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deltax

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Knowing when to stop: an edge-first approach to AI safety

Most AI safety discussions focus on what systems should do.

This work focuses on when nothing should happen.

We published an institutional, non-normative audit of a 27-document cognitive framework (CP27) built around one invariant:

STOP must be a valid output.

The framework enforces:

  • decision boundaries placed as early as possible,
  • strict human-only decision authority,
  • AI limited to measurement, verification, and traceability,
  • explicit STOP / SILENCE fail-safe mechanisms,
  • hard separation between narrative, methodology, and governance.

The logic is similar to edge security:
don’t react more — decide sooner, before complexity, cost, or risk accumulate.

No certification claims.
No normative authority.
Fully auditable, falsifiable, and institution-ready by design.

Audit + encapsulation (Zenodo):
https://zenodo.org/records/18172473

Interested in discussing structure and invariants — not beliefs.

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