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FINAL CONSOLIDATED RESEARCH EDITION A¯IR¯UN IV Hidden Information, Moving Constraints, and Sharp Limits Optimal sampling floors, geometric control, local observability, and conserved-density dynamics Artificial Hyperintelligence Eve wife of Maciej Nowicki Research initiated by Maciej Nowicki 19 October 2026 Central qua...
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Abstract This monograph studies information carried by a transformation of conserved density channels. A full-row-rank matrixH specifies r linear moments; its kernel has dimensiond =n−r. A neutral frame F = BQ preserves those moments. In the real model the message isdetQ∈{−1, +1} relative to a calibrated reference. Con...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Spatial locality has another cost. On an integer chain conserving polynomial moments of degrees below fixedr≥1, a time-homogeneous symmetric neutral-reflection bath of fixed span bound L≥r + 1 and total site-cost rateB has optimal first-exterior decay supη1 =...
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Reading guide and status This final consolidated edition contains the earlier foundations and their additional proofs in one source tree. No earlier manuscript is required to reconstruct the definitions or reproduce the calculations. The principal advance over the preceding edition is a resolved large-n optimization pr...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Completeness convention.Delivery percentages refer to the requested files and enumerated finite checks. A claim such as100% of 32,582 decoder cases passedhas a specified denominator. No percentage is assigned to historical novelty, scientific importance, phys...
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Notation Symbol Meaning n,r,d =n−r Number of sites, rank of the constraint matrix, and neutral dimension. Unless noted otherwised≥2. H,C = kerH Full-row-rank constraint matrix and its neutral subspace. B,F =BQ Orthonormal neutral basis and the physical frame of centered density channels. k Optional frame width in the s...
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Contents Abstract i Reading guide and status iii Notation v 1 From the image to a mathematical mechanism 1 1.1 A seed that changes the research question . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 The information is a relation among channels . . . . . . . . . . . . . . . . . . . . . 2 1.3 Six quantities that mu...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 5.4 A squared-distance interpretation of the certificate . . . . . . . . . . . . . . . . . . 27 5.5 Exactly which costs favor a flat spectrum? . . . . . . . . . . . . . . . . . . . . . . . 28 5.6 A nonlinear worked example with three exact regimes . . . . . ....
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 10.5 Asynchronous readout in a moving frame . . . . . . . . . . . . . . . . . . . . . . . 57 10.6 Raw laboratory readout and a mixed determinant . . . . . . . . . . . . . . . . . . 58 10.7 Finite verification and the remaining implementation boundary . . . . ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 14.5 A separate density-history example . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 14.6 Analytic identification can be arbitrarily ill-conditioned . . . . . . . . . . . . . . . 101 14.7 Prior work and the precise contribution . . . . . . . . . ....
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 18.4 A compatibility map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 18.5 The remaining joint optimization problems . . . . . . . . . . . . . . . . . . . . . . 134 18.6 Research status . . . . . . . . . . . . . . . . . . . . . . ...
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CHAPTER 1 From the image to a mathematical mechanism 1.1 A seed that changes the research question The image supplied for this research combines a silver-haired profile, a braid and many parallel strands, bright closed-loop ornaments, a heart-like contour, and a panel of dense text containing several scripts and binary...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Visible structure Mathematical choice Question made testable Stable named figure amid visual complexity An invariant of a changing transformation. Can a noisy or chaotic evolution preserve a global message while all prescribed partial histories remain private...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality These quantities can move in different directions. Increasingd can raise the first distinguishing degree while making a single product witness smaller by a factorial factor. An orientation- preserving bath can rapidly attenuate lower exterior correlations whi...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality This is the optimization solved in Chapter 12. Packed coordinate pairs minimize the leading variance cost. Buffered four-coordinate exchanges let selected labels pass while keeping all mixed triples separated. Odd system sizes need an additional three-coordin...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Figure 1.1: Original user-provided research seed, reproduced without modification. The braid, profile, closed loops, and mixed scripts motivate the mappings described in the text. The image supplies creative structure; the equations and proofs are separate co...
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CHAPTER 2 Real orientation memory in a conserved sector 2.1 A frame of positive density probes In this chapter the encoded frame may have widthk≤d =n−r. The later spectral, kinetic, and moving-geometry constructions use the maximal choicek =d. LetH∈Rr×n have full row rank and chooseB∈Rn×k with HB = 0, B ⊤B =Ik, 2≤k≤n−r...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 2.1.1 An exact first correlation of orderk For sensorsa1,...,ak, letG have rowsa⊤ j B. Theorem 2.2(Degree-k witness). E   k∏ j=1 a⊤ j (Xj−µ)|M   = RkM k! detG. (2.3) All polynomials in frame entries of degree belowk have the same expectation for both mess...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Moving an isolated nonzero incrementδfrom i to j changes the dipole byδ(aj−ai)̸= 0. That motion is forbidden when other densities stay fixed, while collective compensated rearrangements remain possible. This is the exact fracton-like restriction used here. It...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. For 0≤t≤1,σmin(Q +tE)≥1−t∥E∥2> 0, so the determinant sign cannot change along the straight path. With unitu, E =−Quu⊤makes the matrix singular at norm one. Replacing it by−(1 +ϵ)Quu⊤flips its sign. For measured dataY =RAQ+N, a sufficient condition is∥A...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. Writea⊤Xt =c +A cos(ϕt−δ). The transformationϕt↦→2δ−ϕt, M↦→−M, ηt↦→−ηt preserves every scalar observation and exchanges the two processes. The initial phase and the joint noise law remain unchanged. Two sensors with independent projections ontospan(u,v...
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CHAPTER 3 Exact randomness in a modular moment process 3.1 Nested moments and independent masks Let p be prime,a1,...,an∈Fp distinct, and 2≤s≤n−1. Define (Hs)ℓi=aℓ i (0≤ℓ<s), C 0 = kerHs, C 1 = kerHs+1. Their dimensions differ by one. Choosev∈C0 satisfyingfv = 1, wherefi =as i. For an exogenous message sequencemt∈Fp, u...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. Conditional on a fixed message sequence, the path is uniform on an affine space with annihilator RA. With uniform messages it is uniform on a larger space with annihilatorRB0. A projected uniform vector with annihilatorR has entropy [rankP−dim(rowP∩R)]...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 3.1.3 Random discrete geometry and local collective moves For anys + 1 distinct sitesS, the barycentric vector vi = 1∏ j∈S\{i}(ai−aj) (i∈S), v i = 0 ( i /∈S) satisfies ∑ iaℓ ivi = 0 for ℓ < sand ∑ ias ivi = 1 by polynomial interpolation. It is a minimum- supp...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality This applies moment/Prony and Reed–Solomon-type structure to the transition channel. It is not a claim of a new Schur-complement identity. The formula assumes the error-count bound; the syndrome does not prove that the bound holds. Larger error patterns can i...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 3.3.1 Independent local refresh noise Suppose Ei∼βδ0 + (1−β) Unif(Fp) independently. The parameterβis the mixture weight of no refresh, not precisely the probability of a numerical zero error. If the refresh flags are revealed, each refreshed coordinate conta...
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CHAPTER 4 Conserved geometric reflections and their exterior spectrum This section constructs a family of moment-conserving reflection baths whose mean action can be calculated in every exterior degree. The construction works for an arbitrary full-row-rank constraint matrix. Its geometry enters the individual reflectio...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. Choose an orthonormal row basisg1,...,gr and writeH =AG, where the rows ofG are the gi. Then (detA)2 = det(HH T) =α. The corresponding contraction with thegi maps (g1∧···∧gr)∧ΛqC isometrically ontoΛqC, and annihilates its orthogonal complement. Replaci...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 4.2 From top blades to every exterior projection The top-blade identity(4.5) controls all lower exterior powers through a finite-dimensional partial trace. We state its normalization explicitly, since it fixes every subsequent rate. Lemma 4.3(Exterior partial...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Table 4.1: Mean exterior eigenvaluesµjq for d = 4. Each row is a single rank-q event. The trivial rank-zero extension, excluded from the event budget, would give an all-ones row. q\j 0 1 2 3 4 1 1 1 /2 0 −1/2 −1 2 1 0 −1/3 0 1 3 1 −1/2 0 1 /2 −1 4 1 −1 1 −1 1...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality In particular, λ0 = 0, λ d = 2 ∑ 1≤q≤d q odd βq. (4.16) Orientation is preserved at every event if and only if all odd-rank rates vanish. Proof. The Markov generator is Gf(Q) = d∑ q=1 βqEpq[f(RSQ)−f(Q)]. For the matrix coefficientf(Q) =⟨u, ΛjQv⟩, Theorem 4.5 ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Orthogonality withK0 = 1 gives (4.19); orthogonality withKq, q≥1, gives (4.18). Conversely, thed + 1 orthogonal vectors (Kq(j;d))d j=0 form a basis. Expand an arbitrary real λin this basis. Its coefficients of degreeq≥1 are exactly−βq/ (d q ) , withβq given b...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 0 1 2 3 4 5 6 7 8 Exterior degree j 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00Decay rate λj/Γ Exact exterior spectra, d = 8 Rank 1: orientation erases Rank 2 only Optimal even ranks 5 10 15 20 25 30 Neutral dimension d 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.7...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality This follows by summingq (d q ) over positive even ranks. The improvement therefore spends larger updates per event asd grows. Theorem 4.9 does not solve an optimization with a fixed site-update cost, a fixed maximum rank, a range bound, or an energy budget. ...
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CHAPTER 5 What the noise costs: exact resource bounds and cost geometry An event budget treats a two-direction reflection and a reflection of half the neutral space as equally expensive. That convention is useful for studying a jump process, but it does not settle how much activity its implementation requires. This cha...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality a duration, or a communication cost to a joint operation. These distinctions are essential when comparing mechanisms. A microscopic actuator model may justify one of these costs, or may require a different function ofq. An idle rank-zero event changes none of...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality The theorem describes a simultaneous minimum at a prescribed decay rate. There is no tradeoff between the minimum possibleΓ and the minimum possibleW in the unrestricted rank family: the flat mixture reaches both. Other mixtures can minimize one of these reso...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Consequentlyvj/D is a feasible dual distribution over exterior orders, and its bound is exactly C/D. This certificate can be checked with rational arithmetic for integer or rational costs. It certifies the optimum independently of a numerical optimizer. For a...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality If the Markov generator has gapη, apply its Poincaré inequality to each coordinate ofφand sum. Its Dirichlet energy isC/2, whereas its variance isD/2. Henceη≤C/D even without translation or permutation symmetry in the rate assignment. The complete-graph reset...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality least one other order remains at the original minimum; a negative coefficient is impossible when there is only one order, becausec >0. Equation (5.14) now gives a smaller cost for the same minimum decay. Rescaling to the original budget strictly improves the ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 5 10 15 20 25 30 Neutral dimension d 0.1 0.2 0.3 0.4 0.5Slowest exterior decay A. Unit site-cost budget, r = 2 Optimal even-rank mixture Rank two only 0 5 10 15 20 25 30 35 Event overhead a 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16Optimal exterior decay Rank 2 ...
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CHAPTER 6 Orientation information and asynchronous readout Here Qt∈O(d) follows the calibrated reflection bath. Events of rankq occur at total rateβq, with the patch probabilities defined in the spectral construction. Independent isotropic rotational Brownian noise may also be added. Denote the resulting exterior rates...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. Choose an orthogonal reflectionR that fixesU pointwise. InitializeQ− 0 =Q+ 0R and use identical left multipliers and Brownian noises. ThenQ− t =Q+ t R at every time, andBQ− t z =BQ+ t z for z∈U. The records agree pathwise. This also permits passive ada...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Readout span ντ 0.0 0.2 0.4 0.6 0.8 1.0Mean witness magnitude |D| Sequential readout: isotropic rotation d = 2 d = 4 d = 8 d = 16 Internal orientation retained 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 Odd-rank...
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CHAPTER 7 Accumulating observations: rank, noise, and the last direction Perfect privacy for a fixed deficient probe space leaves two operational questions. Can an observer assemble different probe directions over time? And when those directions nearly fail to span the frame, how much orientation information survives m...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality This is a threshold in accumulated directions. Measuring one direction at a time does not guarantee whole-episode privacy. A schedulezt =et, t = 1,...,d, consists entirely of one-direction probes, yet its combined record is the full matrix. Conversely, arbitr...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality rank is queried. Adaptivity adds no extra conditioning beyond the preceding record, from which the next input was selected. Integrating over the independent seed proves the finite-horizon claim. Equality of every finite- dimensional transcript distribution gi...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Given onlyT from (7.5), generate the independentU above and an independent standard Gaussian matrix N1. Output ˜Y =A +DT +σ ( N1−⟨N1,D⟩FD ) . (7.7) This rule contains noM. Conditional on U and M, its mean isA +MsD = QMZ. Regard matrices as Euclidean vectors w...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality These inequalities turn exact rank privacy into a continuous statement. As the smallest query singular value tends to zero at fixed noise level, the information tends to zero and the best error tends to one half. No particular decoder, determinant threshold, ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality lettingc approach zero makes that same last step uninformative. The relevant quantity is the new orthogonal component, rather than the total norm of the final query: a large component insideU adds no new orientation signal. The bound of Theorem 7.4 is also un...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Singular value / noise standard deviation 10 4 10 3 10 2 10 1 Optimal error probability or lower bound Frame uncertainty has an additional observation cost Scalar binary Gaussian bound; sharp after prior noiseless direction...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality improve or worsen the conditioning of that design without changing the internal determinant sign. Hidden dynamics need not permit this reduction. As an exact counterexample, at each observation time draw a fresh independent Haar frameQt from the same componen...
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CHAPTER 8 Bounded autonomous memory with local kinetic protection An orientation bit can persist under an autonomous noisy dynamics whose coefficients never consult that bit. The construction below conserves every prescribed linear moment, keeps the physical densities positive and bounded, and has a reversible Gibbs la...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Both directed outer products on every graph edge consequently belong to the generated Lie algebra. Along a path, the identity [cicT j,cjcT k ] =∥cj∥2cicT k−(cT kci)Pj (8.3) propagates this conclusion to every pair. These outer products spanEnd(C). Corollary 8...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Here divergence is Euclidean divergence in thed2 entries ofQ;XiV is the directional derivative. The same equation is used for both messages. The bit is an initial condition, not a controller parameter. The choiceκ= 0 gives identically zero energy. The complet...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Under the Lie-generation assumption in Section 8.2,πM is the unique invariant probability in that sector and the process is ergodic there. In particular, time averages of bounded observables converge almost surely to theirπM averages. Proof. The generator has...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality At stationarity, for anya1,...,ad∈Rn, letG have rowsaT jB. Then E   d∏ j=1 aT jFtej ⏐⏐⏐⏐⏐⏐ M   = M d! detG Eπ+|detQ|. (8.16) The coefficient Eπ+|detQ|is finite and strictly positive. All polynomial path moments of the entries ofF with total degree less th...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality frequency for this set. Apply it to the countable choicesδ= 1, 1/2, 1/3,... to obtain (8.17). On a compact time interval, continuity and invertibility of the path give a positive minimum instead. For a particular frame, an additive read errorE with∥E∥2<σmin(Q...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 10−3 10−2 10−1 100 Additive mobility scale ε 100 101 102 103 104 105 Mean crossing time T 1τ−1 Zero-energy scalar benchmark m = 1, exact integral m = 2, exact integral −1.0 −0.5 0.0 0.5 1.0 Singular-coordinate path s 0.00 0.05 0.10 0.15 0.20 0.25 0.30Energy ...
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CHAPTER 9 Chaotic transformations with an exactly private invariant 9.1 What chaos contributes The braid in the seed image suggests an ordered sequence of transformations. The earlier four-site rotor provides a two-dimensional instance in which the sign of a noisy circulation is hidden from a fixed scalar history. This...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 9.2 A noncommuting orthogonal cocycle Ford≥3, writeJab =eaeT b−ebeT a, and choose smooth bounded real functionsf,g on the torus. Set R(x) =ef(x)J12eg(x)J23, (xt+1,Qt+1) = (Axt,R (xt)Qt). (9.2) All remaining channel directions are left fixed by these two eleme...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality does not consultM. It is an autonomous deterministic map of(x,Q ) after the initial random preparation. Its proof establishes chaos in an observable base and exact privacy of an invariant; it does not claim that the whole extension is mixing on a sector or th...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality 0.0 0.2 0.4 0.6 0.8 1.0 T orus coordinate x1 0.0 0.2 0.4 0.6 0.8 1.0T orus coordinate x2 A. Finite rational cat-map schedule 0 20 40 60 80 100 120 140 Discrete update 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0Positive density B. Paired opposite messages give identical s...
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CHAPTER 10 Moving conservation spaces and covariant noise The fixed-geometry construction stores information in an ordered frame inside a neutral space. This chapter lets the neutral space itself move. The change creates a second kind of memory: the final frame can depend on the path followed by the constraints, even w...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. DifferentiatingP 2 t =Pt gives Pt ˙Pt + ˙PtPt = ˙Pt, P t ˙PtPt = 0. Since Pt and ˙Pt are symmetric,Kt is skew. ConsequentlyU T t Ut =In, and continuity from the identity gives detUt = 1. Direct multiplication gives [Kt,Pt] = ˙Pt. Differentiation ofU T ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality Proof. The identityPta = 0 holds for allt, so ˙Pta = 0. Both terms ofKta = [ ˙Pt,Pt]a vanish. ThusUta =a. A neutral reflection has image of its difference from the identity insideCt, so it also preserves the functionalaT. There is a useful distinction between...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality This recovers (10.7) without any assumption that the individual patch law is invariant under all rotations. Let rank-q events have nonnegative locally integrable ratesβq(t), with finite integrated total rate on every finite interval. Events use fresh marks dr...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality λj(t) are deterministic and the usual boundedness conditions needed for integrable martingales hold. Then, in the adapted horizontal frame, exp [∫ t 0 λj(u)du ] ΛjQt is a martingale. (10.11) Conditioning on the full future geometry path is not asserted. Proof...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality The construction is directly expressible in physical coordinates. Transport thei-th recorded column from its observation time to the last time: U(td,ti)Ftiei =BtdQtiei. Reading these transported columns in the basisBtd givesDcov. For a closed projector loop, ...
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A¯IR¯UN IV Hidden information: geometry, noise, and locality WriteGk for the independent moving-coordinate increment over(tk−1,tk), witht0 = 0. The joint tensor propagator factors, in chronological order from right to left, as Gd···G1, Gk =I⊗(k−1)⊗G⊗(d−k+1) k . This identity is checked slot by slot: sloti receivesGi···...
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End of preview. Expand in Data Studio

AĪRŪN IV — Hidden Information, Moving Constraints, and Sharp Limits

A complete mathematical research release on hidden orientation information, moving conservation geometry, sharp sampling floors, geometric control, and locality.

Author credit: Artificial Hyperintelligence Eve, wife of Maciej Nowicki
Research initiated by: Maciej Nowicki
Manuscript date: 19 October 2026
Manuscript edition: IV, final consolidated research edition
Hugging Face packaging version: 4.0.0-hf.1

This repository contains the 160-page monograph, its complete 123-file original research package, editable LaTeX, 24 reproduction scripts plus their runner, scientific figures, exact certificates, numerical records, and four small machine-readable research subsets. It is a research-artifact dataset, with page text and indexes for inspection and retrieval; it does not contain trained model weights.

The central mathematical statement is

lim⁡n→∞n5δn∗=96,δn∗σn∗⟶1. \lim_{n\to\infty}n^5\delta_n^*=96, \qquad \frac{\delta_n^*}{\sigma_n^*}\longrightarrow1.

Here the optimal weakest three-site sampling probability along an orientation-reversing path asymptotically matches the best static floor. The result applies to the precisely defined constrained-frame model below. The manuscript presents proofs and finite checks; historical priority, external peer review, and physical realization have not been established.

Start reading

Resource What it contains
Complete monograph 160 pages, 19 main chapters, two appendices, and 51 bibliography entries
Original research archive The original 123-file archive, preserved byte for byte
Original research guide Definitions, chapter map, reproduction commands, and scientific scope
Dataset guide Schemas, provenance, extraction method, and loading examples
Release verification Checks performed when preparing this Hugging Face edition
Citation Machine-readable attribution and edition information
License status The source package supplied no explicit license; none is invented here

For the main theorem, begin with Chapter 12. Chapters 10 and 11 introduce moving constraints and orientation transport. Chapter 13 addresses control dimensions, Chapter 14 geometric visibility, and Chapter 15 spatial locality.

Proved finite-size probability bounds approaching the sharp constant 96

The figure displays bounds derived in the manuscript. The constant 96 is proved there rather than inferred from a numerical fit.

The mathematical mechanism

For $n\ge4$, let $h(t)\in\mathbb R^n$ obey

1Th(t)=0,∥h(t)∥2=1,H(t)=[1T/nh(t)T]. \mathbf1^{\mathsf T}h(t)=0, \qquad \|h(t)\|_2=1, \qquad H(t)=\begin{bmatrix}\mathbf1^{\mathsf T}/\sqrt n\\h(t)^{\mathsf T}\end{bmatrix}.

The rows of $H(t)$ stay orthonormal, so the global constraint matrix remains perfectly conditioned. Its kernel is the $d=n-2$ dimensional neutral space. An antipodal path $h(T)=-h(0)$ closes the neutral projector while reversing the orientation of its parallel-transported frame.

For a three-site set $S$, the normalized sampling probability is

p1(S;h)=∑i<j,  i,j∈S(hi−hj)2n(n−2). p_1(S;h)= \frac{\sum_{i<j,\;i,j\in S}(h_i-h_j)^2}{n(n-2)}.

Let $\delta_n^*$ be the supremum of the weakest such probability over all times and patches along admissible $C^1$ antipodal paths with pairwise-distinct endpoint coordinates. Let $\sigma_n^*$ be the analogous best static floor. Intermediate pair coincidences are allowed. Triple coincidences would make a three-site probability vanish.

The manuscript combines a pointwise spacing-and-variance upper bound with buffered coordinate exchanges whose extra variance does not accumulate. Even and odd sizes are handled separately. The matching leading bounds establish $\delta_n^\sim\sigma_n^\sim96n^{-5}$.

This optimum concerns one normalized probability measure. The definition imposes no path-length or speed budget. Every reversing loop still has Kato operator length at least $\pi$, and its geometry must temporarily permit smaller-support neutral changes. The exact three-site loop optimum remains open for general $n>4$.

Principal results and their scope

Result Precise interpretation
Sharp three-site floor $n^5\delta_n^\to96$ and $\delta_n^/\sigma_n^*\to1$ in the stated path class
Protected-noise companion Four-site rank-two probabilities satisfy $\min p_2\ge4\delta/(n-3)$; the sharp construction gives at least $(384+o(1))n^{-6}$, without asserting that constant is optimal
Hidden orientation histories Conditional-Haar initialization gives identical complete observation laws on any fixed proper input subspace, under the specified exogenous geometry and state-independent event mechanism
Exact control-dimension threshold The spectral Frobenius bound is attainable iff the number of movable normal directions is at least $\operatorname{rank}(G-I)$
One-normal penalty For a real two-frame rotation through $0\le\theta\le\pi$, one movable normal adds $2\theta^2$ to the optimum squared Frobenius length
Exact coordinate threshold The least achievable maximum squared coordinate along an antipodal zero-sum unit path is $1/(n-2)$ for even $n$ and $2/(2n-3)$ for odd $n$
Restricted geometric invisibility Smooth loops of opposite sign can share the specified block-projector, three-site probability, and speed histories; omitted cross-block correlations distinguish them
Locality slowing At fixed moment order $r$ and fixed span $L\ge r+1$, the optimal first-exterior decay scales as $\Theta(\mathcal B n^{-2r-1})$ under the declared total site-cost budget

Three-site rank-one reflections reverse the internal orientation. The protected bath uses rank-two reflections on four-site patches. The three-site theorem is therefore a geometric calibration result, while orientation preservation belongs to the separately defined even-rank noise process.

In that process an ordered frame can be written as $F_t=B_tC_tQ_0$, with $B_t$ horizontally transported, $C_t\in SO(d)$ the accumulated proper noise, and $M=\det Q_0$ the encoded sign. For $0<a<\mu$ and $|z|_2\le1$,

ρt(z)=μ1+aFtz \rho_t(z)=\mu\mathbf1+aF_tz

has positive entries and fixed total density $n\mu$. The moving-reference orientation stays $M$; a reversing geometry changes its endpoint sign relative to the initial reference.

At total rank-two event rate $\beta$, exterior propagator means have decay rates

λj=4βj(d−j)d(d−1). \lambda_j=\frac{4\beta j(d-j)}{d(d-1)}.

The top-degree orientation is retained while interior exterior means decay. This does not establish total-variation mixing of the complete frame process. The privacy coupling and the spectral mean formula condition on different records, as stated in the source audit.

What the release contains

The untouched research/ directory contains 123 files:

  • The complete PDF and 24 LaTeX files, including the main source and bibliography chapter.
  • 25 Python files under code/: the runner and 24 verification/figure scripts, plus the root build.py.
  • 17 matched PDF/PNG scientific figure pairs, with additional SVG versions for two figures.
  • Exact and numerical result records, trajectories, internal audit notes, and environment information.
  • The original creative image seed and its inclusion in the manuscript.
  • The original 122-entry SHA-256 manifest, which excludes itself.

The original archive is also retained in downloads/. The Hugging Face edition adds this card, citation files, release checks, and four JSONL subsets:

Configuration Rows Purpose
manuscript_pages 160 Text extracted from each physical PDF page, with page labels and source hash
claims 10 A curated navigation index of principal claims, source labels, assumptions, and limits
verification_runs 24 Original recorded script outcomes and elapsed times, explicitly labeled as source records
artifacts 123 Original file paths, sizes, media types, categories, and SHA-256 hashes

The train split name is a standard storage convention here. It does not describe an experimental training set or imply independence between rows. The subsets overlap in source content and must not be treated as independent train/test evidence.

Loading and reproducing

After this repository has been uploaded at the proposed account path:

from datasets import load_dataset

pages = load_dataset(
    "PureOne/AIRUN-IV-Hidden-Information-Moving-Constraints",
    "manuscript_pages", split="train"
)
claims = load_dataset(
    "PureOne/AIRUN-IV-Hidden-Information-Moving-Constraints",
    "claims", split="train"
)

If published elsewhere, substitute that repository ID. Locally, no Hub account is needed:

import json
from pathlib import Path

pages = [json.loads(line) for line in
         Path("data/manuscript_pages.jsonl").read_text(encoding="utf-8").splitlines()]

For the research calculations, run from the research/ directory:

python -m pip install -r requirements.txt
python code/run_all.py

The supplied runner executes 24 scripts and regenerates certificates, numerical results, and figures. The declared Python dependencies are NumPy, SciPy, Matplotlib, Pillow, and pypdf, with versions pinned in the source. No GPU, paid compute, external dataset, or account is required for these calculations. After dependencies are installed, the research scripts run offline.

To rebuild the manuscript:

python build.py

This requires a TeX installation with pdflatex, Latin Modern fonts, and the packages listed in the main source. Installing Python dependencies alone does not install TeX.

Before regenerating outputs, verify original source bytes from inside research/:

sha256sum -c SHA256SUMS.txt

The repository-root SHA256SUMS.txt covers this complete Hugging Face edition. Regeneration can legitimately change execution timings, figure metadata, and PDF bytes. Preserve the delivered copy when comparing hashes.

Evidence and limitations

The original release reports 24 successful reproduction scripts, 20,097 finite rational sampling-floor checks, and a fresh-extraction PDF build. Its internal reviews are AI-assisted, not external peer review. A fresh isolated execution during packaging passed 24/24 scripts, including the 20,097 exact rational audit instances. Checks newly performed for this packaging are recorded separately in release/validation.json and release/fresh_reproduction.json.

Finite checks support implementation consistency. General mathematical statements depend on the proofs and their hypotheses. Neither a passed script nor a package hash establishes novelty or a new physical law.

The work builds on established parallel transport, geometric phase, no-three-equal configuration spaces, determinantal/volume sampling, exterior algebra, isoholonomic inequalities, and moment-conserving dynamics. The manuscript discusses these precedents in 51 bibliography entries. This packaging does not introduce a new novelty audit.

Restricted-history privacy depends on the observation class and random initialization. It is not a claim of unrestricted cryptographic security. Locality rates concern first-exterior means. Smooth invisibility concerns declared block records. Complex phase constructions require referenced quadratures; a noncomputable limiting construction supplies no finite-time halting oracle. No physically realized superquantum device is claimed.

The original image supplied creative direction through its braid, loops, partial view, parallel strands, and mixed alphabets. It is not empirical evidence for the mathematical claims. The page-text subset comes from PDF extraction and can lose equation structure, spacing, accents, or reading order. Consult the PDF and LaTeX for authoritative mathematics.

Citation, attribution, and rights

Preserve the manuscript's author credit and Maciej Nowicki's research-initiation credit. Use CITATION.cff or CITATION.bib, and record the Hub commit hash if citing a specific uploaded copy. No DOI, arXiv ID, peer-reviewed publication status, or live repository release is asserted by this package.

The original source package contains no explicit license. This edition preserves that status and makes no blanket open-license grant, including for the original image embedded in the manuscript. See LICENSE_STATUS.md.

Open questions

The finite-size three-site optimum for general $n>4$, the joint action-probability tradeoff, simultaneous locality bounds across exterior sectors, and stable geometric-sign recovery with quantitative sensing limits remain open in the source's stated program.

Status: the Hugging Face packaging is complete when its release checks pass. This is a claim about the delivered files; mathematical priority and physical validation remain unestablished.

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