Stronger AI models cut false positives but over-reject valid work, study finds

A developer tested three AI models as quality inspectors in an agent pipeline using eight scenarios — four valid outputs and four garbage outputs. Smaller models like Qwen3:0.5b and Gemma3 reduced false positives from 50% to 25% but still failed to catch semantically misleading garbage and wrongly rejected half of all valid outputs. The largest model tested, GLM-5.2, eliminated false positives entirely by correctly interpreting output meaning, yet rejected three out of four legitimate results as insufficient. The findings reveal a classic precision-recall tradeoff: weaker models are too permissive while stronger models are too strict. The author concludes that simply scaling up the inspector model does not resolve the underlying quality-detection problem in AI agent pipelines.