The paper introduces the Latent Advertiser Mixture Auction (LAMA), a token‑level advertising framework that integrates advertiser influence directly into the text generation process. Advertisers provide local continuation values that shape next‑token policies, and the platform decodes these through a latent mixture while updating an allocation posterior. LAMA is shown to satisfy Markov DSIC and IR, achieve near‑optimal KL‑regularized welfare, and, in proof‑of‑concept experiments on commercial‑search queries, improve platform welfare and revenue without compromising user‑facing response quality.
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