As Generative Engines revolutionize information retrieval by synthesizing direct answers from retrieved sources, ensuring source visibility becomes a significant challenge. Improving it through targeted content revisions is a practical strategy termed Generative Engine Optimization (GEO). However, optimizing a document for diverse queries presents a constrained optimization challenge where heterogeneous queries often impose conflicting and competing revision requirements under a limited content budget. To address this challenge, we propose IF-GEO, a "diverge-then-converge" framework comprising two phases: (i) mining distinct optimization preferences from representative latent queries; (ii) synthesizing a Global Revision Blueprint for guided editing by coordinating preferences via conflict-aware instruction fusion. To explicitly quantify IF-GEO's objective of cross-query stability, we introduce risk-aware stability metrics. Experiments on multi-query benchmarks demonstrate that IF-GEO achieves substantial performance gains while maintaining robustness across diverse retrieval scenarios.

翻译：随着生成式引擎通过综合检索来源生成直接答案而革新信息检索领域，确保来源可见性已成为一项重大挑战。通过针对性内容修订来提升可见性是一种实用策略，称为生成式引擎优化。然而，为多样化查询优化文档构成了一个受约束的优化难题：异质查询通常在有限内容预算下提出相互冲突且竞争的修订要求。为解决此问题，我们提出IF-GEO——一个“发散-收敛”双阶段框架，包含：（i）从代表性潜在查询中挖掘差异化优化偏好；（ii）通过冲突感知指令融合协调偏好，合成用于指导编辑的全局修订蓝图。为显式量化IF-GEO的跨查询稳定性目标，我们引入风险感知稳定性度量指标。在多查询基准测试上的实验表明，IF-GEO在保持多样化检索场景鲁棒性的同时，实现了显著的性能提升。