Google DeepMind 论文：让模型竞价拍卖选出最优回答
Google DeepMind 论文：模型竞价拍卖选最优回答
Google DeepMind 新论文提出拍卖机制：各模型为每个问题“出价”，最高出价者作答，在 110 个模型、超 30,000 条 EmbedLLM 提示词上模拟达到最优模型选择的 99.2%，而按提示词类型路由到最佳模型仅 89.4%。在各方法表现最差的提示词簇上，拍卖机制保持 96.0%，单一最佳模型仅 6.0%。论文证明当多个智能体普遍对某提示词估值较高时损失会缩小。
Rohan Paul· @rohanpaul_ai · X·2026-10-23 02:00· 1 小时前AI 评分45
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Google DeepMind 新论文提出让各模型为回答每个问题“出价”、由最高出价者作答的拍卖机制，在 110 个模型、超 30,000 条 EmbedLLM 提示词上模拟，效果达最优模型选择的 99.2%，而按提示词类型路由到最佳模型仅 89.4%。
正文 · 原文
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.
来源：Rohan Paul · x.com
来源：RadarAI · aihot.news