设计新机制让生成引擎与创作者双赢,避免低质内容竞争。
Mechanism Design for Generative Engines: From Exploitation toward Win-Win Outcomes

- 将平台与创作者关系建模为动态博弈,识别出可预测的劣化循环。
- 新机制VCR奖励可验证事实内容,使防御效果提升12.1个百分点。
- 适合关注生成内容可信度与生态可持续性的研究者和平台方。
生成引擎正通过引用机制重塑网络生态,成为注意力、归属权与下游价值分配的核心方式。这带来策略冲突:内容提供者倾向于优化模型引用,而平台需保障答案质量与可信溯源。我们发现这种冲突可能演变为引用战。在多次模拟中,先进生成引擎优化(GEO)攻击能适应传统防御,产生追求引用但降低文档质量、引入无据主张的改写内容。为此,我们将供应者-平台互动建模为部分观测的重复斯塔克尔伯格博弈。局部最优响应分析揭示了引用竞争趋于僵局的条件。受此启发,我们提出基于可验证内容奖励的平台-创作者机制VCR。该机制不仅惩罚可疑改写,还奖励突出可核查事实内容的改写,使创作者激励与答案可信度对齐。在三个基准测试中,VCR始终获得最高净防御-效用得分,平均优于最强基线12.1个百分点,并在我们的经验等价标准下实现双赢结果。
原文摘要 · Abstract (English)
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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