From Experience to Skill: Multi-Agent Generative Engine Optimization via Reusable Strategy Learning explores A multi-agent framework that learns and reuses optimization strategies for generative engines to improve answer quality and citation accuracy.. Commercial viability score: 7/10 in Generative AI Optimization.
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Canonical route: /paper/from-experience-to-skill-multi-agent-generative-engine-optimization-via-reusable-strategy-learning
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Agent Handoff
Canonical ID from-experience-to-skill-multi-agent-generative-engine-optimization-via-reusable-strategy-learning | Route /paper/from-experience-to-skill-multi-agent-generative-engine-optimization-via-reusable-strategy-learning
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/paper/from-experience-to-skill-multi-agent-generative-engine-optimization-via-reusable-strategy-learningMCP example
{
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"arguments": {
"arxiv_id": "2604.19516"
}
}source_context
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}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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Dimensions overall score 7.0
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