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New CORE method optimizes LLM search rankings for product discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method called CORE to influence the product rankings generated by large language models in search applications. This technique aims to address the issue where LLM-based search can disadvantage smaller businesses by manipulating the content retrieved by search engines. CORE appends optimized content, such as strings, reasoning, or reviews, to steer the LLM's output rankings. Tested on GPT-4o, Gemini 2.5, Claude 4, and Grok-3, CORE demonstrated high success rates in promoting products within the top rankings. AI

IMPACT This research could improve visibility for smaller businesses in LLM-driven e-commerce by optimizing search result rankings.

RANK_REASON The cluster contains a research paper detailing a new method for LLM-based search optimization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New CORE method optimizes LLM search rankings for product discovery

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The cluster contains a research paper detailing a new method for LLM-based search optimization. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Haibo Jin, Huimin Zeng, Yifeng Luo, Haohan Wang ·

    Controlling Output Rankings in LLM-Based Search via Generative Engine Optimization

    arXiv:2602.03608v2 Announce Type: replace Abstract: The way customers search for and choose products is changing with the rise of large language models (LLMs). LLM-based search, or generative engines, provides direct product recommendations to users, rather than traditional onlin…

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