A Method and System for Non-Destructive, Structured Provenance Tracking in Artificially Generated Code
As AI-generated codebases grow in scale, they suffer from "intent loss" - the cognitive state, reasoning, constraints, and specific prompts that led to each solution are typically discarded upon output. This defensive publication outlines a method to replace or augment traditional comments with embedded, machine-readable metadata fragments (Statefiles). These fragments are cryptographically linked to the code blocks they describe via a Semantic Lineage Chain (Provenance Chain), ensuring that the "soul" of the code (its provenance and intent) is preserved alongside the logic. The system includes lifecycle management protocols for production deployment, including stripping, compression, and shadow ledger preservation. This work is published as prior art under 35 U.S.C. § 102 to prevent future patenting of this methodology. It has been dedicated to the public domain under CC0 1.0 Universal. Originally published: December 14, 2025GPS timestamp: 2025-12-14 20:25:45 UTC (Oregon, 19-20 satellites, DGPS)Wayback Machine: https://web.archive.org/web/20251214212253/https://gist.github.com/slestak-OG/87565c6a7357fe38dbe9fe03365ddd48GitHub Gist: https://gist.github.com/slestak-OG/87565c6a7357fe38dbe9fe03365ddd48
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-12-14
- DOI
- https://doi.org/10.5281/zenodo.17932092
- Primary Topic
- Scientific Computing and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00