大模型问答引擎催生新风险，需加强透明度与监管
Position: Generative Engine Optimization Creates Underexamined Risks, Governance Must Target Concentration, Disclosure, and Academic Blind Spots
- 提出生成式引擎优化（GEO）通用流程，定位优化环节
- 发现三大风险：权力集中、商业影响隐藏、学术研究盲区
- 建议答案级治理，强调披露与部署环境评估
大型语言模型（LLM）问答引擎正取代传统搜索结果排名，推动生成式引擎优化（GEO）兴起，其针对证据池与生成过程进行优化。本文分析从搜索引擎优化（SEO）到GEO的转变，识别出两类风险：(i) 由于可竞争性低和系统敏感性高导致的影响力集中；(ii) 隐蔽嵌入证据与推理中的商业影响。进一步构建通用GEO流程，比较学术与产业实践，揭示第三类风险：(iii) 因离线设置与实际部署间可见性与评估不对称造成的学术-产业盲区。论文主张应建立答案层级的治理与度量机制，包括提升可竞争性、精准披露、对关键影响的黑箱审计，以及匹配部署场景的暴露持续性指标。
原文摘要 · Abstract (English)
Large language model (LLM) answer engines are increasingly used for information seeking, shifting visibility from ranked lists to synthesized answers. This enables Generative Engine Optimization (GEO), which targets LLM answer engines' evidence pool and generation. We analyze the search engine optimization (SEO) to GEO transition to identify two risks: (i) concentrated influence from low contestability and system sensitivity, and (ii) undisclosed commercial influence embedded in evidence and reasoning. We then formalize a general GEO pipeline to locate where optimization acts and compare academic and industry practices, revealing a third risk: (iii) academic-industry blind spots driven by visibility and evaluation asymmetries between offline setups and deployed systems. This position argues the need for answer-level governance and measurement: stronger contestability, high-precision disclosure, black-box auditing of material influence, and deployment-aligned metrics for exposure persistence.
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