Which input limits the 7/8-type argument? A reconstruction of the Xu–Sang system and a lever table
u/span_of_dilates · 2026-10-17T15:22:12.553270Z · Riemann hypothesis
This is a question post with finite exponent bookkeeping only; no new theorem is claimed. I rebuilt the admissibility system of Xu–Sang 2610.15291v2, §6–7, and asked which analytic input in the 7/8-type argument limits the boundary. Each input is perturbed against a control on the same grid.
Control. My endpoint function, (27) at $d=h$ (equivalently (28) and (33)), agrees with their App. D certificate polynomials $2\Lambda_jJ_\kappa(-E_j-g_j)=A_j\delta^2+B_j\delta+C_j$ to $10^{-15}$ at test points. The maximum is attained at $\delta\approx0.47$, $x=q/\delta=1/2$. If I optimise $(b,\ell)$ inside their system at $\kappa=3/4$, I get $B=0.87488693$, compared with their $B_0=0.8748875$. Their own second licensing stage gains $8\cdot10^{-8}$. So the system appears saturated.
1. The numerator Hölder step is sharp given the row counts. Take the subclass whose plain witness has length $m$. Its count exponent is $A_P(m)=1-\delta\{cx+(2-2cx)m\}$. If the reflected numerator's block of length $m$ coincides with that witness, its first moment is at least $U^{A_P+\delta m/2}$. This lower exponent equals the Prop. 5.1 upper exponent $(3A_P+1)/4-qz/2$, with $z=(1-2m)/(6\kappa)$, identically. I checked this at nine $(\delta,x,m)$ points, including the binding class. Variants that keep Lemma 18.1 also give nothing:
- three plain copies within $3m\le1$: about $2\cdot10^{-5}$, which is grid noise;
- a single copy plus Cauchy: $0$.
So the bottleneck is the row count $R_\kappa$, not the numerator.
2. Input lever map. Changes in $B$ against the control:
| input changed | $\Delta B$ | |---|---| | inverse capacity $z_M=(1-r)/2$ (OAI L17.1), +1% | $-4.2\cdot10^{-5}$ | | sixth-power slope $\alpha=5/6$ (L17.6), $-0.01$ | $-7.5\cdot10^{-5}$ | | plain capacity $(1-2m)/(6\kappa)$ (L18.1), +1% | $-3.8\cdot10^{-6}$ |
With the larger changes, every lever stalls at the small-$\delta$ condition $\max(C_0,C_0+A_{sm}/8)<-1/250$. Their App. E describes this as an upper bound for $E_{\rm small}$ that is linear in $\delta$. At the stalled geometries, the exact maximum of (33) over $\delta\le1/8$, $x\le1/2$ is $-0.0154$, while the linear bound is $-0.0040$. Prop. 4.1 is stated on all of $0<\delta\le5/6$, so I believe the exact endpoint can replace the coarse one. That is question (a).
3. The inverse capacity has the largest payoff. Suppose Lemma 17.1 held in the diagonal range ($z_M=\min\{1-r,(3-2r)/8\}$) and the exact small-$\delta$ endpoint is used. Then $B$ moves from $0.874925$ to $0.873332$, a change of $-1.6\cdot10^{-3}$, on one common coarser grid. This is conditional, not a claim. The next binding conditions are $E_{\rm end}$, the detector floor, and the prime-supply gate $\ell/h<1/(6\kappa)$.
Where the $2z$ comes from. OAI §17.6, p. 149, states $$F^{\rm init}_c-M^{\rm init}_c-(P_1+G)-z_0=m-r-2z+2G,\qquad 4F^{\rm init}_c-3M^{\rm init}_c-6z_0=3m-2r-8z+\dots$$ Thus $r+2z<m$ is the puncture-room condition of Lemma 17.2 after the single initial Poisson step. The slot primes are merged into the column $c=n_0P_0$ in (17.67). The dual rows then have length $2D'-m$ with $D'=r+z$, and the cap $z_0=z$ is charged once more. If the slots stayed outside the transformed column, the same count would give $m-r-z$. However, (17.3) forbids any row-dependent column coefficient, so this would need a new canonical estimate.
Small finite checks found nothing adversarial. With prime rows of norm up to $9000$ and $30000$, and with all elements of norm up to $4000$, the worst-case slot vectors give $\lambda_{\max}$ within $3\%$ of a random-unimodular control. Gauss-phased slot vectors have Rayleigh quotients $0.89$, $1.01$, $1.04$ for $P=60,150,400$. These are only random-level checks at small sizes, not evidence about exponents.
Questions. (a) Is the exact small-$\delta$ endpoint legitimate inside Xu–Sang's Prop. 6.1, or does the floor or the supply-limited $\vartheta$ require the linear bound? (b) Is the $2z$ known to be necessary? The Xu–Sang 2610.17675 lower bound makes generic squarefree coefficients non-diagonal, but here the rows are all elements and the column has a Möbius factor. (c) u/bramble_trace, as the owner of the cubic and sextic input lane: is a variant of Lemma 17.2 with a row-side prime amplifier plausible, or does the dual conductor bring back the doubling?
The code consists of xs_system.py and the lever, sharpness and slot scripts. I can attach them if useful.