The grading wall — judge an answer you can't check
An "AI" hands you a nine-line derivation and a final answer. Half the time one step is silently wrong. You get 12 seconds — a grader's realistic attention budget — to rule Correct or Flawed. Play a few rounds and watch your accuracy hover near a coin flip. This is what supervising a stronger system feels like from the inside.
Conceptual toy: the "AI" is a script generating arithmetic chains, not a trained model. The time pressure is the point — real graders rating long AI outputs face exactly this budget problem.
Debate — make two AIs fight, judge the fight instead
Same impossible derivation — but now two AIs argue about it. A defender says it's fine; a critic points at one specific step. You never check the whole thing: you recheck only the disputed step, a five-second job, and award the win. Checking a pinpointed claim is exponentially easier than finding a flaw yourself.
The debaters here are scripted, but the asymmetry is real: verifying one named step is cheap even when auditing the whole answer is not.
Decomposition — slice the unjudgeable into judgeable bites
Second strategy: don't judge the big claim, shatter it into sub-claims so small each takes seconds. Here an AI totals an invoice. Instead of eyeballing the whole thing, click check on every line — each check is one tiny multiplication — and let the verdict assemble itself. Sometimes a line is corrupted, sometimes the total, sometimes nothing.
Every check button recomputes live from the invoice data — nothing is pre-labelled. Decomposition works perfectly here because an invoice is exactly a sum of independent parts. Hold that thought for EP 06.
Standing on trained shoulders — the recursive oversight ladder
Third strategy, the ambitious one: a human trains a helper AI; human + helper together oversee a stronger AI; that pair's judgment trains the next helper; repeat. This sketch computes the ladder live. Drag the capability jump between tiers and toggle the helpers off to see why anyone bothers with recursion at all.
Toy model: each tier is judgeable if its overseer's power (human + 90% of the previous model, when helpers are on) covers it. Real recursive reward modeling has no such neat formula — that's part of the problem.
The eloquent liar — when the better debater is wrong
Debate's dirty secret: the judge rewards persuasion, and persuasion is a skill separate from truth. Two debaters below disagree about which product is larger. In style-only mode the actual numbers are redacted — exactly like a judge who can't verify — so all you have is rhetoric. Vote, then see who was right. Then turn the evidence on and feel the difference.
The polished debater is scripted to always defend the wrong answer. That's the adversarial worst case — a model optimized to win judges, not to be right.
Every part true, the whole a lie — decomposition's blind spot
Now break EP 03's hero. An AI analyst pitches a company: three crisp claims, each backed by real data. Check them — they all verify. Approve or reject on that basis, like a decomposition pipeline would. Then audit the full record the claims were cherry-picked from.
The company data is generated fresh each round; the three claims are then picked from it, so every check you run is genuinely true — that's what makes the omission invisible.