Mechanical Scientific Method for Deterministic Artificial Infinite Systems for Digital Longevity in an Analog Universe
Scientific discovery depends on more than an answer. Its evidence, unsuccessful attempts, corrections and reasoning must remain available to later investigators. This work develops the Mechanical Scientific Method: a way for agentic models to propose and execute experiments while an antigentic model or verification policy challenges what the results actually support. The purpose is scientific continuity. A valid claim should remain reconstructable when a model changes, a run ends, or another investigator attempts to reproduce it. The paper demonstrates deterministic replay of bounded verification rules and decisions, preservation of negative results, and long-duration inspection of two finite synthetic computing runs. Those results establish aspects of computational repeatability and custody; they do not establish unlimited machine operation, general intelligence, or biological aging intervention. The phrase artificial infinite system describes the intended architecture of a succession of recoverable scientific states. Digital longevity means preserving that scientific and computational history over repeated restarts in a physical world where hardware, storage and biological systems remain finite. The programme builds on separately published research: the Cytoplasmic Inheritance Timer and Shadow Dogma as aging hypotheses; XenoDisorder as computational evidence; Antigence and Anticube as challenge and classification concepts; and Fractal Custody Objects as a foundation for inspectable evidence. Each predecessor retains its own DOI and scientific limitations. AgenticLS and NewInML/HydraDG submission drafts provide additional methodological context, without being represented as externally accepted papers. The longer objective is to use this method to investigate biological perturbations and the mechanisms of aging, retaining failures and inconclusive observations just as carefully as successes. Those future biological claims are not presented as results of this manuscript.
Authors
- Lee Byron (ORCID: https://orcid.org/0000-0002-4925-4795)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-10-08
- DOI
- https://doi.org/10.5281/zenodo.23243249
- Primary Topic
- Scientific Computing and Data Management
- Type
- preprint