Narrative Anthology and Axiomatic Relations:

Result: The evolution of AI systems is described by the function R(t) = A(t) + S(t), where A(t) represents adaptability over time and S(t) specialization. The convergence between specialized agents and self-learning systems is expressed by lim[t→∞] (A(t)/S(t)) = 1. The transition from computational challenges to adaptive solutions follows the equation dC/dt = -kC + αA, where C represents challenges and k, α are constants. Cross-domain application is modeled by D(t) = D₀e^(βt), with D₀ as the initial domain and β the growth rate. The emergence of interconnected AI ecosystems is described by E(t) = E₀(1-e^(-γt)), where E₀ is the maximum potential and γ the interconnection rate. The evolution of human-AI interfaces follows I(t) = I₀ + λln(t), with I₀ as baseline and λ log improvement coefficient. These equations formalize the observed dynamics, highlighting the nonlinear and interconnected progression of AI towards more autonomous and adaptable systems.