# GEO content scores barely predict AI citations, study finds

Published: 2026-09-09T03:05:18.795Z · Source: arXiv GEO (https://arxiv.org/abs/2609.07559v1)
Source date: 2026-09-07

A new arXiv preprint validates a deterministic content score built as a cheap proxy for AI-citation likelihood, using adversarial falsification tests — negative control, dose-response, bounded amplification — rather than assuming the proxy holds. Re-testing the field's original 2023 citation-lever effect sizes on ten modern AI engine families found none of the levers moved citations, and recalibrating to current data strips the score of its lever-responsive components entirely. On a 500-source adversarial benchmark, amplifying the surviving levers gained an attacker at most 6 points, and the score's own citation-predictive power was weak — a within-query Spearman correlation of 0.11 — positioning it as a content-quality filter rather than a citation predictor, per the authors.

Why it matters: It's evidence that the citation levers the GEO industry's early tooling relied on have expired on today's engines, and that content scores marketed as citation predictors are, at best, quality filters.

## What this answers

**Do GEO content-scoring tools actually predict which content AI engines cite?**

Not well, per a new study — a deterministic content score's within-query correlation with actual citations was just 0.11, positioning it as a content-quality filter rather than a citation predictor.

**Do the 'GEO lever' techniques identified in 2023 studies still boost AI citations?**

No — re-testing the original 2023 effect sizes on ten modern AI engine families found none of the levers moved citations, per the study.


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