MIRT: Transformers for Truthful Generative Auctions with Whole-feed Permutation Externalities
Modern online platforms commonly rank ads and organic content separately before blending them into a feed displayed to the user, overlooking externalities: an item's click-through rate depends on its surrounding content, not only on its own position. Recent learning-based feed generation mechanisms model some of these cross-type interactions to globally optimize for the whole feed's welfare. However, these approaches either fix the ordering of organic content, or lack exact strategyproofness guarantees for bidders. To combat these shortfalls, we introduce the Maximal-in-Range Transformer (MIRT) mechanism class, which uses a transformer to generate a range of candidate feeds that jointly o...
| Authors | Ali Elahi, Ermis Soumalias, Jason Cheuk Nam Liang et al. |
|---|---|
| Primary category | cs.GT |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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