What the GEO paper measured

# What the GEO paper measured

The first peer-reviewed controlled experiment of which content tactics move AI citations — what worked, what didn't, and what the per-tactic numbers actually say.

By AgentSite · 6 min read · Updated 2026-05-23

The Generative Engine Optimization paper out of Princeton (Aggarwal et al., KDD 2024) is the only peer-reviewed controlled experiment of which content tactics move AI citations. Combined optimization can lift visibility by up to 40%. The per-tactic breakdown is the more useful part — the top three carry most of the lift, one actively hurts, and several do nothing.

## The experiment

The authors built **GEO-bench**, a benchmark of 10,000 queries drawn from MS MARCO, ORCAS-1, Natural Questions, AllSouls, LIMA, [Perplexity.ai](http://Perplexity.ai) Discover, ELI5, GPT-4-generated queries, and Davinci-Debate, spread across 25 domains and 9 query types ([Aggarwal et al., 2024, arXiv:2311.09735](https://arxiv.org/abs/2311.09735); [project page](https://generative-engines.com/GEO/)). Eighty percent of the queries are informational; ten percent each transactional and navigational.

The generative engine was gpt-3.5-turbo, prompted with the top-5 results from Google search as retrieval sources. Each tactic was tested by applying it to one randomly-selected source for a query, then comparing visibility against the unmodified baseline. The paper also validated the strongest tactics on [Perplexity.ai](http://Perplexity.ai), the deployed retrieval-grounded engine, with consistent results. The setup is a reasonable proxy for production AI crawlers — by late 2024, Vercel measured 569 million GPTBot fetches and 370 million Claude fetches in a single month, none of them executing JavaScript ([Vercel, "The Rise of the AI Crawler," Dec 2024](https://vercel.com/blog/the-rise-of-the-ai-crawler)).

Two metrics were used. **Position-Adjusted Word Count (PAWC)** weighted each cited sentence's word share by a decaying exponential of citation position — the headline result number. **Subjective Impression** scored seven dimensions (relevance, influence, uniqueness, diversity, follow-up, position, count) via GPT-3.5 as judge.

## The 9 tactics

Nine optimization methods were evaluated, defined as algorithmic source-text transformations:

1. **Authoritative** — rewrite to sound more persuasive and authoritative.  
2. **Statistics Addition** — add quantitative statistics in place of qualitative discussion.  
3. **Keyword Stuffing** — add more query-relevant keywords (the classical-SEO move).  
4. **Cite Sources** — add named external citations to support claims.  
5. **Quotation Addition** — add quoted material from credible sources.  
6. **Easy-to-Understand** — simplify the language.  
7. **Fluency Optimization** — improve readability of the source text.  
8. **Unique Words** — add domain-rare vocabulary.  
9. **Technical Terms** — add jargon and specialized terminology.

## What worked

The paper reports verbatim that "our top‑performing methods, Cite Sources, Quotation Addition, and Statistics Addition, achieved a relative improvement of 30‑40 % on the Position‑Adjusted Word Count metric and 15‑30 % on the Subjective Impression metric."

Reading off Table 1 of the paper (PAWC Overall, baseline = 19.3):

| Tactic               | PAWC Overall | Δ vs baseline |
|----------------------|--------------|---------------|
| Quotation Addition   | 27.2         | +41 % |
| Statistics Addition  | 25.4         | +32 % |
| Fluency Optimization | 24.7         | +28 % |
| Cite Sources         | 24.6         | +27 % |
| Technical Terms      | 22.7         | +18 % |
| Easy-to-Understand   | 22.0         | +14 % |
| Authoritative        | 21.3         | +10 % |
| Unique Words         | 20.5         | +6 % |
| Keyword Stuffing     | 17.7         | **−8 %** (only tactic below baseline) |
| Baseline (no optimization) | 19.3 | — |

The paper's stated conclusion on Keyword Stuffing: "we find such methods offer little to no improvement on generative engine's responses." Translated more sharply: it was the only tactic in the experiment with a measurably negative effect on visibility. The classical‑SEO move actively hurts in the generative‑engine paradigm.

## The lower‑ranked‑site finding

The most strategically interesting result is in Table 2 of the paper — per‑tactic lift broken down by the source's original Google‑search rank. Lower‑ranked sites benefit dramatically more:

- **Cite Sources, Rank‑5 source: +115 % relative improvement.** A page that was the fifth‑ranked Google result for the query saw more than double its baseline citation visibility after the GEO tactic was applied.  
- **Quotation Addition, Rank‑5: +99.7 %.**  
- **Statistics Addition, Rank‑5: +97.7 %.**

For Rank‑1 sources, the same tactics show much smaller lifts (and Cite Sources actually showed −30 % at Rank‑1, suggesting top‑ranked pages already saturate their citation potential). The pattern: GEO is _democratizing_. It moves lower‑authority pages up disproportionately, while the top‑ranked pages have less room to grow.

## Domain‑specific patterns

Different tactics work in different content categories (Table 3 of the paper):

- **Cite Sources** wins in Statement, Facts, and Law & Government queries.  
- **Statistics Addition** wins in Law & Government, Debate, and Opinion.  
- **Quotation Addition** wins in People & Society, Explanation, and History.  
- **Authoritative** wins in Debate, History, and Science.

The high‑level read is that fact‑heavy domains reward citation density (Statistics, Cite Sources), discussion‑heavy domains reward voice (Quotation, Authoritative).

## Combinations

The paper's §5.3 reports that combining tactics outperforms any single tactic by more than 5.5 %. The strongest pair is **Fluency Optimization + Statistics Addition** at roughly 35.8 % improvement. Several pairs reach the 30‑35 % range; few exceed it. There is diminishing return past two‑tactic stacks in their data.

## Where this fits

For the AEO corpus, the paper is the load‑bearing source behind three Layer‑4 glossary entries:

- [Statistics and citations](/statistics-citations) — the _Statistics Addition_ + _Cite Sources_ tactics combined into one editorial pattern.  
- [Direct answer](/direct-answer) — the lede‑paragraph extraction unit that benefits from all three top tactics applied together.  
- [Definition density](/definition-density) — the term‑level pattern that compounds with the source‑citation pattern Cite Sources measures.

The longer thesis on why these tactics matter at all — citation is binary, agents don't run JavaScript, the chain dependency of the five layers — is in [agent readability](/agent-readability). The structural map sits in [the five layers of AEO](/five-layer-aeo).

The paper's last word — "Generative Engines value not only content but also information presentation" — is the editorial line every Layer 4 piece in this corpus tries to honor.