Adversarial Self-Registration: A Working Protocol for Keeping a Machine-Assisted Theory Programme Honest

Abstract

The working companion to the truth-maintenance paper above, documenting the discipline the programme's registered predictions actually run on. Large language models make theoretical claims cheap: a machine-assisted physics programme can generate plausible derivations, matching coefficients, and post-hoc rationalisations faster than any referee can check them, so the classical failure modes of self-deception — forking paths, silently adjusted acceptance criteria, tautologies presented as successes, quietly superseded claims — now operate at machine speed. The countermeasure is a protocol of adversarial self-registration whose seven disciplines are mechanical rather than aspirational: acceptance bars committed to version control before data are touched; every quantitative claim living in a self-asserting gate whose draft expectations, when killed by the computation, stay in the code as a visible fossil; superseded claims corrected loudly, never overwritten; a linter that blocks any push contradicting the claim ledger; analysis conventions amendable only before unblinding; registered bars never adjusted after data; and predictions frozen in public, timestamped, machine-readable form. The evidence offered is not a virtue claim but a receipt set: six public registrations with frozen kill rules published in eight days; one dark-energy registration already killed internally under its own rules and left frozen rather than refitted; a registered acceptance bar missed by 0.005 dex on a single point and honoured — the paper shipped with its verdict section vacant; a convention amendment committed while the deciding scan was still running; a sign-convention bug found by a designed eight-variant control matrix, with every contaminated result voided by name, including an apparent 2.6σ "detection"; and a failure museum of the programme's own withdrawn mechanisms. The protocol is situated in the blind-analysis and preregistration traditions, its threats to validity stated — including the card-stuffing attack that cheap registration invites — and a minimal kit extracted that any machine-assisted research effort can steal. It certifies honesty, not correctness: the note is timestamped while most registered discriminators remain unresolved and after the first internal kill, so its claims about how verdicts will be faced are themselves auditable.

Keywords

How to cite

Elliman, D. (2026). Adversarial Self-Registration: A Working Protocol for Keeping a Machine-Assisted Theory Programme Honest. Neuro-Symbolic Ltd technical report. https://doi.org/10.5281/zenodo.21219212

@techreport{elliman2026adversarialselfregistration,
  author      = {Elliman, David},
  title       = {Adversarial Self-Registration: A Working Protocol for Keeping a Machine-Assisted Theory Programme Honest},
  institution = {Neuro-Symbolic Ltd},
  year        = {2026},
  doi         = {10.5281/zenodo.21219212},
  url         = {https://neusym.ai/papers/adversarial_self_registration}
}