Google DeepMind uses bidding auctions to select models, achieving 99.2% of the optimal result
A new Google DeepMind paper proposes having each model bid on every question and letting the highest bidder answer; simulating auctions across 110 models and over 30,000 EmbedLLM prompts, it reached 99.2% of the best possible value, whereas routing to the best model by prompt type achieved 89.4%. On the prompt clusters where each method performed worst, the auction maintained 96.0%, while a single best model achieved only 6.0%. The paper demonstrates that losses shrink when multiple agents generally place high valuations on prompts.
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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.