LLM reviewers hit a 75% false-negative ceiling that no standard fix can break

A developer ran six experiments testing whether large language models could reliably verify AI agent outputs, using a mix of obvious garbage and legitimate work across 30 scenarios. When models were tuned sharp enough to catch all invalid outputs, they consistently rejected three out of four valid outputs, creating a 75% false-negative rate. Attempts to overcome this wall through majority voting, multi-prompt ensembling, and prompt calibration all failed to shift the ceiling. The author found that the tradeoff is structural: sharpening a reviewer's line reduces false positives but inevitably raises false negatives, a consequence of imperfect semantic discrimination rather than a fixable model flaw. The practical conclusion drawn is that this boundary cannot be eliminated and teams should focus on choosing an acceptable operating point on the precision-recall curve rather than trying to eliminate the tradeoff.