AI 中文总结
该研究对2023年11月至2026年有关生成引擎优化的45项研究进行批判性综述，指出其术语等存在异质性。贡献多阶段形式模型等，表明虽有成果但证据有限，未显示技术对有机可发现性等有稳定因果效应。
AI 中文摘要
生成引擎优化（GEO）旨在提高生成引擎所产生答案中内容的呈现度、被引用的可能性或影响力。自基础的GEO论文发表以来，该领域迅速扩展，但术语、指标和证据标准仍存在异质性。本批判性综述回顾了在2023年11月至2026年7月发表窗口下选择的45项研究，包括窗口开放后在EMNLP发表的一篇早期预印本，以及相关的RAG和评估工作。我们认为GEO不是单一的排名任务，而是一个随机的、部分可观察的管道，涵盖搜索激活、爬取和索引、检索、重排和上下文分配、引用、突出性、事实吸收、保真度和用户行为。基础论文中被广泛引用的成果在其实验设置内有效，但依赖于固定上下文中已存在的来源；它们既未确立有机可发现性，也未确立持久的流量影响。综述工作表明，主题相关性和上下文位置是最具可重复性的杠杆，通用启发式方法转移效果不佳，竞争会削弱个体收益，以引用为导向的改写会损害检索。商业审计进一步揭示了低源重叠、大量运行间变异性和持续的保真度差距。我们贡献了一个多阶段形式模型、一个将可发现性、引用、吸收和经济结果分开的可见性向量、一个证据层次结构，以及一个基于重复测量、释义、控制、人工验证和多主体干扰的可重复协议。在这个语料库中，证据有限：已检索的内容可以因果性地改变其引用或使用，但没有经过审查的技术对有机可发现性或下游行为显示出稳定的、纵向的、跨平台的因果效应。
英文摘要
Generative Engine Optimization (GEO) seeks to increase content's presence, likelihood of citation, or influence in answers produced by generative engines. Since the foundational GEO paper, the field has expanded rapidly, but terminology, metrics, and evidence standards remain heterogeneous. This critical survey reviews 45 studies selected under a November 2023-July 2026 publication window, including one earlier preprint published at EMNLP after the window opened, plus relevant RAG and evaluation work. We argue that GEO is not a single ranking task but a stochastic, partially observable pipeline spanning search activation, crawling and indexing, retrieval, reranking and context allocation, citation, prominence, factual absorption, fidelity, and user behavior. The foundational paper's widely cited gains are valid within its experimental setting but conditional on a source already being present in a fixed context; they establish neither organic discoverability nor durable traffic effects. Reviewed work indicates that topical relevance and context position are the most reproducible levers, generic heuristics transfer poorly, competition can erode individual gains, and citation-oriented rewrites can impair retrieval. Commercial audits further reveal low source overlap, substantial run-to-run variability, and persistent fidelity gaps. We contribute a multistage formal model, a visibility vector separating discoverability, citation, absorption, and economic outcomes, an evidence hierarchy, and a reproducible protocol based on repeated measurements, paraphrases, controls, human validation, and multi-actor interference. Within this corpus, the evidence is narrow: already-retrieved content can causally alter its citation or use, but no reviewed technique shows a stable, longitudinal, cross-platform causal effect on organic discoverability or downstream behavior.
Comments18 pages, 8 tables, 1 figure; critical survey of 45 studies; ancillary literature matrix and search protocol included
