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Opening the Black Box of Neural Auction Mechanisms — arXiv:2611.01981

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01981
License (as archived): CC0-1.0
Archived: 2026-11-03T10:31:59.013Z
SHA-256 of the archived text: dc96b48bc11e9df23f871f395233d548eb9692ebe2a9546f4aba6d197b6cb908
Stable URL of this page: https://metraipolis.com/documento/dc_fc75fc0f790e

How to cite

Opening the Black Box of Neural Auction Mechanisms — arXiv:2611.01981 — arXiv (en), https://arxiv.org/abs/2611.01981 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fc75fc0f790e (Metraipolis archive dc_fc75fc0f790e, sha256 dc96b48bc11e9df23f871f395233d548eb9692ebe2a9546f4aba6d197b6cb908)

BibTeX

@misc{dcfc75fc0f790e,
  title = {Opening the Black Box of Neural Auction Mechanisms — arXiv:2611.01981},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01981},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fc75fc0f790e; sha256 dc96b48bc11e9df23f871f395233d548eb9692ebe2a9546f4aba6d197b6cb908},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01981. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Neural auction design has emerged as a powerful paradigm within automated mechanism design for modern Web platforms. Because these learned mechanisms directly dictate critical economic outcomes, their lack of internal transparency raises significant auditability and trust concerns. Opening this black box \textit{post-hoc} is therefore essential, yet presents two distinctive challenges: (1) activation contexts are dense, correlated numerical profiles devoid of explicit semantic structure, and (2) feature interpretations can only be falsifiably audited if they map to measurable auction states or outcomes. In this paper, we propose a post-hoc white-box auditing framework for unpacking trained neural auctions. Using CITransNet as a representative testbed, we first employ sparse autoencoders to decompose hidden representations while faithfully preserving downstream auction behavior. Anchoring feature interpretations to native economic targets, we validate them through activation prediction and causal interventions to isolate verified features from the reconstruction dictionary. Finally, applying this pipeline across varied auction settings, we show that the verified features can serve as analytical instruments to examine how internal representations modulate strategic misreporting incentives, align with Myerson's optimal auction, and support the diagnosis of individual auctions. Our work highlights the potential of white-box mechanism auditing to build the foundational trust necessary for neural auctions to be deployed as widely adopted Web technologies. To enhance research transparency and facilitate independent inspection, we publicly release the experimental results supporting our findings at this https URL

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.01981 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:29:48.532Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).