Across 252,000 paired trials on six LLMs, topical relevance and list position emerged as the strongest drivers of first citation in competitive RAG, with price information and recency providing consistent secondary gains.
Investment in Human Capital: A Theoretical Analysis
8 Pith papers cite this work. Polarity classification is still indexing.
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MAGEO is a multi-agent system that distills validated editing patterns into reusable optimization skills for generative engines, outperforming heuristic baselines on visibility and fidelity via a new benchmark and evaluation protocol.
FeatGEO optimizes interpretable webpage features for higher citation rates in generative answer engines while preserving content quality and outperforms token-level rewriting baselines on GEO-Bench.
A natural experiment on glasp.co estimates that a bundle of AEO interventions produced a 1.82x level shift in ChatGPT referrals (95% CI 1.31-2.54) after subtracting platform growth, though a placebo test leaves the result suggestive.
Google AI Overviews activate on 13.7% of queries overall and 64.7% of questions, cite more credible sources than standard results but omit key information in 11% of claims, and suppress clicks on over half of cited pages that carry ads.
Derives an approximate formula for the precision of top-q selections made by a panel of n AIs with average correlation ρ.
A hypernetwork produces a condition-dependent beta that meta-gates SwiGLU nonlinearity, giving LLMs adaptive behavior across task, domain, persona and style inputs without finetuning.
G-Defense builds claim-centered graphs from sub-claims, applies RAG for evidence and competing explanations, then uses graph inference to detect fake news veracity and generate intuitive explanation graphs, claiming SOTA results.
citing papers explorer
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What Gets Cited: Competitive GEO in AI Answer Engines
Across 252,000 paired trials on six LLMs, topical relevance and list position emerged as the strongest drivers of first citation in competitive RAG, with price information and recency providing consistent secondary gains.
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From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning
MAGEO is a multi-agent system that distills validated editing patterns into reusable optimization skills for generative engines, outperforming heuristic baselines on visibility and fidelity via a new benchmark and evaluation protocol.