VibeMathedMath problems solved with AI

Ball's extension problem for Lipschitz maps from Hilbert space into ℓ1\ell_1, via the Mendel-Naor metric Markov cotype question

Ball (1992) introduced Markov type and cotype to study Lipschitz extension and asked whether every Lipschitz map ff from an arbitrary subset of a Hilbert space HH into L1L_1 extends to all of HH with Lip(F)≤K Lip(f)\mathrm{Lip}(F)\le K\,\mathrm{Lip}(f) for a universal KK. Mendel and Naor (2013) formulated metric Markov cotype two in Cesaro form: XX has it with constant CC if for every reversible stochastic matrix AA with stationary π\pi, every tt and points x1,…,xn∈Xx_1,\dots,x_n\in X there are y1,…,yn∈Xy_1,\dots,y_n\in X with ∑iπid(xi,yi)2+t∑i,jπiaijd(yi,yj)2≤C2∑i,jπi(1t∑s=1tAs)ijd(xi,xj)2\sum_i\pi_i d(x_i,y_i)^2+t\sum_{i,j}\pi_i a_{ij}d(y_i,y_j)^2\le C^2\sum_{i,j}\pi_i(\frac1t\sum_{s=1}^tA^s)_{ij}d(x_i,x_j)^2. They showed that a positive answer for ℓ1\ell_1 yields the extension, and asked (Question 1.15): is N2(ℓ1)<∞N_2(\ell_1)<\infty, that is, does ℓ1\ell_1 have metric Markov cotype two?

Result
Proved(see note)
Status
Candidate (review pending)
AI contribution
AI-discovered
Method
Argument
Field
Metric geometry; nonlinear Banach space theory, Lipschitz extension
Posed by
Keith Ball (1992, extension problem); Manor Mendel and Assaf Naor (2013, Question 1.15)
Year posed
1992
Years open
34y
Solved
2026-10-05
Model
Unreleased internal OpenAI model
Vendor
OpenAI
Collaborators
—
Verification
Unreviewed
Publication
Announced
Collection
OpenAI math release (October 2026), version adc7f12
Significance
35 / 100
Disclosed cost
—
Wikipedia
No dedicated article

What was actually shown

Theorem 1.1: real ℓ1\ell_1 has metric Markov cotype two with N2(ℓ1)≤1221N_2(\ell_1)\le12\sqrt{21}, uniformly in the number of points, coordinates and time parameter; complex ℓ1\ell_1 follows with 124212\sqrt{42}. Corollary: there is a universal KK such that every Lipschitz f:S→ℓ1f:S\to\ell_1, SS any subset of a real Hilbert space, extends to F:H→ℓ1F:H\to\ell_1 with Lip(F)≤K Lip(f)\mathrm{Lip}(F)\le K\,\mathrm{Lip}(f). The construction is nonlinear (expected coordinatewise medians of geometric-walk endpoints after cut smoothing), consistent with the known failure of Ball's linear Markov cotype for ℓ1\ell_1 and the Kalton-based subspace counterexample. No optimal constant is claimed, and targets other than ℓ1\ell_1 are not treated.

What the AI did

The release README says all results were produced by an unreleased internal OpenAI model using one fixed procedure, about three hours of ChatGPT Pro thinking compute per result on average. This result is not among the README's exceptions (the Hodge conjecture for CM abelian varieties, and the Re(s) > 11/12 zero-free region whose write-up was human-edited). The manuscript is authored 'OpenAI' and names no human author. It is a single paper; the extension theorem is derived as a corollary by combining the main theorem with Mendel and Naor's published extension theorem.

Verification

No independent mathematician has checked this yet. Checked here: the abstract, introduction, Theorem 1.1 and the final section of the TeX source were read against Ball's problem and Mendel-Naor Question 1.15. The paper proves N2(ℓ1)≤1221N_2(\ell_1)\le12\sqrt{21} directly and obtains the extension corollary by invoking Mendel-Naor Corollary 1.13, so the extension relies on that published theorem. The family has no Lean formalization (lean/docs/332.md does not exist at the pinned commit). The target is real (and, by a corollary, complex) ℓ1\ell_1; the paper says 'for this target' and does not discuss general L1(μ)L_1(\mu) targets.

Sources

Changelog1 change

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