AI can stay reliable when inputs or workflows become noisy, shifted, repeated, or long-running

Can it still perform well when tasks or data differ from what it usually sees?

Handles shifted tasks and data

ARC-AGI-2 also provides partial evidence that performance can extend under structural shift instead of only familiar distributions. The support is real, but still narrower than full deployment-style robustness.

Does it give similarly good results when the same task is repeated?

Stays consistent across repeated runs

Can it stay on track during long workflows without gradually drifting?

Stays on track over time