Preprint / scholarly article

Stable Self-Improving AI under Value-Anchored Natural-Law Gradient Flows

K. Takahashi

Published
DOI
10.5281/zenodo.17718314

Abstract

The design space is the Wasserstein space P2(Theta) over a metric hypothesis space (Theta, d_Theta), equipped with a value-shortfall functional Val*(mu) = integral V(theta) mu(dtheta) that aggregates deficits in alignment, performance, or physical feasibility.

Keywords

  • mathematical model
  • AI
  • self-improving AI
  • AI alignment
  • value alignment
  • gradient flows
  • value-anchored potential
  • natural-law specification
  • markov kernel stability

Identifiers and source records