Theendofplausible.
AI gives you an answer. StandardProofs gives you a verdict. We check every step, expose the assumptions nobody wrote down, and settle every checkable claim with deterministic solvers. Never a maybe.
AI gives you an answer. StandardProofs gives you a verdict. We check every step, expose the assumptions nobody wrote down, and settle every checkable claim with deterministic solvers. Never a maybe.
No doctor acts on an answer because it sounds right. No lawyer files one because it feels right. Yet the most consequential answers in the world now come from AI that can only promise one thing: plausible.
We built StandardProofs to end the era of plausible.
Watch it catch a mistakeBulletproof, but they only work on problems already written as math. Almost nothing is.
Works on everything, but it cannot tell you when it is wrong. A confidence score is a feeling with a decimal point.
Sits in between. We take AI reasoning in plain language, check every step, and settle every checkable claim with a deterministic solver.
This is a real answer being checked, live. Flip the switch to sneak an error in, and watch it get caught.
The answer, step by step
Problem: A 20 kg child is prescribed 300 mg of acetaminophen every 6 hours. Is the dose appropriate?
The child weighs 20 kg and is dosed every 6 hours
reason: given
Pediatric dosing is 10–15 mg/kg per dose, max 75 mg/kg per day
reason: dosing reference
10 × 20 = 200 mg and 15 × 20 = 300 mg, so the per-dose range is 200–300 mg
reason: computation
300 mg lies inside 200–300 mg
reason: comparison
4 doses × 300 mg = 1,200 mg per day, under the 75 × 20 = 1,500 mg daily cap
reason: computation
The check
A frontier AI proposes an answer. We do not take its word for it.
Every answer comes back certified or declined, with a full audit trail. No confidence scores. A decline names the step that failed.
Most real problems do not arrive as math. StandardProofs meets them in plain language and hands every checkable step to a deterministic solver: proofs, calculations, facts pulled from sources. Everything else is pinned to its reasons.
# finance · loan payment, step 04 >>> P, r, n = 50_000, Rational(6, 100) / 12, 60 >>> round(P * r / (1 - (1 + r) ** -n), 2) 966.64
-- healthcare · dose within range, steps 03–05 theorem dose_in_range (kg dose : ℕ) (h₁ : kg = 20) (h₂ : dose = 300) : 10 * kg ≤ dose ∧ dose ≤ 15 * kg ∧ 4 * dose ≤ 75 * kg := by subst h₁ h₂; decide
Nine of our certified answers on Humanity's Last Exam disagreed with the expert-written key. Independent review sided with us twice, the key once, and found the rest too close to call. We catch what the expert panels miss.
Full methodology and statistics
arXiv:2607.01223 ↗
When AI touches a clinical decision, probably right is not a standard. We catch what does not hold up before it reaches a patient.
Every conclusion arrives with its sources and steps attached. Challenge any line of it. That is what it is for.
Numbers that survive an audit, because they arrive with one.
The mistakes that hurt are not bad arithmetic. They are silent assumptions. We make them visible.
Today, verified answers. Next, verified work that runs for days: research programs, regulatory filings, engineering designs, drug candidates. Eventually, a norm: if a machine claims something about the world, it shows its work, or it does not get believed.
“If the shuttle could weave by itself and the plectrum could play by itself, masters would need no servants.”
Aristotle, Politics I.4, 1253b33
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The paper, the code, and the benchmark results are open for anyone to inspect, reproduce, or attack.