Large-scale analysis of AI engine responses shows brand visibility in three tiers (73%, 44%, 11%) with corporate sites and best-of listicles as top cited sources.
GEO: Generative engine optimization
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 9representative citing papers
A large-scale audit of AI commercial recommendations reveals tier-specific failure modes: L1 brands reach recommendations but convert at 25-41%, L2 convert highest at 37-52%, L3 is an inflection point, and L4/L5 brands suffer 48-52% complete invisibility.
Persona prefixes reduce brand recommendation Jaccard similarity by 0.12-0.20, with mid-market brands swapping up to 75% of recommendations while category leaders remain ~80% consistent across OpenAI and Anthropic models.
Paraphrase Jaccard similarity of 0.135-0.288 falls below the 0.50-0.61 same-prompt rerun baseline on OpenAI and Anthropic models, showing prompt wording dominates buyer intent in commercial recommendations.
A measurement study of 602 prompts across ChatGPT, Google AI Overview, and Perplexity finds that citation selection breadth and absorption depth diverge, with high-influence pages being longer, structured, and evidence-rich.
AI coding agents and assistants fetch documentation in one or two HTTP requests with identifiable fingerprints, undermining standard web analytics.
LLMs cite third-party domains for 85.7% of brand attributions, with Wikipedia dominant in most languages, a long-tailed domain distribution, and market-specific shifts such as YouTube and HR sites in Poland.
Two major AI providers diverge in which brands they recommend but converge on classifying the failure reasons, especially for low-prominence brands.
Deterministic multi-agent intent routing can reduce hallucinations in generative engines to near zero by limiting LLMs to intent routers and handing off tasks to specialized agents.
citing papers explorer
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Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
Large-scale analysis of AI engine responses shows brand visibility in three tiers (73%, 44%, 11%) with corporate sites and best-of listicles as top cited sources.
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Prominence-Stratified Failure Modes in Retrieval-Augmented Commercial Recommendation: A 37,000-Run Audit
A large-scale audit of AI commercial recommendations reveals tier-specific failure modes: L1 brands reach recommendations but convert at 25-41%, L2 convert highest at 37-52%, L3 is an inflection point, and L4/L5 brands suffer 48-52% complete invisibility.
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Persona Conditioning of Brand Recommendations in Retrieval-Augmented Commercial Chat: A Prominence-Stratified Cross-Provider Audit
Persona prefixes reduce brand recommendation Jaccard similarity by 0.12-0.20, with mid-market brands swapping up to 75% of recommendations while category leaders remain ~80% consistent across OpenAI and Anthropic models.
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Paraphrase Brittleness in Production Retrieval-Augmented Commercial Recommendation: Reproducibility Below the Rerun-Stability Baseline
Paraphrase Jaccard similarity of 0.135-0.288 falls below the 0.50-0.61 same-prompt rerun baseline on OpenAI and Anthropic models, showing prompt wording dominates buyer intent in commercial recommendations.
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From Citation Selection to Citation Absorption: A Measurement Framework for Generative Engine Optimization Across AI Search Platforms
A measurement study of 602 prompts across ChatGPT, Google AI Overview, and Perplexity finds that citation selection breadth and absorption depth diverge, with high-influence pages being longer, structured, and evidence-rich.
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Developer Experience with AI Coding Agents: HTTP Behavioral Signatures in Documentation Portals
AI coding agents and assistants fetch documentation in one or two HTTP requests with identifiable fingerprints, undermining standard web analytics.
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How Large Language Models Source Brand Reputation Across Languages and Markets
LLMs cite third-party domains for 85.7% of brand attributions, with Wikipedia dominant in most languages, a long-tailed domain distribution, and market-specific shifts such as YouTube and HR sites in Poland.
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Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation
Two major AI providers diverge in which brands they recommend but converge on classifying the failure reasons, especially for low-prominence brands.
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Beyond Retrieval: Modeling Confidence Decay and Deterministic Agentic Platforms in Generative Engine Optimization
Deterministic multi-agent intent routing can reduce hallucinations in generative engines to near zero by limiting LLMs to intent routers and handing off tasks to specialized agents.