Article / scholarly article

Audited Self-Improvement Loop for LLMs

K. Takahashi

Published
DOI
10.5281/zenodo.17188268

Abstract

It combines anytime-valid e-process auditing (with Ville gate ), finite-window non-vacuity ( FW-1 ), heavy-tail guards (Catoni clipping + sliding-window MGF), sequentially wired e-gates inside Dinkelbach ratio optimization , FKPP/Kingman -style speed KPIs with censoring-aware block bootstrap, and information floors via winsorized Pearson |r| , HSIC , and distance correlation (dCor) with permutation tests and residualization.

Keywords

  • AI
  • large language models
  • superintelligence
  • self-improving AI
  • audited optimization
  • e-process
  • anytime-valid
  • ville inequality
  • catoni clipping
  • sliding-window MGF
  • dinkelbach

Identifiers and source records