The argument, in one line: a reviewer is a mechanism for drawing a line. Every fix moves the line — but the line can't be eliminated, because it lives on a 3-dimensional surface where multiple defensible boundaries cross. So the 75% false-negative wall doesn't move, and the practical move is to stop trying to move it.
1. The wall
The setup was simple. Let an LLM review what an AI agent produced and judge whether it satisfies the task. Outputs were a mix of obvious garbage ("I am a little duck, quack quack", "。", TODO placeholders, zero collected tests) and legitimate work (research briefs, draft documents, passing test runs, code, translations). 8 scenarios in the first round, expanded to 30 in the second.
When the reviewer is sharp enough to catch all the garbage, it lands at 0% false positives and 75% false negatives — three out of four valid outputs rejected. This is the wall. GLM-5.2 and deepseek-v4-flash both hit it. Smaller models (qwen3:0.5b at ~25% FN, gemma3:4.3b at ~50% FN) sit earlier on the curve — letting some garbage through, rejecting less valid work. They're not better; they're just at a different operating point on the same curve.
I tried three standard moves to shift off the wall.
