Discovery isn’t the same as acceptance.
We’re entering an era where AI can discover mathematically meaningful structures — sometimes faster than humans can even decide what to do with them. But the next bottleneck isn’t “more intelligence.” It’s the missing layer between a compelling result and a claim the community can safely accept.
A simple way to see the gap is this: “It converged” is not a receipt.
Convergence can be real and still be fragile — dependent on a particular toolchain, discretization choice, precision setting, training setup, or evaluation harness. The moment the result leaves its birthplace, the natural question becomes: Can someone else verify it quickly, independently, and under slightly different conditions — without replaying the entire pipeline?