Google DeepMind 论文：用竞价拍卖机制给问题分配最合适的模型
DeepMind 把模型选择做成了拍卖：谁对这道题有信心谁出高价，效果达到最优的99.2%，比传统路由还准，做法挺好抄的。
Google DeepMind 的论文提出让模型对每个问题出价，出价最高者作答。实验覆盖 110 个模型和超过 30,000 条 EmbedLLM 提示。结果显示拍卖方式达到最优选择 99.2% 的效果，而按类型路由到最佳模型只有 89.4%。论文还证明当多个模型对某类问题估值接近时（如蒸馏模型接近最强模型），拍卖的损失会缩小。
New Google DeepMind paper shows that if AI models offer money to answer each question and the highest offer answers, results are 99.2% as good as always picking the best model.
i.e. say you don't know in advance which model is best for each new question. So who should answer?
The paper’s answer: let each model offer money for the job. The model that knows it is good at this question offers more. Highest offer gets to answer.
They proved the loss shrinks when several agents usually value a prompt highly, as when distilled models perform close to the best. They simulated auctions among 110 models on over 30,000 EmbedLLM prompts.
With all 110 models bidding, the auction reached 99.2% of the best possible value, versus 89.4% for routing each prompt type to its best model. On each method's worst prompt cluster, the auction held 96.0% while the single best model kept 6.0%.
If you run a model marketplace, a simple pay-your-bid auction works well when every bidding pool has several strong models.
