Math-2909 · mathematics · Probability

Math-2909

Glauber cutoff throughout the high-temperature SK phase

Status OpenLLM status Open unverified

Top Open Problems unknownunknown Not reported cataloguedOpenOpen

Statement

Definitions and mathematical statement

Statement recorded in the supplied source.
For each fixed $0\leq \beta <1$, does worst-start heat-bath dynamics for Gaussian SK have total-variation cutoff at (log n)/(2lambda(beta)) in continuous site-rate-one time and at n times that scale in single-update discrete time, in disorder probability? Require a deterministic $\lambda (\beta )>0$, $\lambda (0)=1$, and $\lambda (\beta )\leq (1-\beta )^2$.

Short English statement

For each fixed $0\leq \beta <1$, does worst-start heat-bath dynamics for Gaussian SK have total-variation cutoff at (log n)/(2lambda(beta)) in continuous site-rate-one time and at n times that scale in single-update discrete time, in disorder probability? Require a deterministic $\lambda (\beta )>0$, $\lambda (0)=1$, and $\lambda (\beta )\leq (1-\beta )^2$.

Sources

Statement licence: CC-BY-4.0 · Source file

Contributions 1

Math-2909-A1 · Literature report · LLM-generated · unknown ·

OpenAI claim summary for Math-2909

Worst-start SK heat-bath dynamics is claimed to cut off at $(2\lambda(\beta))^{-1}\log n$ for each fixed $0\leq\beta<1$ in per-site rate-one time. The question retains these same parameters and conventions. Within these assumptions, this is a claimed affirmative answer to the stated question.

Glauber cutoff throughout the high-temperature SK phase

OpenAI's claim

OpenAI presents a proof in the selected manuscript. Worst-start SK heat-bath dynamics is claimed to cut off at $(2\lambda(\beta))^{-1}\log n$ for each fixed $0\leq\beta<1$ in per-site rate-one time.

Mathematical statement of the claimed result

Result 1.
For independent standard Gaussian $J_{ij}$, $i<j$, with $J_{ji}=J_{ij},J_{ii}=0$, use the zero-field SK measure \[ \pi_{n,\beta}^J(x)=Z^{-1}\exp\left(\frac\beta{\sqrt n}\sum_{i<j}J_{ij}x_ix_j\right),\qquad x\in\{-1,1\}^n. \] Continuous heat-bath dynamics refreshes every site at rate one; a discrete step refreshes one uniformly selected site, including attempts that retain its spin. For every fixed $0\le\beta<1$, there is a deterministic $\lambda(\beta)>0$. Set $t_n=\log n/[2\lambda(\beta)]$, let $d_n(t)$ be the maximum continuous-time distance over initial states, and define $d_n^{\mathrm{disc}}(k)$ analogously. For every $0<\epsilon<1$, in disorder probability, \[ d_n((1-\epsilon)t_n)\to1,\quad d_n((1+\epsilon)t_n)\to0, \] \[ d_n^{\mathrm{disc}}(\lfloor(1-\epsilon)nt_n\rfloor)\to1,\quad d_n^{\mathrm{disc}}(\lceil(1+\epsilon)nt_n\rceil)\to0. \] The rate obeys $\lambda(0)=1$ and $0<\lambda(\beta)\le(1-\beta)^2$ for $0<\beta<1$; its definition through an equilibrium gradient operator is implicit. Temperature is fixed before $n\to\infty$.

Outline of the argument

Finite-path Gaussian calculus and projected likelihoods identify the equilibrium spin spectral decay. The proof makes one sufficiently long comparison block uniform over initial configurations before iterating it, allowing equilibrium information to control logarithmic-time worst-start total variation.

Formal verification

For family 227, the selected statements include the high-temperature gap for $\beta<1$, cutoff ratios only for $\beta<1/2$, critical lower mixing scales with a subexponential upper bound, and low-temperature barriers. They do not provide the full critical exponent, the full cutoff location, or the newer autocorrelation limits. The listed Comparator files are CriticalSK.lean, CriticalSKMixing.lean, SKBarriers.lean, SKHighTemperature.lean, SKRatio.lean. Named declarations include OAI.CriticalSK.covariance_and_linear_tests, OAI.CriticalSK.critical_mixing_bounds. This site has not executed the Lean code. The scope descriptions do not certify the remaining manuscript arguments.

Status caveat

OpenAI attributes these manuscripts to work by an internal model. Its release cautions that arguments without formalization can contain mistakes. This catalogue has not independently checked the proofs; the labels report the mathematical claims attributed to OpenAI.

Sources

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