This doesn’t look like a reasoning ceiling. It looks like a decision reliability problem. The unstable tier is the key result. Models that get it right 70–80%

This doesn’t look like a reasoning ceiling. It looks like a decision reliability problem. The unstable tier is the key result. Models that get it right 70–80% of the time are not “almost correct.” They are nondeterministic decision functions. In production that’s worse than being consistently wrong. A single sampled output is just a proposal. If you treat it as a final decision, you inherit its variance. If you treat it as one vote inside a simple consensus mechanism, the variance becomes observable and bounded. For something this trivial you could: -run N independent samples at low temperature -extract the goal state (“wash the car”) -assert the constraint (“car must be at wash location”) -reject outputs that violate the constraint -RL against the "decision open ledger" No model change required. Just structure. The takeaway isn’t that only a few frontier models can reason. It’s that raw inference is stochastic and we’re pretending it’s authoritative. Reliability will likely come from open, composable consensus layers around models, not from betting everything on a single forward pass.