Year: 2026
Type: conference-paper
Abstract: 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.
Institution: University of Science and Technology of China
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G4804376479
OpenAlex ID: W7166820696