Jev 做 RAG 声明核查：成本低 187 倍，误放率更高
论文在 495 条人工标注声明上对比 Jev 与 GPT-6 Astra：Jev 准确率仅差 0.6 分、成本低 187 倍，但放行了 23.2% 无依据声明（Astra 14.1%），级联方案省下的钱与随机分流相当。
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.