OpenAI reports the symmetric maximal triangular Hilbert estimate

For arbitrary complex F,G in L3(R²), the supremum of the triangular Hilbert transform over both finite hard-truncation endpoints is bounded in L^(3/2)(R²) by C times the two L3 norms.

Sources for this record

What was already known

Earlier square-function and finite-scale triangular-form bounds had dependence on the number or range of scales, including hard-truncation bounds growing with log(R/epsilon) at the symmetric tuple. They did not give one pointwise majorant over both endpoints (companion manuscript introduction, Previous work, pp. 2–3).

AI involvement AI-led

OpenAI credits itself as manuscript author and states that its internal frontier model produced the results in this release. Individual human input is not detailed enough to establish task-only autonomy.

OpenAI is named as author on the manuscript, and its release attributes the results to an internal frontier model. The individual human input is not detailed enough to establish autonomy.

Importance Major

Gives a scale-uniform pointwise maximal estimate at the symmetric triangular Hilbert exponent.

Limitations

The selected Lean scope covers the maximal L3×L3 to L^(3/2) bound and measurability on a common full-measure set. The first manuscript’s stronger annular r-variation estimate and principal-value consequences are outside this assessment.

Technical verification Formal (Lean 4)

OpenAI reports a Lean proof matching the selected claim.

Formal checking

The pinned Lean note proves the symmetric maximal estimate for all complex L3 inputs and a common full-measure set on which the truncated integrals are defined.

Checker
OpenAI-reported Lean 4 / Comparator
Scope
The L^(3/2) norm of the supremum over all finite hard-truncation intervals of the triangular Hilbert transform is bounded by C times the L3 norms of arbitrary complex inputs.
Basis
Reported by the linked checker
Evidence source

Checking a formal statement does not establish that it matches the original problem.

Human understanding Few

Few is the default when evidence for a higher level is insufficient. No named person is documented as explaining the central argument for this assessed contribution.

Correction and publication history

2026-10-18 · publication

Primary companion manuscript, attribution, prior-work, and exact Lean-scope review for non-notifying local inclusion.

Read this published revision