world modeling
03Modeling decentralized prediction markets and complex belief systems
