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generative-engine-optimization

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First comprehensive benchmark for Generative Engine Marketing (GEM), an emerging field that focuses on monetizing generative AI by seamlessly integrating advertisements into Large Language Model (LLM) responses. Our work addresses the core problem of ad-injected response (AIR) generation and provides a framework for its evaluation.

  • Updated Nov 18, 2025
  • Python

To produce a rigorous, primary-source analytical paper that documents the gap between llms.txt’s design intent (inference-time content discovery), the infrastructure reality (WAF/CDN blocking), and actual AI system behavior (no confirmed inference-time usage).

  • Updated Feb 15, 2026
  • TeX

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