斯坦福团队用 Jev 搭 24/7 情报流水线，成本降 444 倍
把「要不要读、归哪个栏目、重排、核验、去重、写笔记、判定、是否推送」都做成 Jev 类型化决策，LLM 只写最终笔记，千次判定 0.044 美元。
THIS IS PURE F**KING TREASURE. A Stanford AI research group has reportedly found a way to run LLM workflows 24/7 at up to 444X LOWER COST using JEV. The idea is simple: THE LLM ONLY DOES WHAT NEEDS WORDS. EVERYTHING ELSE IS A TYPED DECISION. Here’s how the system works: → Worth reading? JEV answers yes or no before a single token is spent. → Which desk? Papers, repos, or markets. One decision. → Rerank: Embeddings pull 20 sources. JEV keeps the 5 that matter. → Claim holds? Every claim gets checked against its source. Weak ones get dropped. → New or known? Duplicate, related, revises, or contradicts. Everything gets checked against the vault. → The LLM writes: One note. 5–12 lines. One claim. → Judge: Confident verdicts pass. Uncertain ones get escalated to a frontier model. → Matters to you? Only the highest-scoring items make it into the morning brief. The judge rule comes from Carnegie Mellon’s JEV-as-a-Judge paper. The reported results: 34% of cases escalated, 99.6% accuracy retained, and only 47% of the fee. And the cost difference is the real eye-opener: → JEV: $0.044 per 1,000 judgments → GPT-6: $12.182 for the same 1,000 judgments A full day of decisions on a frontier model can cost dollars. The same workload on JEV can cost cents. Instead of reading 300 sources every morning, you get one concise brief with the information that matters. LET THE AI FILTER THE NOISE. YOU READ THE SIGNAL.
这条是引用着另一条写的：x.com/he1s_sammy/status/2104828310819618979