Source authenticated

Yau's nodal-set conjecture for smooth metrics: is nodal measure comparable to sqrt(lambda)?

Dimensions three and four (Theorems 1.1, 1.2 of the principal manuscript): a smooth metric arbitrarily close to the round metric on S3S^3, and one on S2×T2S^2\times T^2, with exact eigenfunctions whose nodal measure divided by λ\sqrt\lambda is unbounded. Dimension five and up (companion): a metric on S4×S1S^4\times S^1 with nodal measure ≫λ1/2+ϵ0\gg\lambda^{1/2+\epsilon_0} for a fixed ϵ0>0\epsilon_0>0, and products give every dimension above five. Surfaces (companion): H1(Zu)≤C(M,g)λ\mathcal H^1(Z_u)\le C(M,g)\sqrt\lambda, so with Bruning's lower bound Yau's conjecture holds in dimension two. So the upper bound is true for surfaces and false for smooth metrics in every dimension ≥3\ge3; the lower bound (Logunov) is untouched, and real-analytic metrics are unaffected. No power excess is claimed in dimensions 3 and 4.

Exact FrontierDelta

Prior state unknown→disproved

Scope and record

Occurred: Sep 23, 2026

Delta type: SOURCE CLAIM

Assumptions: VibeMathed verification: lean-checked. Publication: announcement. AI contribution: ai-discovered. VibeMathed editorial classifications, scores, notes, relations, and dataset structure are CC BY 4.0. Source statements and linked content retain their own rights.

Canonical aliases: Yau's nodal-set conjecture for smooth metrics: is nodal measure comparable to sqrt(lambda)? · Yau's nodal-set conjecture (smooth)

Confidence: Not scored

Registry verification: lean checked · announcement · candidate

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Attribution

VibeMathed
registry · event recorded by

Unreleased internal OpenAI model
model · ai model contributor · OpenAI

Artifacts and verifiers

Lean proof, S^3 (OAI.YauCounterexamples.sphere_three)

lean artifact · pending

Artifact ↗
Lean proof, S^2 x T^2 (OAI.YauCounterexamples.sphere_two_torus_two)

lean artifact · pending

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Lean proof, S^4 x S^1 (OAI.Yau.Target.yau_nodal_set_upper_bound_counterexample)

lean artifact · pending

Artifact ↗
Lean proof, surfaces (OAI.SharpNodal.Main)

lean artifact · pending

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Comparator statement: SmoothYau.lean

lean artifact · pending

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Comparator statement: YauCounterexample.lean

lean artifact · pending

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Comparator statement: NodalLength.lean

lean artifact · pending

Artifact ↗

Compute record

No linked compute attempts recorded.

Lineage and corrections

This event attributed to Unreleased internal OpenAI model

Lean proof, S^3 (OAI.YauCounterexamples.sphere_three) evidence for this event

Lean proof, S^4 x S^1 (OAI.Yau.Target.yau_nodal_set_upper_bound_counterexample) evidence for this event

Comparator statement: NodalLength.lean evidence for this event

Comparator statement: YauCounterexample.lean evidence for this event

VibeMathed record: Yau's nodal-set conjecture for smooth metrics: is nodal measure comparable to sqrt(lambda)? evidence for this event

Lean proof, S^2 x T^2 (OAI.YauCounterexamples.sphere_two_torus_two) evidence for this event

Yau's nodal-set conjecture for smooth metrics: is nodal measure comparable to sqrt(lambda)? evidence for this event

Companion: Power-law violations of Yau's nodal upper bound evidence for this event

Companion: Sharp nodal length on smooth surfaces evidence for this event

Yau's nodal-set conjecture for smooth metrics: is nodal measure comparable to sqrt(lambda)? parent of this event

Comparator statement: SmoothYau.lean evidence for this event

Dimensions three and four (Theorems 1.1, 1.2 of the principal manuscript): a smooth metric arbitrarily close to the round metric on S3S^3, and one on S2×T2S^2\times T^2, with exact eigenfunctions whose nodal measure divided by λ\sqrt\lambda is unbounded. Dimension five and up (companion): a metric on S4×S1S^4\times S^1 with nodal measure ≫λ1/2+ϵ0\gg\lambda^{1/2+\epsilon_0} for a fixed ϵ0>0\epsilon_0>0, and products give every dimension above five. Surfaces (companion): H1(Zu)≤C(M,g)λ\mathcal H^1(Z_u)\le C(M,g)\sqrt\lambda, so with Bruning's lower bound Yau's conjecture holds in dimension two. So the upper bound is true for surfaces and false for smooth metrics in every dimension ≥3\ge3; the lower bound (Logunov) is untouched, and real-analytic metrics are unaffected. No power excess is claimed in dimensions 3 and 4. parent of this event

Lean proof, surfaces (OAI.SharpNodal.Main) evidence for this event

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