多伦多大学与 Google 论文：GEO 基准需统一编辑规则
GEO 方法都想让网页被 AI 引用，但各家测试规则不同，分数根本没法比。这篇论文给出统一的打标签框架，做内容优化的人可以照着核对。
多伦多大学与 Google 的一篇论文指出，当前 GEO（生成式引擎优化）基准允许各家使用不同编辑方式，重写整页的方法可能比只追加一行的方法得分更高，结果无法比较。论文提出按改动位置、改动方式和针对的线索（如权威性）对每次 GEO 编辑打标签，测试时固定允许的编辑范围，并同时报告可见性、答案质量与规则遵守情况。论文没有做实验，证据是对 11 个已发表 GEO 方法的映射，显示各自只覆盖编辑空间的不同切面。作者建议对比 GEO 方法时先确认分数允许了哪些编辑、是否测量了答案质量。
New University of Toronto and Google paper argues that picking an AI assistant may be the last big decision people make, since that assistant then makes the decisions that follow.
Argues that tests of how websites game AI answers need shared editing rules, or scores can't be compared.
Content owners now edit web pages so AI assistants cite or recommend them, a practice called generative engine optimization, or GEO. Current GEO benchmarks allow different edits, so a method that rewrites whole pages can look better than one that only appends a line.
They propose labeling every GEO edit by where the page changes, how it changes, and which cue it targets, such as authority. Tests would fix the allowed edits and report visibility, answer quality, and rule-following together.
The paper runs no experiments. Its evidence is a mapping of 11 published GEO methods, showing that each covers a different slice of the possible edits.
When comparing GEO methods or AI systems, check which edits each score allowed and whether answer quality was measured.
