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.
Investment in Human Capital: A Theoretical Analysis
6 Pith papers cite this work. Polarity classification is still indexing.
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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.
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.
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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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.
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Think Before Writing: Feature-Level Multi-Objective Optimization for Generative Citation Visibility
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.
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Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact
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.
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Quantifying how AI Panels improve precision
Derives an approximate formula for the precision of top-q selections made by a panel of n AIs with average correlation ρ.
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A Graph-Enhanced Defense Framework for Explainable Fake News Detection with LLM
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.
- Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM