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

Chen Xu; Zitian Guo; Chenyan Xiong

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arXiv · 2608.11390

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

Chen Xu·Zitian Guo·Chenyan Xiong

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Abstract

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.

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BibTeX RIS

Chen Xu, Zitian Guo, Chenyan Xiong. 2026-08-11. Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes. https://arxiv.org/abs/2608.11390

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