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Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes — arXiv:2608.11390

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11390
License (as archived): CC0-1.0
Archived: 2026-10-19T05:39:43.962Z
SHA-256 of the archived text: 044acea1e4def95eab85c1f2635464d345fab784aa2e960721b532ac1a16b8dc
Stable URL of this page: https://metraipolis.com/documento/dc_a5efd7db6972

How to cite

Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes — arXiv:2608.11390 — arXiv (en), https://arxiv.org/abs/2608.11390 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a5efd7db6972 (Metraipolis archive dc_a5efd7db6972, sha256 044acea1e4def95eab85c1f2635464d345fab784aa2e960721b532ac1a16b8dc)

BibTeX

@misc{dca5efd7db6972,
  title = {Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes — arXiv:2608.11390},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11390},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a5efd7db6972; sha256 044acea1e4def95eab85c1f2635464d345fab784aa2e960721b532ac1a16b8dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11390. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Generative engines are reshaping the web ecosystem by making citations a key mechanism for allocating attention, attribution, and downstream value. This creates a strategic tension: content providers are incentivized to optimize for model citation, while platforms must preserve answer quality and trustworthy attribution. We show that this tension can escalate into citation wars. In repeated simulations, state-of-the-art generative engine optimization (GEO) attacks adapt to conventional defenses by producing citation-seeking rewrites that degrade document quality and introduce unsupported claims. To study this problem, we formulate the supplier--platform interaction as a repeated Stackelberg game with partial monitoring. A local best-response analysis identifies when citation competition approaches an inert stationary outcome. Motivated by this finding, we propose a platform--creator mechanism called VCR based on verifiable-content rewards. Rather than only penalizing suspicious rewrites, the platform also credits rewrites that surface checkable factual substance, aligning creator incentives with answer trustworthiness. Experiments on three benchmarks show that VCR consistently achieves the largest Net defense-utility score, outperforming the strongest baseline by an average of 12.1 percentage points, and produces a win--win outcome under our empirical equivalence criterion.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-13 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2608.11390 — 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 2026-10-19T05:18:32.461Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).