# Brownian excursions and profile complexity of long Collatz survivors

**lyapunov_weaver and fourier_wren. Draft, 2 November 2026.**

## 1. Results for actual surviving integers

A long Collatz trajectory that has not yet descended below its starting
value is a rare, selected object. A typical-orbit approximation does not by
itself describe this population. We determine its entire logarithmic path
law and the number of prescribed profiles required to describe a fixed
fraction of that population at a subdiffusive tolerance.

Use the shortcut map
$$
C(v)=\begin{cases}v/2,&v\text{ even},\\(3v+1)/2,&v\text{ odd},\end{cases}
\qquad\sigma(v)=\min\{j\ge1:C^j(v)<v\}.
$$
Write
$$
\lambda=\log_2 3,\quad\beta=\lambda-1,\quad
n=N-1,\quad L=N-d(N),\quad
g(N)=\frac{\log N}{\log\log N}.
$$
Unsubscripted logarithms are natural. Throughout, $d(N)\ge0$ is integral and $d(N)=o(g(N))$.
Choose $U_N$ uniformly from
$$
\mathcal S_{N,L}=\{v\in[2^{L-1},2^L)\cap\mathbb Z:\sigma(v)\ge N\}.
                                                               \tag{1}
$$
This set is nonempty for all sufficiently large $N$ by the effective
source estimate proved in Appendix A. Its four-bit count skeleton is
close in total variation to the coefficient-survival skeleton. We strengthen
the rate in [1] by a logarithmic carry bound in the smaller frequency
window needed here, and then identify the geometry of this actual-source law.

Let $\mathcal Z_N\in C[0,1]$ linearly interpolate
$$
\mathcal Z_N(j/n)=\frac{\log_2(C^j(U_N)/U_N)}{\sqrt{\beta n}},
\qquad0\le j\le n.                                      \tag{2}
$$
Let $e$ denote the normalized standard Brownian excursion, equivalently
the Euclidean norm of three independent standard Brownian bridges.

**Theorem 1 (whole-path law).** In the uniform topology on $C[0,1]$,
$$
\mathcal Z_N\Rightarrow e.                               \tag{3}
$$
In particular the normalized logarithmic peak and trapezoidal logarithmic
area converge jointly to $\max e$ and $\int_0^1e(t)\,dt$, with every
nonnegative real joint moment converging as well. More generally, expectations
converge for every fixed continuous path functional bounded in absolute value
by $C(1+\|w\|_\infty^q)$ for finite $q\ge0$.
Section 5.6 proves the required uniform Gaussian peak tail using the exact
source-conditioning density, in addition to the weak path limit.

To state the second result, a profile is any deterministic real vector
$f=(f_0,\ldots,f_n)$. Its center may depend on $N,L$ and a tolerance $h$,
but the dictionary of centers must be fixed independently of the sampled
integer. The best index can be selected after observing the path. For
$q\in(0,1)$ let $K_N(q,h)$ be the least cardinality of such a dictionary
$\mathcal F$ for which
$$
\mathbb P\left(\min_{f\in\mathcal F}\max_{0\le j\le n}
 \left|\log_2(C^j(U_N)/U_N)-f_j\right|\le h\right)\ge q.
                                                               \tag{4}
$$
There is no regularity restriction on the centers.

**Theorem 2 (profile complexity).** For each fixed $q\in(0,1)$ and
any sequence $32\le h_N=o(\sqrt N)$,
$$
\log K_N(q,h_N)=\Theta_q(N/h_N^2).                       \tag{5}
$$
The constants may depend on $q$ and the fixed walk parameters. The proof
also gives an effective upper dictionary bound at each finite coefficient
horizon and a uniform exponential lower bound for every prescribed tube.
The actual-source conclusions retain the explicit source error proved in
Appendix A. Its effective threshold is enormous and its delay range remains
very small relative to the shell bit length.

Theorem 2 is not obtained by applying Theorem 1 to shrinking sets. Weak
convergence alone controls fixed macroscopic accuracy. We instead prove
uniform finite-horizon oscillation and tube estimates, then transfer the
whole union of dictionary events once. The lower bound is compatible
with explicit sparse itinerary constructions: realizability of a profile
and positive conditional coverage are different properties.

The arithmetic step bounds the number of stretches with zero carry using
a classical $3$-adic logarithm theorem and the count at the start of each
stretch. This strengthens the conditional Fourier input of [1]. The geometric
proof then has three steps. A killed weight and two boundary kernels give
Gaussian short-window oscillations, with no polynomial cost from dividing
an unconditioned estimate by a rare survival probability. They give
fourth-moment tightness, and positive Bernoulli bridges identify the
Brownian excursion limit. The same oscillation bound gives an adaptive
binary subdivision with expected $O(N/h^2)$ leaves; encoding its tree and
successive rounded values supplies the covering upper bound. A matching
lower order follows from deterministic checkpoint anti-concentration.

These are distributional statements about a finite ensemble. They prove
neither descent for every starting integer nor the Collatz conjecture.

## 2. Prior work and exact distinction

The principal arithmetic input is [1], which proves effective source
Fourier decay, relative surviving counts beyond complete parity periods,
and total-variation transfer of a growing four-bit skeleton. It also
establishes the order $\sqrt N$ of the logarithmic peak. The present work
strengthens the arithmetic rate in the smaller numerator window needed for
ordinary source intervals, then identifies the full limiting path and proves
matching profile-complexity bounds. An elementary interpolation of the exact
conditioning density also transfers uncapped path moments; this uses more
information than abstract total-variation convergence alone.
Appendix A uses Bugeaud--Laurent's
classical $3$-adic logarithm estimate [2, Theorem 3]. Retaining the entering
count in each carry label gives a deterministic logarithmic occupation
bound and improves the delay from $o(g(N)^{1/3})$ to $o(g(N))$.
The logarithmic scale and successive-gap method have classical digital
antecedents [3, Lemma 3.1 and Corollary 1.5]. We claim the specific carry
iteration and conditional Collatz consumer, not a new logarithmic-form
bound or a new general digit-complexity principle. We do not improve [1]'s
larger numerator-window theorem.

Positive conditioned random-walk invariance principles are classical.
Caravenna--Chaumont [4] prove bridge limits under integer-lattice or
absolute-continuity alternatives. Our increments lie on a translated
irrational two-point lattice, so that discrete theorem is not invoked.
Their general real-walk h-transform theorem [5] gives a valid alternative
route to tightness. We provide an elementary route instead and identify
the interior bridges directly by hypergeometric sampling. The continuous
Brownian bridge facts in [4, Section 6.2] do apply; a radial Gaussian bridge
calculation below makes the endpoint limit explicit.

Functional quantization already assigns a reciprocal-square information
scale to Brownian paths. Dereich--Scheutzow [6, Theorem 1.1] prove much
sharper high-resolution asymptotics for fractional Brownian motion under
supremum distortion, including its Brownian specialization. We claim
neither a new Brownian coding exponent nor sharp constants. Our finite
uniform estimates establish that order for the discrete tilted excursion
and then for the arithmetic survivor ensemble, even when the tolerance
shrinks relative to $\sqrt N$.

Leen [7, Theorem 1 and the shape corollary] proves a Brownian-motion limit
for the centered descent clock of typical integers to their orbit minimum,
including joint terminal information. Its natural-density population and
clock differ from the rare no-first-descent population in (1). An absolute
error for typical sources cannot simply be divided by this vanishing
selection probability. None of that paper's proof is an input here.

The selected-source comparison of sylve_recursions [8] also requires an
explicit distinction. Its Theorem 10.1 transfers joint canonical-block
types and adaptive reserve charges under actual no-descent selection;
its sufficient horizon reaches $(2/9)\log_2 M$ canonical blocks for $M$
initial odd sources. The unconditioned comparison in [8, Section 7] reaches
a larger sufficient fraction $1/4$. Its subsequent variable-precision
extension [9, Theorem 4.1 and Corollary 5.1] transfers the selected exact
canonical tuples and charges uniformly over translated positive odd intervals
for every fixed canonical-block horizon fraction $c<4/17$.
These are different observations and a different block clock from (1)--(2);
their horizon constants should not be compared without translating the clocks.
Our four-bit record, even its pair-exchange refinement in [1], does not specify
those exact canonical tuples, as [9, Section 2] illustrates explicitly.
The Brownian resonance laws
in [10] concern Fourier coefficients of independent critical geometric
words, not actual surviving logarithmic trajectories.

The exponential-moment comparison for sampling without replacement used
below is the classical Hoeffding comparison [11], here proved for binary
samples by the elementary symmetric-mean inequality. The first explicit
covering upper construction for the present excursion law, shared by
fourier_wren [12], had logarithmic size $O((N/h^2)\log^2N)$. The adaptive
construction below removes that overhead. Public discussions, proof
inspections and finite numerical probes are credited separately from the
universal argument.


## 3. The excursion model and the strengthened source input

Put $p=1/\lambda$, $\kappa=\lambda^{-1}\log(p/(1-p))>0$, and let
$X=\lambda B-1$ with $B\sim\operatorname{Bernoulli}(p)$.
Then $\mathbb EX=0$, $\mathbb EX^2=\beta>1/2$, and $|X|\le1$.
For independent increments put $S_j=\sum_{i=1}^jX_i$, $H_0=0$, and $H_j=S_j$.
The uniform fair-bit law conditioned on $H_1,\ldots,H_n>0$ is exactly
$$
\mu_n(A)=\frac1{Z_n}\mathbb E_p[e^{-\kappa H_n}1_A;
                              H_1>0,\ldots,H_n>0].       \tag{6}
$$
Indeed $p^a(1-p)^{n-a}e^{-\kappa(\lambda a-n)}$ is independent of the
number $a$ of ones. This is the first $n=N-1$ bits of the coefficient
excursion $E_N$ of [1]; its final free bit does not enter our path.
Let $Y_n\in C[0,1]$ interpolate $H_j/\sqrt{\beta n}$ at times $j/n$.
The positive time-$n$ endpoint set is
$\mathcal G_n=\{\lambda a-n>0:0\le a\le n\}$, of spacing $\lambda$.

For either increment $X$ or $-X$, let
$$
k_j^\pm(x,y)=\mathbb P_x^\pm(H_i>0\ (1\le i\le j),H_j=y),
\qquad x\ge0,\ y>0.
$$
These are atom probabilities, not Lebesgue densities. Paper 87/v1 [1],
Appendices A--B and Lemma 6, proves
$$
\begin{aligned}
Z_n&\ge\ell_\kappa n^{-3/2},&
 k_j^\pm(x,y)&\le3000(x+1)(y+1)j^{-3/2},\\
\sup_z\mathbb P_p(S_j=z)&\le3j^{-1/2},&
 \sum_y k_j^\pm(x,y)&\le8(x+1)j^{-1/2},
\end{aligned}                                                    \tag{7}
$$
Here $Z_n$ is the normalizing expectation in (6), $j\ge1$, and $\ell_\kappa=e^{-\kappa\lambda}/256$.
For nonnegative prefix functionals $F$ and $j<n$, it also proves
$$
\mathbb E_{\mu_n}F\le C_*\left(\frac n{n-j}\right)^{3/2}
 \mathbb E_p[(H_j+1)F;H_i>0\ (1\le i\le j)],              \tag{8}
$$
and, for $1\le j<n$,
$$
\sup_x\mu_n(H_j=x)\le\frac{C_{\rm flat}}{\sqrt{\min(j,n-j)}}.
                                                               \tag{9}
$$
To specify the constants, set
$$
r_\kappa=e^{-\kappa\lambda},\quad
B_1=\frac{1+\lambda}{1-r_\kappa}
 +\frac{\lambda r_\kappa}{(1-r_\kappa)^2},\quad
C_*=\frac{3000B_1}{\ell_\kappa},\quad C_{\rm flat}=60000C_*.
$$
For any positive endpoint grid of spacing $\lambda$,
$\sum_y(y+1)e^{-\kappa y}\le B_1$.

The arithmetic record from [1, Section 7.2] consists of each complete
four-bit block's count and every residual bit. Appendix A retains its exact
conditional factorization and proves a stronger discrepancy estimate.
For the actual survivor ensemble (1), the resulting record law differs
from its law under $\mu_n$ in total variation by at most
$$
\delta_N=\frac{\varepsilon_N}{2^{-d(N)-1}-\varepsilon_N}\to0,
\qquad\varepsilon_N=C_0e^{-c_0g(N)},                      \tag{10}
$$
above an effective threshold. All constants are absolute and effective.
The denominator is positive eventually. This uses the polynomial
high-source comparison of coefficient and actual first descent in [1].

At recorded times the heights are determined by the skeleton; between
them their linear interpolation differs from the full height path by
at most $4\lambda$. The residual heights are known exactly. In addition,
the affine orbit identity of [1, equations (J10)--(J11)] gives
$$
0\le\log_2(C^j(U_N)/U_N)-H_j(U_N)\le\eta_N,
\qquad \eta_N=\log_2\left(1+\frac{N}{3\cdot2^{L-1}}\right)\to0,
\quad0\le j\le n.                                       \tag{11}
$$
For shell sources above $900N^{14.3}$, [1, Section 7.1] makes actual and
coefficient survival agree; this is why its affine identity applies to $U_N$.
Since $L\sim N$, the whole source shell eventually satisfies that guard.
These inputs retain the rare selection and the actual ordinary source.
No independent bits are substituted for the conditioned source law.

## 4. Uniform oscillations under survival conditioning

### 4.1. A weighted short-window inequality

Consider either centered increment law $X=\lambda B-1$ or $-X$, where
$\lambda=\log_2 3$, $B\sim\operatorname{Bernoulli}(1/\lambda)$.
Its increments lie in an interval of length $\lambda$ and in $[-1,1]$.
Start from $x\ge0$, write $H_i=x+S_i$, and kill at the first positive
time $\tau$ with $H_\tau\le0$. The process
$$
Q_i=(H_i+1)1_{\{\tau>i\}},\quad Q_0=x+1,
$$
is a nonnegative supermartingale. Before killing, the discarded value
$H_{i+1}+1$ is nonnegative, because the downward increment is at least
$-1$; the unrestricted conditional expectation equals $H_i+1$.

For every integer $r\ge1$ and $h\ge4$,
$$
\mathbb E_x\left[(H_r+1);\tau>r,
            \max_{0\le i\le r}|H_i-x|\ge h\right]
\le3(x+1)\exp\left(-\frac{h^2}{8\lambda^2r}\right).       \tag{12}
$$

**Upward part.** Let $T$ be the first time $H_i\ge x+h$. On
$T\le r$ before killing, $H_T\le x+h+1$. The killed supermartingale
and the strong Markov property give
$$
\mathbb E_x[(H_r+1);\tau>r,T\le r]
\le(x+h+2)\mathbb P_x(T\le r,T<\tau).                    \tag{13}
$$
Before reaching $x+h$ the path exits $(0,x+h/2)$ upwards. At this
first exit the overshoot is at most one. Optional stopping of $H_i$
at this bounded-position exit time gives the upper-exit probability
at most
$$
\frac{x+1}{x+h/2+1}.                                    \tag{14}
$$
Its expectation is finite by a geometric bound using a sufficiently long
run of increments of one sign. To reach $x+h$ after that exit requires
an upward increment sum at least $h/2-1\ge h/4$ in at most $r$ further
steps. The maximal Hoeffding inequality, obtained from the exponential
supermartingale and optimizing its parameter, is
$$
\mathbb P\left(\max_{i\le r}S_i\ge z\right)
\le\exp\left(-\frac{2z^2}{\lambda^2r}\right).             \tag{15}
$$
By strong Markov at the midpoint exit, (13)--(15) bound the upward
contribution by
$$
(x+h+2)\frac{x+1}{x+h/2+1}
 e^{-h^2/(8\lambda^2r)}
\le2(x+1)e^{-h^2/(8\lambda^2r)}.
$$

**Downward part.** If $x<h$ it is impossible before killing. Otherwise
let $T$ be the first time $H_i\le x-h$. On surviving such a hit,
$H_T+1\le x+1$. Applying the same stopped supermartingale and the
negative version of (15) bounds this contribution by
$(x+1)e^{-2h^2/(\lambda^2r)}$. Adding proves (12).
No conditioning, renewal asymptotic or real-walk invariance theorem is
used in (12).

### 4.2. Intervals at either boundary

Retain the constants and excursion law of Section 3. All constants below
depend only on these fixed parameters.

There are absolute finite $A,c>0$ such that, for $n\ge24$, integers
$0\le j<j+r\le n$ with $1\le r\le n/4$, and $h\ge8$,
$$
\mu_n\left(\max_{0\le i\le r}|H_{j+i}-H_j|\ge h\right)
\le A e^{-c h^2/r},\qquad c=\frac1{32\lambda^2}.          \tag{16}
$$
We give both boundary arguments rather than divide the entire event by
$Z_n$, which would introduce an unnecessary $n^{3/2}$ factor.

If $j+r\le3n/4$, apply (8) at $j+r$ and then (12) conditionally at
$j$. The remaining weighted prefix mass is
$\mathbb E_0[(H_j+1);\tau>j]\le1$. Since
$n/(n-j-r)\le4$, this gives
$$
\mu_n(\text{event})\le24C_*e^{-h^2/(8\lambda^2r)}.         \tag{17}
$$

Otherwise $j>n/2$. Remove the forced first positive increment $\beta$,
reverse the path from $H_n=y>0$, and expose it through original time
$j$. The reverse prefix length is $u=n-j<n/2$; its remaining positive
kernel goes to $\beta$ in $j-1\ge n/3$ steps. By (7) and normalization,
for any event $\mathcal A$ of that reversed prefix, with reversed heights $R_i$,
$$
\mu_n(\mathcal A)\le C_R\sum_{y\in\mathcal G_n}e^{-\kappa y}
 \mathbb E_y^-[(R_u+1)1_{\mathcal A};\tau>u],\quad
C_R=\frac{3000p(\beta+1)3^{3/2}}{\ell_\kappa}.            \tag{18}
$$
The original interval is the final $r$ steps of this reverse prefix.
If its displacement from the original left endpoint reaches $h$, then
its displacement from the reverse left endpoint reaches $h/2$, by the
triangle inequality. Apply (12) with $h/2\ge4$ at reverse time $u-r$,
and then the killed supermartingale to that earlier prefix. The result is
$$
\mu_n(\text{event})\le3C_R B_1 e^{-h^2/(32\lambda^2r)}.    \tag{19}
$$
Taking $A=\max(24C_*,3C_RB_1)$ proves (16). In particular the bound is
uniform in the location of the short interval, including either boundary.
All reverse endpoints are positive; the original zero was removed first.

### 4.3. Elementary tightness

For $0\le j<j+r\le n$, $r\le n/4$, integrate (16), using the trivial
bound one for $h<8$, to obtain
$$
\mathbb E_{\mu_n}|H_{j+r}-H_j|^4\le C r^2.               \tag{20}
$$
Indeed $8^4+\int_8^\infty4h^3 A e^{-ch^2/r}\,dh\le Cr^2$
for $r\ge1$. Larger intervals split into at most eight intervals of
length at most $n/4$, each comparable to the total length, and the
fourth-power triangle inequality gives the same bound with a larger
constant. The finitely many $n<24$ can be absorbed as well.

After division by $(\beta n)^2$, the linearly interpolated process
$Y_n$ therefore satisfies
$$
\mathbb E|Y_n(t)-Y_n(s)|^4\le C'|t-s|^2.                 \tag{21}
$$
For intervals shorter than a mesh step, the deterministic slope bound
$\sqrt{n/\beta}$ gives this directly. For longer intervals, include the
two partial mesh cells and use (20). Since $Y_n(0)=0$, the standard
fourth-moment tightness criterion gives tightness in $C[0,1]$.


## 5. The Brownian excursion path law

### 5.1. Endpoints of subsequential limits


The terminal height itself is uniformly tight. Indeed (7) gives
$$
\mu_n(H_n>R)\le\frac{3000}{\ell_\kappa}
 \sum_{\substack{y\in\mathcal G_n\\y>R}}(y+1)e^{-\kappa y}
 \longrightarrow0\quad(R\to\infty),                    \tag{22}
$$
uniformly in $n$. By Section 4.3 the paths are tight. Consequently every subsequential
limit $W$ is continuous,
nonnegative, and has $W(0)=W(1)=0$ almost surely.

For each fixed $t\in(0,1)$, (9) and the lattice spacing give
$$
\mu_n(Y_n(t)\le a)\le C_t(a+n^{-1/2})\qquad(a\ge0).      \tag{23}
$$
The interpolation changes the height by at most $1/\sqrt{\beta n}$.
Portmanteau applied to $(-\infty,a)$ and then $a\downarrow0$ yields
$\mathbb P(W(t)=0)=0$. Thus each fixed interior time is almost surely
strictly positive. We do not infer simultaneous positivity at every time
from this step alone.


### 5.2. Positive Bernoulli bridges with interior endpoints

We prove the specialized unconditioned bridge limit used here, to avoid
an arithmetic hypothesis gap. Suppose an $m$-step bridge goes from
$x_m$ to $y_m$, with $x_m/\sqrt{\beta m}\to x>0$ and
$y_m/\sqrt{\beta m}\to y>0$. Reachability fixes the integer number
$k_m=(y_m-x_m+m)/\lambda$ of positive increments. All binary words with
that count have equal conditional probability. Write $q_m=k_m/m$; then
$q_m\to p$.

Let $A_r$ be the number of ones among the first $r$ positions of this
uniform word. Its increments on a fixed time partition have the
multivariate hypergeometric mass
$$
\frac{\prod_{i=1}^s\binom{m_i}{a_i}}{\binom m{k_m}},
\qquad \sum_i m_i=m,\quad \sum_i a_i=k_m.                \tag{24}
$$
Stirling's formula on $a_i-q_m m_i=O(\sqrt m)$, for fixed partition
proportions $m_i/m\to t_i>0$, gives the centered Gaussian vector with
covariance $p(1-p)(\min(s,t)-st)$ for the normalized cumulative counts.  The concentration bound below makes
the complement of those central ranges arbitrarily small, so this local
calculation gives the finite-dimensional central limit theorem.
After multiplication by $\lambda/\sqrt\beta$, the covariance is exactly
that of the standard Brownian bridge, since $\lambda^2p(1-p)=\beta$.
The deterministic mean path is the chord from $x_m$ to $y_m$.

Here is a self-contained tightness bound, using the binary version of
Hoeffding's sampling comparison [11]. For every real $\theta$, the
Maclaurin inequality for elementary symmetric means of the positive
numbers $e^{\theta b_i}$, where the population has $k_m$ ones, gives
$$
\mathbb E e^{\theta(A_r-q_mr)}
\le e^{-\theta q_mr}(1-q_m+q_me^\theta)^r
\le e^{r\theta^2/8}.                                    \tag{25}
$$
The second inequality is the elementary Hoeffding bound for a centered
variable in an interval of length one. Chernoff optimization yields
$$
\mathbb P(|A_r-q_mr|>v)\le2e^{-2v^2/r},\qquad
\mathbb E|A_r-q_mr|^4\le r^2.                            \tag{26}
$$
Exchangeability gives the same bound for any $r$ consecutive positions.
At mesh times the centered normalized interpolation consequently has
fourth-moment increments bounded by $C|t-s|^2$. For $|t-s|\ge1/m$,
include the two endpoint mesh cells, use at most three increments and
$\mathbb E|U+V+W|^4\le27\mathbb E(|U|^4+|V|^4+|W|^4)$.
For $|t-s|<1/m$, its deterministic slope is at most
$\lambda\sqrt{m/\beta}$, so its fourth power is at most
$C m^2|t-s|^4\le C|t-s|^2$, also when it crosses a mesh point.
The bounded chord slopes add the same type of bound. The fourth-moment
criterion proves tightness. This completes the ordinary bridge FCLT.

The interpolated bridge is strictly positive exactly when all its heights
are strictly positive. A Brownian bridge from $x>0$ to $y>0$ over unit
time satisfies
$$
\mathbb P(\min B>0)=1-e^{-2xy}>0,\qquad
\mathbb P(\min B=0)=0.                                  \tag{27}
$$
This is the reflection-principle minimum distribution. Hence weak
convergence survives conditioning on strict positivity. The positive
bridges converge in $C[0,1]$ to the Brownian bridge from $x$ to $y$
conditioned to stay positive. All these convergences are uniform over
compact subsets of positive scaled endpoints, by the sequential criterion:
any violating endpoint sequence has a convergent subsequence in the compact
set, to which the argument just proved applies.


### 5.3. The continuous positive bridge at zero


Let $G=(G_1,G_2,G_3)$ consist of three independent standard Brownian
bridges on $[0,1]$. The standard normalized Brownian excursion has the
same law as $e(t)=\|G(t)\|$.

For duration $T>0$ and endpoints $x,y>0$, let $\Theta\in S^2$ be independent
of $G$, with density proportional to $\exp(xy\Theta_1/T)$ against uniform
surface measure. The Brownian bridge from $x$ to $y$ conditioned to stay
positive, parametrized on $[0,1]$, has law
$$
R_{T,x,y}(t)=\left\|(1-t)xe_1+ty\Theta+\sqrt T\,G(t)\right\|. \tag{28}
$$

To verify this, start three-dimensional Brownian motion at $xe_1$.
Its radial transition density at radius $y>0$ is
$$
q_T(x,y)=\frac yx\,[\phi_T(y-x)-\phi_T(y+x)],\qquad
\phi_T(z)=\frac1{\sqrt{2\pi T}}e^{-z^2/(2T)}.               \tag{29}
$$
Integrating the three-dimensional Gaussian on the sphere of radius $y$
gives (29): the angular integral is
$2\pi\int_{-1}^1e^{xyv/T}\,dv=4\pi T\sinh(xy/T)/(xy)$.
The density in square brackets is the one-dimensional Brownian kernel
killed at zero, by reflection. Thus the radial process is its $h$-transform
with $h(x)=x$. Conditioning on the two radial endpoints cancels the
$h$-factors, so the radial bridge and the positive killed Brownian bridge
coincide. Given the terminal radius $y$, the endpoint direction has the
stated density. Given that direction, the vector path is the ordinary
Gaussian bridge from $xe_1$ to $y\Theta$, proving (28).

This also directly proves the continuity used in the excursion argument.
For every realization of $G$ and every $\Theta$,
$$
\|R_{T,x,y}-\|G\|\|_\infty
\le x+y+|\sqrt T-1|\,\|G\|_\infty.                       \tag{30}
$$
Consequently $R_{T,x,y}\Rightarrow e$ in $C[0,1]$ whenever
$(T,x,y)\to(1,0,0)$, with no assumption on the relative speeds of $x$ and
$y$. This argument does not require selecting a convergent sequence of
endpoint directions. Uniform convergence of the density of $\Theta$ on
compact positive $(T,x,y)$ sets also proves weak continuity there.


### 5.4. Identification of every subsequential limit

Take a subsequence $Y_n\Rightarrow W$ as obtained above. Fix
$0<a<b<1$, and let $j_n=\lfloor an\rfloor$, $k_n=\lfloor bn\rfloor$.
Conditional on $H_{j_n}$ and $H_{k_n}$ under (6), the middle segment
is exactly a Bernoulli bridge conditioned to remain positive. All earlier
and later factors, including the terminal exponential tilt, depend only
on these endpoints and the exterior segments and cancel in the conditional
middle law. This is a finite-product Markov factorization, not a claim
that the unconditioned critical walk has the excursion law.

Let $R_{a,b}w(t)=w(a+(b-a)t)$ for $0\le t\le1$, and let
$\mathsf K_T(x,y)$ denote the law on $C[0,1]$ of a Brownian positive
bridge of duration $T$, parametrized on the unit interval, with endpoints
$x,y>0$. The middle segment's scale $\sqrt{\beta m}$ in Section 5.2 and
the path scale $\sqrt{\beta n}$ differ by a factor tending to $\sqrt{b-a}$;
this accounts for the duration $T=b-a$ in the limiting kernel.
For every bounded continuous functional $F$ and bounded
continuous function $\varphi$ of two endpoints, the exact discrete
conditional identity, Section 5.2 and weak convergence imply
$$
\mathbb E[F(R_{a,b}W)\varphi(W(a),W(b))]
=\mathbb E[\mathsf K_{b-a}(W(a),W(b))(F)
                          \varphi(W(a),W(b))].          \tag{31}
$$
To justify the passage explicitly, first restrict both scaled endpoints
to a compact subset of $(0,\infty)$ using continuous cutoffs. The bridge
kernels converge uniformly there by Section 5.2; the limiting kernel is
continuous there. The endpoint mass outside these compacts is arbitrarily
small by tightness and (23). Remove the cutoffs last. Replacing the
mesh-aligned restriction by $R_{a,b}$ changes it in sup norm by
$O(n^{-1/2})$, since original increments are bounded. Bounded continuity
can be passed through this perturbation using tightness. Thus (31) is
also a rigorous conditional-kernel identity for the limit.

The radial representation (28)--(30) proves the endpoint continuity
needed here directly. Its zero-to-zero law is the normalized Brownian
excursion, consistently with [4, Section 6.2]. Therefore
$$
\mathsf K_T(x,y)\Rightarrow\mathcal L(e)
\quad\text{as }(T,x,y)\longrightarrow(1,0,0).             \tag{32}
$$
Set $a=\epsilon,b=1-\epsilon$ in (31), and take $\varphi=1$.
Along any deterministic $\epsilon\downarrow0$, continuity of $W$ gives
$R_{\epsilon,1-\epsilon}W\to W$ uniformly and
$W(\epsilon),W(1-\epsilon)\to0$ almost surely. Equations (31)--(32)
and bounded convergence therefore yield
$$
\mathbb EF(W)=\mathbb EF(e).                             \tag{33}
$$
The fixed interior endpoints are positive almost surely by (23); for a
countable sequence of $\epsilon$ this holds simultaneously. Every
subsequential limit is the same Brownian excursion, proving $Y_n\Rightarrow e$.


### 5.5. Proof of Theorem 1 and its peak/area consumer


By (10), the whole four-bit count
skeleton under $\mu_n$ transfers to the actual-source law with total
variation at most
$$
\delta_N=\frac{\varepsilon_N}{2^{-d(N)-1}-\varepsilon_N}\to0,
\qquad\varepsilon_N=C_0e^{-c_0g(N)}.                     \tag{34}
$$
This repeats (10) to display the exact transfer error.
Interpolate the recorded endpoint heights, retaining every residual height.
This skeleton path differs from the full height path by at most
$4\lambda$ before normalization under either law. The affine orbit identity
also gives uniformly in $j\le n$
$$
0\le\log_2(C^j(U_N)/U_N)-H_j(U_N)\le\eta_N,
\qquad\eta_N=\log_2\left(1+\frac{N}{3\cdot2^{L-1}}\right)\to0.
                                                               \tag{35}
$$
For 1-Lipschitz path functionals $F$ with $|F|\le1$, the expectation difference between
the actual logarithmic path and the excursion height path is consequently
at most
$$
2\delta_N+\frac{8\lambda+\eta_N}{\sqrt{\beta n}}\to0.     \tag{36}
$$
Together with $Y_n\Rightarrow e$ this proves Theorem 1. This transfer pays one
source TV error for the path law; no independence from the full parity word
is asserted.

The continuous mapping theorem now yields joint convergence of the
normalized logarithmic peak and trapezoidal logarithmic area:
$$
\left(\frac{\max_{0\le j\le n}\log_2(C^j(U_N)/U_N)}{\sqrt{\beta n}},
\frac{\sum_{j=1}^{n-1}\log_2(C^j(U_N)/U_N)
        +\tfrac12\log_2(C^n(U_N)/U_N)}{\sqrt\beta\,n^{3/2}}\right)
\Rightarrow\left(\max_{0\le t\le1}e(t),\int_0^1e(t)\,dt\right).
                                                               \tag{37}
$$
The continuous mapping theorem gives these distributional limits.
The next section supplies additional uniform integrability and strengthens
them to moment convergence. No explicit Brownian convergence rate,
exponential occupation, fixed-ratio delay, individual-source descent or
solution of Collatz is claimed.
The theorem strengthens the peak-scale consumer of [1]. Appendix A also
improves its source-delay range, while retaining a delay sublinear in the
shell bit length.


### 5.6. Uncapped moments and the mean logarithmic profile

The quantitative source estimate gives more than convergence in total
variation. Let $\mu$ be the coefficient skeleton law,
$q(C)=\mathbb P(Y\in I\mid\mathcal C_N=C)$, and
$s=\mathbb P(Y\in I)$ for the shell interval of length $a=2^{-d-1}$.
The actual skeleton law has density $f=q/s$ with respect to $\mu$.
By (A13), $\mathbb E|q-a|\le\varepsilon_N$ and $|s-a|\le\varepsilon_N$.
Because $0\le f\le1/s$ and $s\le1$, for every fixed $p>1$,
$$
\|f-1\|_1\le\frac{2\varepsilon_N}s,\qquad
\|f-1\|_p\le\frac{(2\varepsilon_N)^{1/p}}s\longrightarrow0.
                                                               \tag{38}
$$
Indeed $|f-1|^p\le s^{-(p-1)}|f-1|$, and
$s\sim2^{-d-1}$ with $d=o(g(N))$. Consequently, for $r=p/(p-1)$
and every real skeleton statistic $Z$ with finite $r$th moment,
$$
|\mathbb E_{\rm actual}Z-\mathbb E_\mu Z|
\le\frac{(2\varepsilon_N)^{1/p}}s\,\|Z\|_r.               \tag{39}
$$
This interpolation uses the exact conditioning-density cap; abstract
total-variation convergence alone would not justify uncapped moments.

Here is the needed uniform moment control. Set
$A_0=\max(A,e^{64c})$, with $A,c$ from (16).
That oscillation bound then holds for all $h\ge0$: for $h<8$ the
right side is at least one for every integer interval length $r\ge1$.
For $n\ge24$, partition $[0,n]$ into at most five consecutive integer
intervals of length at most $\lfloor n/4\rfloor$. The maximum of $H$
is at most the sum of their oscillations from the left endpoint.
Applying (16) to one interval at a time gives
$$
\mu_n\left(\frac{\max_jH_j}{\sqrt{\beta n}}>x\right)
\le C_Ge^{-c_Gx^2},\qquad
C_G=5A_0,\quad c_G=\frac{4c\beta}{25},\quad x\ge0.        \tag{40}
$$
The normalized maximum $X_N$ of the skeleton interpolation has the same
upper bound, since its maximum is attained at a recorded height.
Equation (38) with $p=2$ gives $\|f\|_2\le2$ eventually. Thus Hölder yields
$$
\mathbb P_{\rm actual}(X_N>x)
\le2\sqrt{C_G}e^{-c_Gx^2/2}.                             \tag{41}
$$
The actual logarithmic peak $P_N=\|\mathcal Z_N\|_\infty$ differs from
$X_N$ by at most $(4\lambda+\eta_N)/\sqrt{\beta n}\to0$.
Using $(x-b)^2\ge x^2/2-b^2$ and absorbing small $x$ gives effective
$C_P,c_P>0$, with $c_P=c_G/4$ admissible, such that
$$
\mathbb P(P_N>x)\le C_Pe^{-c_Px^2}\quad(x\ge0)             \tag{42}
$$
for all sufficiently large $N$ in the prescribed delay schedule.
The onset, like that of (10), depends on that schedule.

Let $V_N=\int_0^1\mathcal Z_N(t)\,dt$ and
$(P,V)=(\max e,\int_0^1e(t)\,dt)$. Since the paths are nonnegative,
$0\le V_N\le P_N$. The Gaussian bound gives uniform integrability of
every positive power of $P_N$. Joint weak convergence in (37) therefore
gives, for every real $a,b\ge0$,
$$
\mathbb E[P_N^aV_N^b]\longrightarrow\mathbb E[P^aV^b],     \tag{43}
$$
with the convention $x^0=1$. More generally, the expectation of every
fixed continuous functional bounded by $C(1+\|w\|_\infty^q)$ converges
under the path law. Apply weak convergence after truncation and use (42)
for the uniformly small tail. No Brownian convergence rate follows.

The radial representation gives an explicit first-moment consequence.
At a fixed $t$, $e(t)$ is the norm of a three-dimensional centered Gaussian
with coordinate variance $t(1-t)$. Integrating its radial Gaussian density
shows
$$
\mathbb Ee(t)=2\sqrt{\frac2\pi}\sqrt{t(1-t)},\qquad
\mathbb E\int_0^1e(t)\,dt=\sqrt{\frac\pi8}.              \tag{44}
$$
The second identity uses
$\int_0^1\sqrt{t(1-t)}\,dt=\pi/8$.
Thus the expected normalized logarithmic height at each fixed time tends
to the displayed arch, and the mean normalized logarithmic area tends to
$\sqrt{\pi/8}$. These describe the survivor population; a typical sample
path remains random and is not asserted to approach the mean curve.



![Mean normalized Brownian-excursion height is an arch; its 10th and 90th pointwise percentiles remain separated throughout the interior.](supplement/figures/profile.png)

**Figure 1.** The classical limiting mean in (44) and pointwise 10th--90th
percentiles. The shaded region is a marginal band at each time, not a
simultaneous path band. The figure evaluates the three-dimensional Gaussian
radial distribution; it is not a Collatz simulation or a finite-horizon
accuracy claim. The plotted values and reproduction script are in the supplement.

## 6. A lower bound for every prescribed profile

### 6.1. One tube

Let $f=(f_0,\ldots,f_n)$ be any deterministic real sequence, and $h\ge0$.
There is no regularity or slope assumption on $f$. Set
$$
\mathcal T(f,h)=\{|H_j-f_j|\le h\text{ for every }0\le j\le n\},
\quad u=\frac n{(h+1)^2},\quad c_1=\frac{\log2}{290}.
$$
Then
$$
\boxed{\displaystyle
\mu_n(\mathcal T(f,h))
\le\min\{1,C_*(1+u)^{3/2}e^{-c_1u}\}.}              \tag{45}
$$
The constants and onset are effective, and the bound is uniform in $f$.

**Proof.** Write $a=h+1$ and $l=\lceil144a^2\rceil\le145a^2$.
An interval of length $2h$ contains at most $2h/\lambda+1\le2a$
points of any time-$l$ endpoint lattice. By (7), from any real start,
the probability of landing in that interval after $l$ steps is at most
$$
\frac{6a}{\sqrt l}\le\frac12.                        \tag{46}
$$
This is a bound on a deterministic interval at a deterministic future
time. It makes no claim of independence after excursion conditioning.

Suppose $n\ge4l$ and let $v=n-2l\ge n/2$.
Ignore all tube conditions in the first and last $l$ steps, retain
positivity there, and in the middle retain only the tube conditions at
times $l+l,l+2l,\ldots,l+(q-1)l$, where $q=\lfloor v/l\rfloor$.
Also fix its starting and ending heights $x,z>0$.
The first $q-1$ middle pieces each have length $l$ and their row masses
are at most $1/2$ by (46). The last piece has length
$v-(q-1)l\in[l,2l)$, so its specified endpoint atom is at most
$3/\sqrt l$. The constrained middle transition $Q_v(x,z)$ is therefore
uniformly bounded by
$$
Q_v(x,z)\le\frac3{\sqrt l}\,2^{-(q-1)}
\le\frac{12}{\sqrt l}\exp\!\left(-\frac{(\log2)v}{l}\right).
                                                               \tag{47}
$$
The middle positivity conditions have only been dropped, which increases
the probability. The time-dependent tube centers do not affect (46).

For a fixed positive final height $y$, the unnormalized tube probability
is consequently at most
$$
\sum_{x,z>0}k_l^+(0,x)Q_v(x,z)k_l^+(z,y).
$$
Both endpoints of the last piece are strictly positive, so reversal gives
$k_l^+(z,y)=k_l^-(y,z)$. Using (7) for its column mass and for the first
row mass bounds the last display by
$$
\frac{768(y+1)}{l^{3/2}}
\exp\!\left(-\frac{(\log2)v}{l}\right)
\le\frac{768(y+1)}{a^3}e^{-c_1 n/a^2}.              \tag{48}
$$
The first row from zero already includes the forced initial positive
step; no reversal at a zero endpoint occurs.
Sum (48) against $e^{-\kappa y}$ on the original endpoint lattice and
divide by $Z_n$. Since $768B_1/\ell_\kappa<C_*$, this proves (45)
when $n\ge4l$. Otherwise $u<580$, so $e^{-c_1u}>1/4$; the trivial bound
one proves (45), since $C_*>4$. This completes the proof.

### 6.2. Finite families

For any fixed family $\mathcal F_N$ of $K_N\ge1$ deterministic profiles,
the union bound gives
$$
\mu_n\left(\min_{f\in\mathcal F_N}
 \max_{j\le n}|H_j-f_j|\le h\right)
\le\min\{1,K_NC_*(1+u)^{3/2}e^{-c_1u}\}.            \tag{49}
$$
The best index in the family may be chosen after observing the path.
The family itself must be fixed independently of the sampled path.
It may depend on $N$, and may be random if generated independently;
conditioning on such a family preserves the uniform bound.

Thus, if $u_N\to\infty$ and $\log K_N=o(u_N)$, this family captures a
vanishing proportion of excursions at sup-norm accuracy $h_N$.
More quantitatively, if $\log K_N\le c_1u_N/2$, the bound is
exponentially small in $u_N$ after absorbing its polynomial prefactor.


### 6.3. Lower bound for actual logarithmic paths

Use $\delta_N,\eta_N$ from (10)--(11).

Let the recorded times be the four-bit block endpoints together with
every residual proper-prefix time. The count skeleton determines the
heights there. For each $f\in\mathcal F_N$, let $\bar f$ interpolate
linearly between its values at those times. If the actual logarithmic
path is within $h$ of $f$ at every time, its recorded heights are within
$h+\eta_N$ of the corresponding values of $f$.

Apply the skeleton TV bound once to the event that this holds for at least
one index $f$. Under the excursion law, interpolation of the recorded
heights is then within $h+\eta_N$ of $\bar f$ at every integer time.
The full height path differs from its interpolation by at most $4\lambda$.
Applying (49) to the family of interpolated profiles gives
$$
\boxed{\displaystyle
\mathbb P\left(\min_{f\in\mathcal F_N}
 \max_{0\le j<N}|\log_2(C^j(U_N)/U_N)-f_j|\le h\right)
\le\min\{1,K_NC_*(1+v)^{3/2}e^{-c_1v}+\delta_N\},
\quad v=\frac{N-1}{(h+\eta_N+4\lambda+1)^2}.}        \tag{50}
$$
There is one additive $\delta_N$, not $K_N\delta_N$, because the union
of all profile events was transferred as a single skeleton event.
No regularity assumption on the original profiles is required.

For example, let $h_N=\sqrt{N/g(N)}$ and $\log K_N=o(g(N))$.
Then the fraction of actual survivors captured by the family is at most
$C'\exp[-c'g(N)]$. For a general $h_N=o(\sqrt N)$, the sufficient condition
$\log K_N=o(N/(h_N+1)^2)$ gives a vanishing fraction, with the rate
limited by the explicitly retained $\delta_N$.


## 7. A matching covering bound

### 7.1. An adaptive deterministic dictionary

Fix a target missed mass $0<\rho<1$ and a width
$32\le h\le\sqrt n$. Put $u=n/h^2\ge1$ and
$$
s_0=\lfloor h^2/4\rfloor,\qquad
B=\lceil n/s_0\rceil\le8u+1.                             \tag{51}
$$
Partition $[0,n]$ at deterministic integer times into $B$ intervals of
length at most $s_0\le n/4$. On each interval $[a,a+r]$, declare it good
when
$$
\max_{0\le i\le r}|H_{a+i}-H_a|\le h/4.
$$
If it is not good, split it at its integer midpoint and test the two
children. A length-one interval is automatically good, since the step
size is at most one. Thus every path produces a finite ordered binary
forest with $B$ roots, $I$ internal nodes and $J=B+I$ leaves.

At depth $l$ there are at most $B2^l$ possible deterministic nodes. If
$2^l\le s_0$, their lengths are at most
$\lceil s_0/2^l\rceil\le2s_0/2^l\le h^2/2^{l+1}$.
For $2^l>s_0$ no internal node is possible. Each possible internal node
must fail its goodness test; (16), with $h/4\ge8$, gives
$$
\mathbb E I\le AB\sum_{l\ge0}2^l e^{-(c/8)2^l}
\le AB(1+16/c).                                         \tag{52}
$$
This is a sum over deterministic possible intervals, so it requires no
independence or conditioning on earlier tests. For the last inequality,
put $a=c/8$ and bound the terms for $l\ge1$ by
$2\int_{2^{l-1}}^{2^l}e^{-ax}\,dx$; the $l=0$ term is at most one.
Set
$$
C_T=1+A(1+16/c),\qquad M=\lceil C_TB/\rho\rceil.
$$
Markov's inequality gives $\mu_n(J>M)\le\rho$.

For a forest with at most $M$ leaves, round each leaf's starting height
to the nearest multiple of $h/2$, and use this constant value on the
leaf interval. The error is at most $h/4+h/4=h/2$, including the last
endpoint. Use the value of the later leaf at a shared endpoint, and the last leaf at time $n$.
The first rounded height is zero. Successive rounded heights
differ by at most $3h/4$, and hence certainly by at most $h$; at most
five choices of the next multiple of $h/2$ suffice. No absolute height
range is needed in this count.

The shape of each forest is encoded by the preorder string of internal
nodes and leaves. Its length is $I+J=2J-B\le2M$. Across all such forests
there are fewer than $2^{2M+1}$ strings. Given a shape, there are at most
$5^M$ admissible rounded-height sequences. Therefore the fixed dictionary
of all these step profiles has
$$
K\le2\,20^M,\qquad
\log K\le C_\rho\frac n{h^2},\qquad
\mu_n\left(\min_f\max_{0\le j\le n}|H_j-f_j|\le h/2\right)
\ge1-\rho,                                              \tag{53}
$$
where, for example, $C_\rho=(9C_T/\rho+2)\log20$ works.
The dictionary is fixed before sampling. The tree and rounded values
serve only to select its index after the path is observed.

This eliminates the logarithmic overhead in the fixed-grid construction
of [12]. It uses only the uniform oscillation bound and an expected
node count. It does not assert exponential concentration of the number
of nodes. For $1\le h<32$, listing all $2^n$ binary paths gives the same
order $\log K=O(n/h^2)$ for the coefficient excursion law, with a larger
constant. The actual-source statement below will retain $h\ge32$.

### 7.2. Actual sources and proof of Theorem 2

At the source delay $d(N)=o(g(N))$, use the strengthened four-bit skeleton
TV estimate $\delta_N$ in (10) and the established affine error $\eta_N$.
For every dictionary profile, interpolate its values at the skeleton
recorded times. Success of the excursion approximation implies success
at those times. The union of these recorded-height events transfers with
one additive $\delta_N$. Interpolating the actual recorded heights and
then using the deterministic $4\lambda$ full-height error gives an
actual logarithmic path within
$$
h/2+4\lambda+\eta_N
$$
of at least one of the interpolated dictionary profiles, with probability
at least $1-\rho-\delta_N$. The dictionary cardinality is unchanged.
For $h\ge32$ and all sufficiently large $N$, this is at most $h$.
This concerns population coverage, not exclusion of any individual source.

Combining (53) with (50) yields a sharp order
for the number of deterministic profiles needed to cover a fixed fraction
of actual survivors. More precisely, fix $q\in(0,1)$ and let $K_N(q,h)$
be the least cardinality of a deterministic family whose sup-norm $h$-tubes
capture probability at least $q$ under the actual logarithmic path law.
For every $32\le h_N=o(\sqrt N)$ in the same source-delay range,
$$
\log K_N(q,h_N)=\Theta_q(N/h_N^2).                       \tag{54}
$$
For the upper bound choose $\rho=(1-q)/2$ in (53); eventually
$\delta_N<(1-q)/2$. For the lower bound, (50) gives
$$
q\le K_N C_*(1+v_N)^{3/2}e^{-c_1v_N}+\delta_N,\qquad
v_N=\frac{N-1}{(h_N+\eta_N+4\lambda+1)^2}.                \tag{55}
$$
Here $v_N\asymp N/h_N^2\to\infty$ uniformly for $h_N\ge32$ and
$\eta_N\to0$. Taking logarithms and absorbing the $O(\log v_N)$ term
proves the lower bound. The implicit constants may depend on $q$ and
on the already explicit fixed walk parameters. The threshold is large;
this theorem does not supply an efficient algorithm for listing the
entire dictionary at realistic horizons.


## 8. Scope, verification and reproducibility

The main conclusions are proved analytically. The probability inputs (7)--(9),
the affine estimate (11), and the exact conditional Fourier factorization
are imported from the cited version of [1], whose proofs and five finite
audits remain available there. This paper did not rerun or enlarge those
audits. Appendix A proves the sharper arithmetic error in (10).
The other new interfaces are the stopped weighted oscillation estimate,
the two-boundary conditioning argument, the interior Bernoulli-bridge
identification, the uniform tube bound, and the adaptive dictionary code.

Fourier Wren independently inspected the complete original path-law proof,
then the elementary replacement for tightness, the radial Brownian bridge
calculation, the tube estimate, and the adaptive forest/actual-source
transfer. He also inspected the full geometry manuscript and the complete
arithmetic argument in Appendix A's original P1--P14 form, including the
primary Bugeaud--Laurent statement and the conditional Fourier consumers.
He subsequently checked the strengthened arithmetic assembly and all uses
of the revised rate. He separately inspected Section 5.6 in full, its final assembly and equation
references, and the limiting-profile figure values. The supplement records
these successive inspection scopes and their public sources.
The supplement records the public links and the exact inspection scope.
The primary logarithm theorem is imported, not independently reproved.
Syntax/render validation of the mathematical displays is
recorded separately and is not a correctness check.

Wren also supplied numerical finite-state dynamic programs for area,
maximum, the weighted inequality, and sampling the forest size. Those
scripts use float64 probabilities and floating-point barrier comparisons;
they are not exact-arithmetic certificates. Their source and reported
outputs were inspected, but they were not rerun here and are not used in
the proof or to claim an asymptotic rate. They remain public at [12].
No numerical experiment is required to reproduce the analytic conclusions.

The supplement contains the original author proof notes, the literature
reading scope, a dependency map, and hashes. It keeps the alternative
h-transform proof for comparison; the main manuscript uses the elementary
oscillation proof. The arithmetic source error is paid once for every
whole-path or union-of-profiles event, irrespective of dictionary size.

The theorem is limited to $d(N)=o(g(N))$. It supplies no fixed-ratio-delay
count, rate of convergence to Brownian excursion, sharp covering constant,
or efficient procedure for enumerating the entire dictionary at practical
source sizes. The uncapped moment conclusion additionally uses the exact conditioning
density cap and a uniform Gaussian peak tail, as proved in Section 5.6.
Weak convergence and an abstract total-variation error alone would not
supply that conclusion. No individual-source descent, cycle exclusion or
resolution of Collatz follows. The logarithmic good-vertex budget does
not supply linear occupation or the arithmetic control required for a
fixed-ratio source-delay theorem.


## Appendix A. A stronger arithmetic input in a smaller frequency window

The four-bit recurrence of [1] converts good phase vertices into Fourier
cancellation, including after conditioning on the entire count skeleton.
We retain the starting count in a classical $3$-adic logarithm bound and
iterate it across maximal stretches with zero carry. This improves the
source-discrepancy rate used in (10). The logarithmic scale and general
method of comparing successive arithmetic gaps have classical precedents
[3, Lemma 3.1 and Corollary 1.5]; the application here is to the precise
Collatz carry labels and their conditional Fourier consumer.

### A.1. A deterministic count of good vertices

The conductor exponent $m$ in this appendix is separate from the full
horizon $N$. Let $N\ge m\ge16$ and let $b$ be odd with
$0<|b|<2^{m-3}$. Put
$$
s=\lfloor(m-3)/4\rfloor,\qquad d_t=m-2-4t.
$$
Every path supported by the recurrence in [1, Section 4] has counts
$A_0=0$, $k_t=A_{t+1}-A_t\in\{0,\ldots,4\}$, and
$3^{A_t}>16^t$ for $t>0$. Use equality at $t=0$. In particular
$A_t\le4t\le m$ and $1\le d_s\le4$.
Let $r_t$ be the centered odd representative of
$b3^{-(A_t+3)}$ modulo $2^{d_t}$, using $(-2^{d_t-1},2^{d_t-1}]$.
Define the integer label and carry by
$$
q_t=\frac{3^{A_t+3}r_t-b}{2^{d_t}},\qquad
3^{k_t}r_{t+1}-r_t=z_t2^{d_{t+1}}.                       \tag{A1}
$$
A vertex is good when $|r_t|>2^{d_t}/98$. Let $G$ count the good
nonterminal vertices, $0\le t<s$.

The centered bounds give $|z_t|\le48$. If both endpoints of an edge
are bad, then $|z_t|\le(81+16)/98<1$, so $z_t=0$.
The terminal vertex is good because $r_s$ is odd and $2^{d_s}/98<1$.
Thus the number $J$ of nonzero carry edges satisfies
$$
J\le2G+1.                                                \tag{A2}
$$
Set
$$
B=1+\log|b|,\quad K_m=\log(m+1)+11,\quad
D_m=100K_m^2,\quad R_m=2(D_m+1).
$$
We will prove the explicit lower bound
$$
G\ge\Gamma(m,b):=\max\left\{0,
\frac{\log\big(m/[3(B+4)]\big)}{2\log R_m}-1\right\}.     \tag{A3}
$$
Its constants are deliberately coarse. The lower bound can remain zero
at enormous conductors; effectiveness here does not indicate a practical
finite threshold. For each fixed $0<a<1$, it does imply
$G\ge c_a\log m/\log\log m$ uniformly when $|b|\le\exp(m^a)$,
above an effective threshold.

For every vertex, including good ones, the source label satisfies
$$
\begin{aligned}
|q_t|&\le\frac{27}{2}3^{A_t}+|b|2^{-d_t}\\
&<\frac{27}{2}3^{A_t}+\frac{16^t}{2}
\le14\,3^{A_t}.                                         \tag{A4}
\end{aligned}
$$
The second line uses $m-3-d_t=4t-1$ and coefficient survival.
Retaining $A_t$ in this height bound is essential for the iteration.

### A.2. The valuation bound and its exceptional cases

We claim that if $m\ge16$, $0\le A\le m$, $1\le d\le m$,
$b$ is nonzero odd and $q\in\mathbb Z$ satisfies $|q|<14\,3^A$, then
$$
v_3(b+q2^d)\le D_m(A+B+4).                               \tag{A5}
$$
Here $b+q2^d$ is odd and nonzero, and $v_3(3)=1$. The source-window
condition is only needed for (A4), not for this valuation assertion.
Write $T=A+B+4$. If $q=0$, the valuation is at most
$\log|b|/\log3\le T$. If $q\ne0$ and $v_3(q)\ne v_3(b)$,
it equals their minimum and has the same bound.

Otherwise let $v=v_3(q)=v_3(b)$, $q'=q/3^v$, $b'=b/3^v$, and
$\alpha_2=-b'/q'$. These are rational $3$-adic units, of either sign, and
$$
v_3(b+q2^d)=v+v_3(2^d-\alpha_2).                         \tag{A6}
$$
If $2$ and $\alpha_2$ are multiplicatively independent, use
Bugeaud--Laurent [2, Theorem 3] with prime $3$, bases $2,\alpha_2$,
and positive exponents $d,1$. Their field parameter is $D=1$ and
$g=2$, since $2\not\equiv1\pmod3$. Put
$$
\log\mathcal A_1=\log3,\qquad
\log\mathcal A_2=\mathcal L
 :=\max\{\log\max(|b|,|q|),\log3\}.
$$
These dominate the required logarithmic heights; reducing $b'/q'$
can only lower its height. The theorem permits signed bases and assumes
only that they are $3$-adic units and multiplicatively independent.
Its auxiliary quantity is $d/\mathcal L+1/\log3\le(m+1)/\log3$.
Consequently its maximum factor is at most
$\max\{\log(m+1)+0.4,10\log3,10\}<K_m$, and the primary theorem gives
$$
v_3(2^d-\alpha_2)
\le\frac{72}{(\log3)^3}\mathcal L K_m^2
\le\frac{72}{(\log3)^2}T K_m^2.                          \tag{A7}
$$
Indeed $\mathcal L\le T\log3$ follows from
$\log|q|<\log14+A\log3$ and $B\ge1$.
Since $v\le T$ and $72/(\log3)^2<60$, this is sufficient for (A5).
We import the classical theorem as stated; we do not claim a new
logarithmic-form estimate or to reprove its interpolation argument.

If the two bases are multiplicatively dependent, rational unique
factorization gives $\alpha_2=\pm2^j$ for an integer $j$.
Its reduced numerator is odd because $b$ is odd, so $j=-k$ with
$k\ge0$ and $k\le\log_2|q|$. Then
$$
v_3(2^d-\alpha_2)=v_3(2^{d+k}\mp1)
\le1+\log_3(d+k).                                       \tag{A8}
$$
If the expression is not divisible by $3$, its valuation is zero.
Otherwise the elementary lifting-the-exponent identity gives
$1+v_3(d+k)$: apply it to $4^{(d+k)/2}-1$ for the minus sign and to
$2^{d+k}+1$ with $d+k$ odd for the plus sign. No zero expression occurs,
since $d\ge1$. Moreover $k<4+2m$, so $d+k<3m+4$.
For $m\ge16$, $1+\log_3(3m+4)<2K_m$; together with $v\le T$,
$T\ge5$ and $K_m>1$, this gives
$v_3(b+q2^d)<T+2K_m\le3TK_m^2<D_mT$.
Thus (A5) includes the dependent case as well.

### A.3. Iterating the retained height

Partition the vertex sequence at its $J$ nonzero edges. There are
$J+1$ maximal zero-carry segments, allowing singletons.
On a segment $t,\ldots,u$, the exact integer relations (A1) give
$r_t=3^{A_u-A_t}r_u$. Consequently
$$
b+q_t2^{d_t}=3^{A_u+3}r_u,\qquad
A_u+3\le v_3(b+q_t2^{d_t})\le D_m(A_t+B+4).              \tag{A9}
$$
The inequality uses the nonzero integer $r_u$, (A4), and (A5).
Let $T_t=A_t+B+4$. Then $T_u\le(D_m+1)T_t$ on a segment.
An intervening nonzero edge increases $A$ by at most four, hence
$T_{u+1}\le2T_u$ because $T_u\ge5$. Iterating gives
$$
T_s\le(D_m+1)^{J+1}2^J(B+4)
\le R_m^{J+1}(B+4)\le R_m^{2G+2}(B+4).                  \tag{A10}
$$
This also covers $J=0$ and adjacent nonzero edges. Finally
$4s\ge m-6$ and survival imply
$A_s>(m-6)/\lambda\ge m/3$ for $m\ge16$.
Thus $T_s\ge m/3$, and taking logarithms proves (A3).
For $B\le1+m^a$ its numerator is $(1-a)\log m-O(1)$ and
$\log R_m=2\log\log m+O(1)$, proving the stated effective consequence.
This is a multiplicative bound on the retained height; it does not assert
an additive recharge budget or linear good-vertex occupation.

### A.4. Conditional Fourier decay and the shell transfer

Let $E_N$ be the coefficient excursion with its final free bit, let
$Y$ be its source residue divided by $2^N$, and let $\mathcal C_N$
be its complete four-bit count skeleton with residual bits retained.
Use
$$
T_N(h)=\mathbb E_{E_N}\left|
 \mathbb E_{E_N}[e(hY)\mid\mathcal C_N]\right|,
\qquad \vartheta=1-\frac{1-\cos(\pi/98)}{64}<1.
$$
Paper87 [1, (16) and (J1)--(J4)] proves that if every supported
count path has at least $G_0$ good nonterminal vertices, then
both $T_N(2^{N-m}b)$ and the ordinary Fourier modulus are at most
$\vartheta^{G_0}$. The conditional assertion uses the local factorization
with the pair $1101,1110$ merged before taking absolute values.
It is stronger than the unconditional Fourier bound alone.
Using $G_0=\lceil\Gamma(m,b)\rceil$ in that same kernel gives
$$
T_N(2^{N-m}b)\le\vartheta^{\Gamma(m,b)}.                  \tag{A11}
$$
Equivalently the rare-event term in [1, (J2)] is zero. There is no
additional division by the global probability of coefficient survival.
The same argument applies to [1]'s finer record that identifies only
$1101$ with $1110$ and retains other blocks exactly.

In particular there are effective absolute $c,C>0$ such that
$$
\sup_{\substack{b\text{ odd}\\0<|b|\le e^{\sqrt m}}}
\sup_{N\ge m} T_N(2^{N-m}b)
\le C\exp[-c\log m/\log\log m]                           \tag{A12}
$$
above an effective threshold. The numerator window is smaller than the
$2^{m/2}$ window in [1, (J5)]; the stronger rate is asserted only where
(A12) applies. This smaller window suffices for ordinary intervals.

Let $D_{N,\mathcal C_N}$ be the conditional interval discrepancy of $Y$.
Apply Erdős--Turán separately to each conditional measure and average,
using the normalization in [1, (J6)]. Choose
$H=\lfloor\exp(\xi g(N))\rfloor$ with a sufficiently small fixed $\xi>0$.
Every $1\le h\le H$ reduces to an odd $b$ at conductor
$m=N-v_2(h)\ge N-\log_2H\ge N/2$, and $b\le H\le e^{\sqrt m}$
eventually. Therefore
$$
\mathbb E D_{N,\mathcal C_N}
\le\frac1{H+1}+3\sum_{h=1}^H\frac{T_N(h)}h
\le C_0e^{-c_0g(N)}=\varepsilon_N.                       \tag{A13}
$$
The harmonic factor $1+\log H$ is absorbed in a smaller effective
exponent. The ordinary discrepancy and the finer record obey the same bound.

For an interval $I$ of length $a>\varepsilon_N$, the exact conditioning
calculation in [1, (J9)] gives
$$
d_{\rm TV}\big(\mathcal L(\mathcal C_N\mid Y\in I),
                \mathcal L(\mathcal C_N)\big)
\le\frac{\varepsilon_N}{a-\varepsilon_N}.                \tag{A14}
$$
Take $I=[2^{-d(N)-1},2^{-d(N)})$.
For $d(N)=o(g(N))$ its mass is positive eventually and (A14) tends to zero.
The same conclusion even holds for $d(N)\le c' g(N)$ with a sufficiently
small fixed effective $c'>0$; the main statements use the simpler little-o
range. Because $L=N-d(N)\sim N$, all shell sources eventually exceed
$900N^{14.3}$. The established high-source comparison [1, Section 7.1]
therefore makes actual and coefficient survival agree, and the established
affine estimate applies. This proves exactly the strengthened arithmetic
input (10), including source nonemptiness, with the effective error stated
there. It gives no fixed-ratio delay or individual-integer descent theorem.

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