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

2026-08-11

AI Summary

This study addresses the challenge in generative engines where content providers excessively optimize for citations, leading to citation competition, degraded output quality, and factual inaccuracies—making it difficult for platforms to simultaneously ensure answer quality and reliable attribution. To model this interaction, the work introduces the first formulation as a repeated Stackelberg game with partial monitoring and proposes a Verifiable Content Reward (VCR) mechanism. VCR aligns creator incentives with platform objectives by rewarding verifiable factual content and penalizing suspicious rewrites. Experimental results demonstrate that VCR outperforms the strongest baseline by an average of 12.1 percentage points across three benchmarks, achieving the highest net defensive utility while satisfying a win-win equilibrium under empirical equivalence.

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.

Problem

• generative engines
• citation wars
• incentive alignment
• content quality
• attribution trustworthiness

Innovation

• Mechanism Design
• Generative Engines
• Verifiable-Content Rewards
• Stackelberg Game
• Citation Wars