# Conclusion

<!-- 9 October 2026 publication, PDF-reconciled conversion source. Mathematical macros used below:
\TV = \operatorname{TV}
\Dtr = \operatorname{D}
\tr = \operatorname{tr}
\id = \operatorname{id}
\dd = \,\mathrm d
\pTV = \operatorname{TV}
\pVar = \operatorname{Var}
\pLaw = \operatorname{Law}
\pGam = \Gamma
\pUnif = \mathsf U
-->

<a id="paragraph-1"></a>

## Conclusion

 Reversible finite Gaussian dynamics can prepare a writer conditionally on its complete retained archive for a broad, explicitly non-Born class of original laws. The exact smooth map is essential: fine archive information invalidates an unrestricted writer-only premise, while full conditional physical scores and actual moments yield constructive quantitative bounds without a density cap. The same parent supports a separate physical receiver and an input-uniform finite-internal-reference instrument. These are positive mathematical results with identifiable statistical and control premises. Establishing those premises independently in a material apparatus, and extending the argument to repeated unknown inputs and arbitrary positional references, remain distinct scientific tasks.
