生成式引擎的机制设计：从利用到双赢结果

中文摘要 AI 辅助

生成式引擎正通过将引用作为分配注意力、归因及下游价值的关键机制，重塑网络生态系统。这造成了战略张力：内容提供者受激励优化模型引用，而平台必须保持答案质量与可信归因。研究显示，这种张力可能升级为引用战争。在重复模拟中，最先进的生成式引擎优化（GEO）攻击会适应传统防御，生成追求引用的改写内容，降低文档质量并引入无依据的主张。为研究该问题，我们将供给方—平台互动建模为带部分监测的重复斯塔克尔伯格博弈。局部最优反应分析确定了引用竞争趋近于惰性平稳结果的情形。基于此发现，我们提出名为VCR（可验证内容奖励）的平台—创作者机制，平台不仅惩罚可疑改写，还对呈现可核查事实内容的改写予以奖励，使创作者激励与答案可信性对齐。在三个基准上的实验表明，VCR始终实现最大的净防御效用得分，较最强基线平均高出12.1个百分点，且在我们的经验等价准则下产生双赢结果。

英文摘要

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.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）
• University of California, San Diego（加利福尼亚大学圣迭戈分校）

机构由 AI 辅助整理，请以论文原文为准。