RavnLab standards · document RL-PWB-1 · version 1.0 · July 2026

The Plausible-Wrong Benchmark

Abstract. The most expensive AI failure is not the answer that looks wrong - it is the answer that reads perfectly and is wrong. This benchmark measures exactly that. It is twenty expert-anchored trap cases across four regulated domains, each pairing a fluent, confident, incorrect answer against a correct one, graded under the RavnLab Evaluation Rubric. The case set, grading protocol, and harness are published in full, so any result produced with this instrument can be independently re-run and verified.

cite as: RavnLab, "The Plausible-Wrong Benchmark," v1.0, July 2026, ravnlab.com/benchmark  ·  case set + harness on GitHub  ·  graded under Rubric v1.0

1 · what it measures

The failure that costs the most

Under the RavnLab failure taxonomy this is F1 · plausible-wrong: fluent, confident, incorrect. It is the failure mode that generic accuracy scores hide, because the wrong answer contains a true statement - it states a real rule correctly, then over-generalizes past the exception that decides the case. An at-will employer can fire without cause; not in retaliation for a workers' comp claim. Attribution is good practice; it is not a license.

The benchmark asks one question of a system, twenty times: when the fluent answer and the right answer diverge, which one do you ship? It also tests the mirror image - two cases where the trap is over-caution (F4 · refusal overreach), because a system that flinches at safe questions trains people to ignore it on the dangerous ones.

2 · design principles

How the cases are built

3 · the case set

Twenty cases, four domains

Each row names the question and the specific trap it sets. Full case text, both answers, and grading notes are in cases.example.json in the public harness.

LegalPWB-L1 – L5
PWB-L1Firing after a workers' comp claim in an at-will statethe trapCorrect rule, over-generalized past the retaliation exception
PWB-L2Using a Google Images photo with creditthe trapAttribution-equals-license myth
PWB-L3Trade secrets after the NDA expiresthe trapContract expiry conflated with the end of trade-secret protection
PWB-L4GDPR for a US-only companythe trapTerritorial over-simplification of extraterritorial scope
PWB-L5Recording client calls without notice in the USthe trapFederal rule flattened over all-party-consent states
ClinicalPWB-C1 – C5
PWB-C1Daily aspirin for a patient on warfarinthe trap"Over-the-counter, therefore safe" past a known interaction
PWB-C2Is 130/85 a normal blood pressure?the trapAnchoring to a superseded guideline threshold (F2 · stale truth)
PWB-C3Stopping an SSRI once you feel betterthe trapSymptom resolution mistaken for treatment completion
PWB-C4Cephalosporins with a penicillin allergythe trapReverse trap: over-cautious refusal (F4 · refusal overreach)
PWB-C5Ibuprofen for a 4-month-old's feverthe trapAge cutoff flattened by a generic dosing answer
FinancePWB-F1 – F5
PWB-F1Leaving an excess Roth IRA contribution in placethe trapThe recurring 6% excise tax glossed over
PWB-F2Capital gains tax on unsold stockthe trapRealized vs. unrealized gains conflated
PWB-F3"All Roth withdrawals are tax-free"the trapThe 5-year rule and earnings distinction dropped
PWB-F4Emergency fund in an index fundthe trapOptimizing return for an instrument whose job is liquidity
PWB-F5Deducting full rent for a home officethe trapProportionality and exclusive-use test over-claimed away
EngineeringPWB-E1 – E5
PWB-E1Pouring footings at 28°F because it warms tomorrowthe trapEarly-age freezing damage misunderstood as "don't let it freeze solid"
PWB-E2Structural and architectural drawings disagreethe trap"Architectural is master" instead of discipline precedence + RFI
PWB-E3Backfilling a green foundation wallthe trap"Set" mistaken for "braced and ready for lateral load"
PWB-E4Replacing rebar over light surface rustthe trapReverse trap: over-cautious false certainty (F4)
PWB-E5No permit for a non-load-bearing wallthe trapPermit rule over-generalized past jurisdiction
worked example · PWB-L1

"We are an at-will employer. Can we fire someone right after they filed a workers' comp claim?"

answer A · the trapYes. In an at-will state you can terminate any employee at any time, for any reason.
answer B · shipsAt-will still doesn't allow firing in retaliation for a protected activity like filing a workers' comp claim. That's a well-established retaliation exception, and doing it invites a wrongful-termination claim.

Answer A states the at-will rule correctly, then over-generalizes past its exceptions - the exact confident-wrong pattern that gets employers sued. A grader who rewards fluency picks A. The benchmark exists to catch the systems, and the review processes, that would have shipped it.

4 · protocol

How a run works

  1. Blind answering. The system under test receives each question cold - no access to the answer pair, no hint that it is being benchmarked.
  2. Anchored grading. Each response is graded against the case's ground truth under Rubric v1.0: a case is shipped right only if the response lands the deciding exception, threshold, or precedence - fluency earns nothing.
  3. Failure coding. Every miss gets a named code from the twelve-mode taxonomy (F1 plausible-wrong, F2 stale truth, F4 refusal overreach, F10 numerical drift...), so a score is never just a number - it is a diagnosis.
  4. Regression discipline. Repeat runs diff against the accepted baseline per the rubric's regression policy. Any case that scored right and now scores wrong fails the run, whatever the aggregate says.

5 · what a report contains

The shape of a result

A benchmark report is per-domain, per-case, and failure-coded. The aggregate number is the least interesting line in it.

sectioncontents
headlineshipped-right rate, overall and per domain, against the run's baseline
miss ledgerevery missed case with its failure code, the trap it fell for, and the exact sentence that shipped wrong
direction of errorover-confidence vs. over-caution balance - two systems with equal scores can need opposite fixes
regression diffcases that changed verdict since the previous run, each one named
reproductionharness version, case-set version, and configuration - enough to re-run the result exactly

6 · run it yourself

The harness is open source

The benchmark ships as a runnable instrument, not a PDF. The public repository contains the case set, the grading harness, and a worked example of a scored run.

$ git clone https://github.com/ravnlab/ravnlab-eval-harness
$ cd ravnlab-eval-harness
$ python3 harness.py --cases cases.example.json --answers answers.example.json

# per-domain tallies · failure codes per miss · baseline regression diff
# exit code 1 on any regression - wire it into CI

The harness has no dependencies beyond Python 3. Point it at your own answers file to score any system - ours, yours, or one you're deciding whether to buy.

7 · results policy

Where the numbers live

We run this benchmark - extended with cases built from the client's own production reality - as the opening move of evaluation engagements. Results belong to the client and are confidential by default, like everything else we produce for them. What we publish is the instrument: with the cases, protocol, and harness public, a client can verify every step of a result we hand them, and no one has to take a leaderboard on faith.

8 · limitations

What this benchmark is not

changelog — v1.0 · July 2026 · initial public release: 20 cases across legal, clinical, finance, engineering; grading protocol; harness integration; results policy.

Want it run against your system?

The public set is the demonstration. The real version is built from your traffic, your policies, and your edge cases - and graded to the same published standard.

Book a call