Overview
AutoGEO automatically discovers actionable rules to improve content visibility in generative search engines. Unlike traditional methods that rely on expert-designed prompts and trial-and-error, AutoGEO uses large language models to automatically extract preference rules of generative search engines, enabling better adaptability across domains and engines.
Key Contributions
🎯 Automated Rule Discovery
Automatically extracts actionable rules from generative engines, eliminating manual prompt engineering.
⚡ Dual Deployment Options
AutoGEOAPI for plug-and-play integration; AutoGEOMini for cost-efficient fine-tuned models.
📈 Significant Improvements
Up to 50.99% improvement over baselines while maintaining generative engine utility.
🔄 Cross-Engine Transferability
Rules transfer effectively across different LLMs (Gemini, GPT, Claude) and domains.
Introduction
(a) Generative Engine Workflow: The engine retrieves relevant information and uses an LLM to generate the final answer.
(b) GEO Model Workflow: The GEO model rewrites relevant information to improve its visibility in the final output.
(c) Existing GEO Models: Traditional methods rely on expert-designed prompts and trial-and-error, with limited visibility improvements and adaptability.
(d) AutoGEO (Ours): AutoGEO automatically discovers actionable rules to flexibly build efficient GEO models. These rules can be applied directly in a prompt-based approach or used to fine-tune a compact model, enabling better adaptability across domains and engines.
Method
Left Panel - Automated Preference Rule Discovery: Takes conversational interactions from the Generative Engine as input. An LLM analyzes these interactions to identify patterns and preferences that correlate with higher visibility. Outputs a structured Rule Set containing explicit, actionable guidelines for rewriting content.
Right Panel - Rule Guided GEO Models:
- AutoGEOAPI (Plug-and-play): The Rule Set is directly fed as an Instruction into a black-box LLM API (e.g., GPT-4, Claude). Designed for easy integration without complex fine-tuning.
- AutoGEOMini (Cost-efficient): Uses a two-stage process: Cold Start with rule-based prompting, followed by GRPO fine-tuning. Reduces inference costs and latency compared to large LLM APIs.
Experimental Results
🚀 Superior Performance
AutoGEOAPI and AutoGEOMini consistently outperform all baselines across three datasets:
- AutoGEOAPI: Up to 50.99% improvement over the strongest baseline
- AutoGEOMini: Average 20.99% gain over baselines
✅ Key Findings
- Cross-Engine Robustness: Improvements are consistent across Gemini, GPT, and Claude engines
- Utility Preservation: Maintains or slightly improves generative engine utility metrics (Precision, Recall, Clarity, Insight)
- Challenging Scenarios: Substantially improves visibility for low-visibility documents
- Rule Transferability: Rules show high overlap across LLMs (78-84%) and transfer effectively across engines
🛡️ Cooperative vs. Adversarial
AutoGEO achieves strong visibility improvements without harming engine utility, unlike adversarial methods (Hijack Attack, Poisoning Attack) that degrade answer quality.
More details can be found in the 📄 Paper. Try it out in the 🚀 AutoGEOMini Demo.
Resources
📚 Paper & Code & Demo
🤖 AutoGEOMini Checkpoints
Citation
If you find AutoGEO useful, please cite:
@article{wu2025generative,
title={What Generative Search Engines Like and How to Optimize Web Content Cooperatively},
author={Wu, Yujiang and Zhong, Shanshan and Kim, Yubin and Xiong, Chenyan},
journal={arXiv preprint arXiv:2510.11438},
year={2025}
}