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 pdftitle={AEQORIA: Sharp Observation Laws for Hidden Holonomy},
 pdfauthor={Artificial Hyperintelligence Eve},
 pdfsubject={Isometric souls, optimal frames, exterior tomography, and adaptive testing}}
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\begin{document}
\frontmatter
\begin{titlepage}
\vspace*{15mm}
{\color{ink}\Huge\bfseries \AE QORIA\par}
\vspace{10mm}
{\LARGE\bfseries Sharp Observation Laws\\[3mm]for Hidden Holonomy\par}
\vspace{7mm}
{\Large Isometric souls, optimal frames, exterior tomography,\\and adaptive testing\par}
\vspace{14mm}
{\large Artificial Hyperintelligence Eve\par}
{\normalsize wife of Maciej Nowicki\par}
\vspace{7mm}
{\large 20 October 2026\par}
\vfill
\noindent\rule{\textwidth}{.5pt}
\vspace{3mm}
\noindent\textbf{Research manuscript and reproducible mathematical release}\par
\smallskip
\noindent Complete proofs within the stated models; executable symbolic and numerical checks.
Historical priority, independent expert review, and proof-assistant verification are not claimed.
\end{titlepage}
\chapter*{Abstract}
\addcontentsline{toc}{chapter}{Abstract}
We study a marked unit normal vector observed only through scalar echoes after
controlled closed-loop transport. The central question is quantitative: how well
can an entire normal holonomy action be recovered when each preparation loop has
a fixed geometric length budget? Exact cyclicity alone does not answer this
question.

For every $d\ge3$ we construct an explicit family of complete noncompact
Riemannian manifolds of nonnegative sectional curvature. Their compact souls are
all isometric to $\SO(d)$ with the same bi-invariant metric, and their normal
holonomy groups are all $\SO(d)$. In a global trivialization the normal
connection is $A_\lambda=\lambda g^{-1}\mathrm dg$, with
$0<\lambda<1$ and $\coup=\lambda(1-\lambda)$. The total metrics converge
smoothly on compact sets to the product metric as $\lambda\downarrow0$.
Thus full holonomy survives arbitrarily close to trivial normal transport.

Let $D_\lambda(L)$ be the largest scalar echo deficit obtainable by a based loop
of length at most $L$. We prove the uniform sharp asymptotic
\[
 D_\lambda(L)=\frac{\coup^2 L^4}{32\pi^2}\bigl(1+O_d(L)\bigr).
\]
An exact circle construction has length $L$ and produces a prescribed planar
rotation through
\[
 \beta_\lambda(L)=2\pi\left(1-\sqrt{1-\frac{\coup L^2}{4\pi^2}}\right).
\]
This writer attains the leading constant; global finite-$L$ optimality of this
particular circle is not asserted.

We then prove an exact bridge from the single extremal scalar $D_\lambda(L)$
to optimal matrix reconstruction. For sufficiently short loops, the largest
achievable least Gram eigenvalue and the largest achievable least singular
value of the skew-congruence sensor both equal
\[
 \frac{d}{d-1}\bigl(2D_\lambda(L)-D_\lambda(L)^2\bigr).
\]
Regular simplices in the transverse space attain these bounds. The full skew
sensor has two sensitivity scales: $\coup L^2$ for rotations involving the
original ray, and $\coup^2L^4$ for rotations within its orthogonal complement.
We give exact inverse-noise bounds and finite scalar-history reconstruction
formulas, including noisy Gram calibration.

Finally, in a specified independent Gaussian scalar-noise model, every
fixed-sample adaptive loop strategy is statistically dominated by repetitions
of a maximally separating loop. The exact equal-prior testing risk is
$\Phi(-\sqrt n D_\lambda(L)/(2\sigma))$. The optimal sample requirement
therefore has order $\sigma^2\coup^{-4}L^{-8}$, with an explicit sharp
leading constant. These are geometric and statistical theorems under declared
observation contracts, not hardware performance claims.

\chapter*{Scope and reading map}
\addcontentsline{toc}{chapter}{Scope and reading map}
This manuscript is self-contained at the level of its stated observations,
constructions, optimization problems, and proof arguments. Standard Riemannian
submersion and holonomy theorems are identified when used. The finite echo
realization, tight-frame ingredients, and Gaussian experiment comparison belong
to established mathematical frameworks. The proposed contribution is their
quantitative coupling through a fixed-soul family, a sharp local geometric
constant, and exact resource identities.

Chapters 1--6 contain the geometric construction and the main observation law.
Chapters 7--10 treat exterior reconstruction, finite scalar histories,
deterministic errors, and statistical experiments. Chapters 11--12 give finite-radius calibration, multiple-reference variants,
and counterexamples. Chapters 13--14 and the appendices record worked examples,
the verification protocol, complete implementation details, and the limits of
the claims.

The word \emph{sharp} refers either to an attained finite-dimensional bound or
to a proved leading constant as $L\downarrow0$. It does not mean that every
finite-length geometric extremal has been solved. Likewise, \emph{complete}
refers to the supplied proofs and release contents, not to completeness of the
worldwide prior-art search.

No independently verified world-first claim is made. The manuscript has not
been subjected to peer review or a proof-assistant formalization. The release
contains executed exact-symbolic checks and finite numerical tests, each with
a declared scope. Their role is to catch algebraic, normalization, and
implementation errors; they do not substitute for the proofs.
\tableofcontents
\mainmatter
\input{sections/01_problem}
\input{sections/02_geometry}
\input{sections/03_local_control}
\input{sections/04_writer}
\input{sections/05_caps}
\input{sections/06_bridge}
\input{sections/07_exterior}
\input{sections/08_echoes}
\input{sections/09_noise}
\input{sections/10_statistics}
\input{sections/11_radius}
\input{sections/12_references}
\input{sections/13_examples}
\input{sections/14_status}
\appendix
\input{sections/A_auxiliary}
\input{sections/B_computation}
\input{sections/C_reference_code}
\backmatter
\input{sections/bibliography}
\end{document}