Preprint / scholarly article

Silent Data Corruption--Limited Scaling Kinetics for Large-Scale AI Training

K. Takahashi

Published
DOI
10.5281/zenodo.18050287

Full text PDF (Zenodo)

Abstract

The preprint treats silent data corruption as a scaling limiter for large-scale AI training and proposes a telemetry-contract framework that fails closed on missing integrity evidence, producing certified progress and useful-compute floors under explicit coverage assumptions.

Keywords

  • silent data corruption
  • AI training
  • integrity checks
  • telemetry contract
  • fail-closed verification
  • certified progress
  • useful compute floor
  • evidence log
  • auditability
  • fault tolerance
  • large-scale systems

Identifiers and source records