{
  "abstract": "Automated mechanism design has enabled the learning of complex auction mechanisms using neural networks. However, designing non-truthful auctions remains challenging because bidders' equilibrium strategies depend on the auction mechanism and thus evolve as the mechanism is optimized. In this work, we formulate auction design as a Stackelberg game with multiple strategic followers and develop a fully first-order algorithm for revenue optimization in non-truthful auctions. We establish non-asymptotic guarantees for hypergradient approximation and show that, under standard smoothness and strong-monotonicity assumptions, our method reaches an $O(ε)$-stationary point using $\\tilde{\\mathcal{O}}(ε^{-2})$ first-order oracle calls. Experiments on symmetric and asymmetric single-item auctions, as well as multi-item auctions, show that our method converges faster than second- and zeroth-order baselines and scales better as the number of bidders increases. Our method also discovers auctions that achieve revenue close to truthful baselines. These results provide a scalable framework for automated mechanism design beyond truthful auctions.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.40403",
  "author_total": 4,
  "authors": [
    {
      "name": "Shing-Hei Ho"
    },
    {
      "name": "April Niu"
    },
    {
      "name": "Ellen Vitercik"
    },
    {
      "name": "Kai Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b0g9gt5sehj3g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.40403",
  "fulltext_url": null,
  "id": "b0g9gt5sehj3g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.40403",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.GT"
    ],
    [
      "arxiv",
      "math.OC"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2610.40403",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/b0g9gt5sehj3g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:36:35Z",
      "native_id": "2610.40403",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.40403"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "First-Order Bilevel Optimization for Non-truthful Auction Design",
  "title_text": "First-Order Bilevel Optimization for Non-truthful Auction Design",
  "type": "preprint",
  "updated_at": "2026-11-01T12:20:53Z",
  "url": "https://research.draconitic.com/paper/b0g9gt5sehj3g",
  "venue": "arXiv",
  "versions": []
}