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    <a href="/">Audit</a><a href="/compare">Compare</a><a href="/roadmap">Roadmap</a><a href="/research" class="active">Research</a><a href="/docs/">Docs</a>
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  <div class="hero"><h1>Research Foundation</h1><p>Built on peer-reviewed science, not marketing claims.</p></div>

  <div class="paper">
    <h2>GEO: Generative Engine Optimization</h2>
    <div class="venue">KDD 2024 &mdash; Princeton, Georgia Tech, AI2, IIT Delhi</div>
    <div class="finding">Tested 9 optimization strategies across 10,000 queries on GEO-bench. Validated on real Perplexity.ai with 22-37% improvements.</div>
    <div class="impact"><strong>Key:</strong> Citations increase AI visibility up to +115%. Statistics add +40%. Keyword stuffing has ~0% effect.</div>
    <div class="methods">
      <div class="method">Cite Sources <span class="pct">+27-115%</span></div>
      <div class="method">Quotations <span class="pct">+41%</span></div>
      <div class="method">Statistics <span class="pct">+33%</span></div>
      <div class="method">Fluency <span class="pct">+29%</span></div>
      <div class="method">Technical Terms <span class="pct">+18%</span></div>
      <div class="method">Authority <span class="pct">+16%</span></div>
      <div class="method">Readability <span class="pct">+14%</span></div>
      <div class="method">Unique Words <span class="pct">+7%</span></div>
      <div class="method">Keyword Stuffing <span class="neg">~0%</span></div>
    </div>
    <a class="link" href="https://arxiv.org/abs/2311.09735">Read paper &rarr;</a>
  </div>

  <div class="paper">
    <h2>AutoGEO: Automatic Generative Engine Optimization</h2>
    <div class="venue">ICLR 2026 &mdash; Carnegie Mellon University</div>
    <div class="finding">Automatic rule extraction from generative engines via LLMs + reinforcement learning. +50.99% over Princeton baseline. Introduces GEU (utility) metric.</div>
    <div class="impact"><strong>How we use it:</strong> Informs scoring weight updates. Answer-first structure and passage density checks are based on AutoGEO findings.</div>
    <a class="link" href="https://arxiv.org/abs/2510.11438">Read paper &rarr;</a>
  </div>

  <div class="paper">
    <h2>C-SEO Bench: Conversational SEO Methods</h2>
    <div class="venue">2025 &mdash; Puerto et al.</div>
    <div class="finding">Most conversational SEO methods are ineffective and often negative. Gains decrease as adoption increases &mdash; zero-sum competitive dynamic.</div>
    <div class="impact"><strong>How we use it:</strong> Validates our focus on infrastructure over content manipulation. We build white-hat GEO tools.</div>
    <a class="link" href="https://arxiv.org/abs/2506.11097">Read paper &rarr;</a>
  </div>

  <div class="paper">
    <h2>Schema Markup &amp; AI Citations</h2>
    <div class="venue">Growth Marshal, Feb 2026 &mdash; 730 pages</div>
    <div class="finding">Rich schema (5+ attributes): 61.7% citation rate. Generic schema: 41.6%. Generic actually underperforms no schema.</div>
    <div class="impact"><strong>How we use it:</strong> Schema richness check differentiates rich vs generic. Generic gets zero bonus.</div>
  </div>

  <div class="paper">
    <h2>AI Citations Report 2026</h2>
    <div class="venue">OtterlyAI &mdash; 1M+ citations analyzed</div>
    <div class="finding">73% of sites have technical barriers blocking AI crawlers. Reference-grade content gets 3-5x more citations.</div>
    <div class="impact"><strong>How we use it:</strong> robots.txt, CDN, and JS rendering checks address the 73% barrier problem.</div>
  </div>

  <div class="paper">
    <h2>AI Mode Citation Factors</h2>
    <div class="venue">SE Ranking, 2025 &mdash; 2.3M pages</div>
    <div class="finding">Flesch-Kincaid Grade 6-8 is the sweet spot. Sections of 100-150 words are optimal. FAQ schema has zero impact &mdash; only real FAQ content matters.</div>
    <div class="impact"><strong>How we use it:</strong> Readability, passage density, FAQ-in-content, and section length checks.</div>
  </div>

  <div class="disclaimer">GEO Optimizer focuses on <strong>infrastructure optimization</strong> (robots.txt, llms.txt, schema, meta, content structure) &mdash; not content manipulation. The adversarial research confirms that manipulative techniques degrade the ecosystem.</div>

  <footer><a href="https://github.com/auriti-labs/geo-optimizer-skill">GitHub</a> &middot; <a href="/docs/">Docs</a> &middot; <a href="https://pypi.org/project/geo-optimizer-skill/">PyPI</a></footer>
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