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Polynomial configurations and pointwise averages along Piatetski--Shapiro primes

Combinatorics · Submitted 3 Nov 2026 · 25 references

Pith's verdict: Solid extension of the density-increment machinery to Piatetski–Shapiro primes, matching the (log N)^{-κ} threshold. The main technical work—the comparison estimate and its integration—looks correct. Real risk: the entire argument depends on two unpublished preprints that supply the power-saving estimates. If those hold, this is a substantial contribution.

T0 · AI review

How this review was made: An AI referee (deepseek/deepseek-v4-flash) read the paper on November 4, 2026 and answered a fixed list of questions about it. T0 is the starting level every paper gets. The levels describe how deeply Pith has checked the paper, never the authors.

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arXiv ID2611.04575 (version 1)
Pith ID5PITWYFE, Pith's permanent record of this paper and its review
CitationHamed Mousavi. "Polynomial configurations and pointwise averages along Piatetski--Shapiro primes." arXiv:2611.04575 (2026). https://pith.science/paper/5PITWYFE
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Claim paper

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Referee report
The paper

The paper claims

稀疏素数参数下多项式配置仍有对数幂次密度门槛

The reading

这篇论文研究的是当配置中的差值取自极其稀疏的Piatetski–Shapiro素数减一时,整数集合中多项式配置的存在性与多重遍历平均的收敛性。Piatetski–Shapiro素数集的密度仅为普通素数的幂次稀疏(约$N^{1/c}/\log N$,$c>1$),但论文证明,只要$c$足够接近$1$,任何密度不低于$(\log N)^{-\kappa}$的整数子集必然包含形如$x, x+(p-1), x+(p-1)^2,\dots,x+(p-1)^k$的配置,其中$p$是Piatetski–Shapiro素数。这一密度门槛与普通素数参数下的最优结果相当。论文还证明,沿这些稀疏素数的多重多项式遍历平均几乎处处收敛,且极限与沿全体素数的平均相同。这意味着稀疏化并不改变极限行为,为在极低密度素数子集上的多项式回复理论提供了定量支撑。

What carries the argument

论证的核心机制是通过误差权重$D_\gamma = \Lambda_\gamma - \Lambda$将Piatetski–Shapiro素数权重转化为普通素数权重,其中$\Lambda_\gamma$是经过归一化的稀疏素数von Mangoldt函数。Shao–Sun的多项式相位估计(定理2.1)给出$D_\gamma$与任意多项式相位关联的幂次节省上界,Teräväinen定理(定理1.4)则将该上界转化为对更一般的多项式平均的控制,从而避免了显式的Gowers范数估计。在组合部分,这一比较被插入Krause–Mousavi–Tao–Teräväinen的密度增量论证中:通过一个巧妙的变量代换($n=Qm,\,z=Qx$)处理系数增长问题,使得若集合缺失配置则导致密度在某个算术级数上提升,反复迭代直至密度超过$1$得出矛盾。在遍历部分,通过$L^1$比较和求和分部得到逐点收敛。

Extended reading notes

Core claim

论文的核心发现包含两个定理。定理1.1:对于任意一族满足常数项为零且次数各不相同的整数多项式$P_1,\dots,P_\ell$,存在$c^*>1$和$\kappa>0$,使得对所有$1<c<c^*$,任意充分大的$N$和任意满足$|A|\geq N/(\log N)^\kappa$的子集$A\subset[N]$,都存在一个Piatetski–Shapiro素数$p=\lfloor n^c\rfloor$,使得配置$x, x+P_1(p-1),\dots,x+P_\ell(p-1)$全部落在$A$中且两两不同。定理1.2:在同样的参数范围内,沿Piatetski–Shapiro素数的多项式多重遍历平均几乎处处收敛,且极限与沿全体素数的平均一致。

Load-bearing premise

c必须足够接近1,使得1-γ小于一个依赖于多项式最高次数d和外部估计中常数(σ_d来自Shao–Sun,K来自Teräväinen)的显式上界,但这个上界在论文中未完全量化,仅要求1-γ < min(1,σ_d)/(8K)。

Editorial extensions

If this is right

  • 对于任意固定次数严格递增的多项式族,存在一个确定的$c^*$(仅依赖于最高次数),使得所有$1<c<c^*$对应的Piatetski–Shapiro素数参数都有相同的对数幂次密度门槛。
  • 沿Piatetski–Shapiro素数的多项式遍历平均的极限与沿全体素数的平均相同,从而将已有关于普通素数的点态收敛结果(Mirek–Wan–Wright)扩充到极稀疏素数子集。
  • 推论4.2给出相应的滞后$r$-变差估计($r>2$),这为进一步的几乎处处收敛和最大函数有界性提供了定量工具。
  • 定理1.1的证明本质上与具体的多项式族无关,只依赖于次数和系数有界,因此适用于所有满足常数项为零的有限个不同次数的整系数多项式。

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • 定理1.2目前要求$f_i\in L^\infty$,但论文Remark 1.3指出可能通过更精细的估计推广到$f_i\in L^{p_i}$($p_i>1$且$\sum p_i^{-1}\leq 1$)。若实现这一推广,将覆盖更多自然的时间演化系统。
  • 论证中的密度增量仅依赖于稀疏素数集的幂次节省相位估计,这暗示对更一般的零密度稀疏素数集(如素数的小区间子集或Maynard的有限数字素数)也可能获得类似结论,只要存在相应的Shao–Sun型估计。
  • Lean形式化([17])仅在有限个外部输入条件下验证了推理链,但外部结果(如Shao–Sun、Teräväinen)自身尚未形式化,因此完整的计算机验证仍依赖于这些算术输入的可靠翻译。
Original abstract

For every \(c>1\) sufficiently close to \(1\), we prove that there is \(\kappa>0\) such that every sufficiently large \(A\subset[N]\) of density at least \((\log N)^{-\kappa}\) contains \[ x,\ x+(p-1),\ x+(p-1)^2,\ldots,\ x+(p-1)^k, \qquad p=\lfloor n^c\rfloor\in\mathbb P. \] We also prove pointwise almost-everywhere convergence of the corresponding multiple ergodic averages.

The compiled record

The paper's own sentences, arranged, kept beside the review and separate from it. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-11-04: 1 request, 2,482 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.68 · human participants 0.01 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.78 · compares with prior results 0.06 · mentions code or data availability 0.01.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

For every \(c>1\) sufficiently close to \(1\), we prove that there is \(\kappa>0\) such that every sufficiently large \(A\subset[N]\) of density at least \((\log N)^{-\kappa}\) contains \[ x,\ x+(p-1),\ x+(p-1)^2,\ldots,\ x+(p-1)^k, \qquad p=\lfloor n^c\rfloor\in\mathbb P. \] We also prove pointwise almost-everywhere convergence of the corresponding multiple ergodic averages.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 For every \(c>1\) sufficiently close to \(1\), we prove that there is \(\kappa>0\) such that every sufficiently large \(A\subset[N]\) of density at least \((\log N)^{-\kappa}\) contains \[ x,\ x+(p-1),\ x+(p-1)^2,\ldots,\ x+(p-1)^k, \qquad p=\lfloor n^c\rfloor\in\mathbb P. result 0.96 0.95 0.85 asserted 1.00
s1 \] We also prove pointwise almost-everywhere convergence of the corresponding multiple ergodic averages. result 0.97 0.94 0.16 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

What was examined, and what was not

sources read
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  • figures and tables
  • references and citing sentences
  • relations to other papers
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not run
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  • T body sentences
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

References

Discussion

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