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Self-correcting evidence · Research paper

Out-Reflect

Transformational Intelligence for Constructing Self-Correcting Evidence

Vincent Boucher · MONTREAL.AI
21 October 2026 · Substantively revised research edition · 23 pages · PDF, 171 KiB

What this paper explores

This manuscript constructs moment-shaped audit codes for a declared nonlinear polynomial family. Its designed experiments recover model coefficients despite bounded corruption of entire reference records.

Evidence boundary. The result is conditional on the declared target class, exact arithmetic, and fault assumptions. Source-group and beyond-budget counterexamples limit the assurance. No universal self-certification, neural improvement, or operational deployment is established. This publication is not an independent reproduction.

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