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        <a href="/">← Audit Tool</a>
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    <h1>Research Foundation</h1>
    <p class="subtitle">Built on peer-reviewed science, not marketing claims.</p>

    <div class="paper">
        <h2>GEO: Generative Engine Optimization</h2>
        <div class="venue">KDD 2024 — Princeton, Georgia Tech, AI2, IIT Delhi</div>
        <div class="finding">
            Tested 9 optimization strategies across 10,000 queries on GEO-bench. Validated on Perplexity.ai with 22-37% real-world improvements. The foundational controlled experiment for GEO.
        </div>
        <div class="impact">
            <strong>Key finding:</strong> Adding citations to content increases AI visibility by up to +115%. Statistics add +40%. Keyword stuffing has ~0% effect.
        </div>
        <div class="method-grid">
            <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 href="https://arxiv.org/abs/2311.09735">Read paper →</a>
    </div>

    <div class="paper">
        <h2>AutoGEO: Automatic Generative Engine Optimization</h2>
        <div class="venue">ICLR 2026 — Carnegie Mellon University</div>
        <div class="finding">
            Uses frontier LLMs to automatically discover optimization rules, then trains compact models via reinforcement learning. Achieved +50.99% improvement over best Princeton baseline. Introduces GEU (Generative Engine Utility) metric.
        </div>
        <div class="impact">
            <strong>How we use it:</strong> Informs our scoring weight updates and content analysis heuristics. Answer-first structure and passage density checks are based on AutoGEO findings.
        </div>
        <a href="https://arxiv.org/abs/2510.11438">Read paper →</a>
    </div>

    <div class="paper">
        <h2>C-SEO Bench: Conversational SEO Methods</h2>
        <div class="venue">2025 — Puerto et al.</div>
        <div class="finding">
            Found that most conversational SEO methods are largely ineffective and frequently have negative impact. As adoption increases, gains decrease — revealing a zero-sum competitive dynamic.
        </div>
        <div class="impact">
            <strong>How we use it:</strong> Validates our focus on technical infrastructure optimization over content manipulation. We build white-hat GEO tools.
        </div>
        <a href="https://arxiv.org/abs/2506.11097">Read paper →</a>
    </div>

    <div class="paper">
        <h2>Schema Markup & AI Citations</h2>
        <div class="venue">Growth Marshal, February 2026 — Study of 730 pages</div>
        <div class="finding">
            Attribute-rich schema markup earns 61.7% citation rate vs 41.6% for generic schema. Generic schema actually underperforms having no schema at all.
        </div>
        <div class="impact">
            <strong>How we use it:</strong> Our schema richness check differentiates between rich and generic schema. Generic schema gets zero bonus points.
        </div>
    </div>

    <div class="paper">
        <h2>AI Citations Report 2026</h2>
        <div class="venue">OtterlyAI — 1M+ citations analyzed</div>
        <div class="finding">
            73% of sites have technical barriers blocking AI crawlers. Reference-grade content receives 3-5x more citations. Reddit is the #1 cited domain across all AI platforms.
        </div>
        <div class="impact">
            <strong>How we use it:</strong> Our robots.txt, CDN, and JS rendering checks address the 73% barrier problem. Content quality analysis targets reference-grade optimization.
        </div>
    </div>

    <div class="paper">
        <h2>AI Mode Citation Factors</h2>
        <div class="venue">SE Ranking, 2025 — 2.3M pages analyzed</div>
        <div class="finding">
            Domain traffic is the #1 predictor (3x), followed by referring domains (2.5x). Flesch-Kincaid Grade 6-8 is the readability sweet spot. Sections of 100-150 words between headings are optimal. FAQ schema markup has zero impact — only real FAQ content in the body matters.
        </div>
        <div class="impact">
            <strong>How we use it:</strong> Informs our readability, passage density, FAQ-in-content, and content structure checks.
        </div>
    </div>

    <div class="disclaimer">
        GEO Optimizer focuses on <strong>infrastructure optimization</strong> (robots.txt, llms.txt, schema, meta tags, content structure) — not content manipulation. The adversarial research (ETH Zurich, Harvard, UC Berkeley) shows that manipulative techniques degrade the ecosystem. We build tools for white-hat GEO.
    </div>

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        <p><a href="https://github.com/auriti-labs/geo-optimizer-skill">GitHub</a> · <a href="/docs">API Docs</a> · <a href="https://pypi.org/project/geo-optimizer-skill/">PyPI</a></p>
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