OpenAI reports the higher-dimensional Erdős distinct-distance bound

For every fixed dimension d≥3, a constant c_d>0 makes every n-point set of distinct points in ℝᵈ determine at least c_d n^(2/d) distinct distances.

Sources for this record

What was already known

The n^(2/d) order was conjectured for higher-dimensional point sets and is optimal up to a dimension-dependent constant by integer-grid examples.

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

Resolves the higher-dimensional Erdős distinct-distances conjecture at the optimal exponent.

Limitations

The constant may depend on fixed d; no uniform dependence on growing dimension or planar analogue is claimed. No completed proof check was documented in the available sources.

Technical verification Reported

Reported is the default when evidence for a higher level is insufficient. The OpenAI-authored manuscript reports and argues the result, but the reviewed sources do not document a completed check of the full assessed claim.

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 manuscript, attribution, and prior-work review for non-notifying local inclusion; technical check remains undocumented.

Read this published revision