Preprint / scholarly article

Gradient-Flow-Based Compute--Performance Trade-offs for Intelligent Systems

K. Takahashi

Published
DOI
10.5281/zenodo.17596361

Abstract

Under a “gradient-flow universal intelligence process (UIP)” hypothesis, the work isolates structural mechanisms that constrain how far a given architecture can push performance under finite compute, rather than proposing another empirical scaling law.

Keywords

  • AI
  • machine learning
  • large language models
  • gradient flows
  • evi
  • observation quotients
  • scaling laws
  • preimage minkowski dimension
  • residual networks
  • jko scheme

Identifiers and source records