This paper finds that a cheap verifier can match a frontier LLM's accuracy at checking RAG claims, while letting more unsupported claims slip through.

RAG pipelines keep asking whether evidence supports a claim, a simple yes or no. Most teams get that answer from a text-generating LLM at generation prices.

They tested Jev, which returns only a label and a probability, against GPT-6 Astra on 495 human-labelled claims. They also built a cascade where Jev answers confident claims and passes the rest to Astra.

Jev landed within 0.6 points of Astra's accuracy at 187 times lower cost, but it approved 23.2% of unsupported claims against Astra's 14.1%. The cascade also cut Astra's bill, yet sending the same share of claims to Astra at random saved about as much.

Before picking a claim checker for a RAG product, measure how often it approves unsupported claims, since headline accuracy hides that.