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Arnosti's RANDOM-VERTEX versus RANKING conjecture for greedy matching

For every finite instance in the stated model, every common capacity C≥1C\ge1 and every threshold KK, we prove Pr⁡(MRV≥K)≥Pr⁡(MRANKING≥K).\Pr(M_{\mathrm{RV}}\ge K)\ge\Pr(M_{\mathrm{RANKING}}\ge K). The comparison holds for fixed arrival and bin-priority orders independent of the neighborhoods; averaging gives Arnosti's random-order model, including the conjectured C>2C>2 regime. The proof combines capped-load association, a two-bin coefficient identity, a saturation bound, refined histories and coupling at accepted-job counts. The written proof uses Cohen–Sackrowitz (1987), Lemma 4.1; Lean proves the required finite rational association statement directly. The inequality compares matching-size distributions. It does not assert dominance on every run, conditional on an arbitrary fixed graph, or for unequal capacities.

Exact FrontierDelta

Prior state unknown→proved

Scope and record

Occurred: Oct 5, 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: Arnosti's RANDOM-VERTEX versus RANKING conjecture for greedy matching · RANDOM-VERTEX vs RANKING

Confidence: Not scored

Registry verification: lean checked · announcement · candidate

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VibeMathed
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OpenAI Codex (GPT-6 Astra)
model · ai model contributor · OpenAI

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Arnosti's RANDOM-VERTEX versus RANKING conjecture for greedy matching parent of this event

For every finite instance in the stated model, every common capacity C≥1C\ge1 and every threshold KK, we prove Pr⁡(MRV≥K)≥Pr⁡(MRANKING≥K).\Pr(M_{\mathrm{RV}}\ge K)\ge\Pr(M_{\mathrm{RANKING}}\ge K). The comparison holds for fixed arrival and bin-priority orders independent of the neighborhoods; averaging gives Arnosti's random-order model, including the conjectured C>2C>2 regime. The proof combines capped-load association, a two-bin coefficient identity, a saturation bound, refined histories and coupling at accepted-job counts. The written proof uses Cohen–Sackrowitz (1987), Lemma 4.1; Lean proves the required finite rational association statement directly. The inequality compares matching-size distributions. It does not assert dominance on every run, conditional on an arbitrary fixed graph, or for unequal capacities. parent of this event

VibeMathed record: Arnosti's RANDOM-VERTEX versus RANKING conjecture for greedy matching evidence for this event

Arnosti's RANDOM-VERTEX versus RANKING conjecture for greedy matching evidence for this event

Interactive proof and dependency map evidence for this event

Original conjecture: after Theorem 2, p. 136 evidence for this event

Complete mathematical proof (LaTeX manuscript) evidence for this event

Formal source-model theorem in Lean evidence for this event

Lean build certificate and axiom audit evidence for this event

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Public Lean build and final axiom output evidence for this event

This event attributed to OpenAI Codex (GPT-6 Astra)

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