arXiv:2311.09735 Published at KDD 2024
GEO: Generative Engine Optimization
Pranjal Aggarwal · Vishvak Murahari · Tanmay Rajpurohit · Ashwin Kalyan · Karthik Narasimhan · Ameet Deshpande · Academic research (Princeton and others)
The authors concluded you can. This paper introduced the term GEO (Generative Engine Optimization) and defined it as a black-box optimization framework for raising content visibility in generative engine responses. On their own benchmark, GEO-bench, they reported visibility gains of up to 40%.
arXiv:2602.12187 Accepted at KDD 2026
SAGEO Arena: A Realistic Environment for Evaluating Search-Augmented Generative Engine Optimization
Sunghwan Kim · Wooseok Jeong · Serin Kim · Sangam Lee · Dongha Lee (corresponding author) · Yonsei University, Dept. of Artificial Intelligence · Konkuk University, Dept. of Computer Engineering
They did not, and in places they hurt. When methods that optimize body text alone were reproduced in an environment with retrieval → reranking → generation, performance at the retrieval and reranking stages fell noticeably. The authors concluded that optimization has to be matched to each pipeline stage.
arXiv:2603.20213 arXiv preprint (code released)
AgenticGEO: A Self-Evolving Agentic System for Generative Engine Optimization
Jiaqi Yuan · Jialu Wang · Zihan Wang · Qingyun Sun · Ruijie Wang (corresponding author) · Jianxin Li · Beihang University, School of Computer Science · with independent contributors
It did. The authors argue that the static heuristics proposed in earlier GEO work — add keywords, add citations, add statistics — fail to optimize roughly half of samples, and propose an agentic system that evolves a strategy per document. In their own evaluation they report gains of 26–28% in-domain and 37–70% out-of-domain over the previous best (AutoGEO).
arXiv:2606.20065 arXiv preprint (single author) CONFLICT DISCLOSED
Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines
Pratyush Kumar · Ranqo (a commercial GEO tracking platform)
Mostly outside the brand's own site. Across 149,912 citations, the brand's own domain accounted for just 2.9%, while company and third-party brand pages made up 75.2%. Appearance rates diverged sharply by brand size. Note that this data comes from a company selling GEO tracking tools, produced with its own platform.
arXiv:2607.14035 arXiv preprint
Optimizing Visibility in Generative Engines: A Critical Survey of Generative Engine Optimization (2023–2026)
Martinez · Academic preprint
It proposes modelling GEO as a nine-stage probabilistic pipeline and argues that discoverability and citation must be treated separately. It also points out that figures like 'a 40% visibility increase' are conditional values that presuppose the document was already retrieved.
arXiv:2605.25517 arXiv preprint CONFLICT DISCLOSED
What Gets Cited: Competitive GEO in AI Answer Engines
Rahul Vishwakarma · Shushant Kumar · Ratnesh Jamidar · Sprinklr (Gurugram, India · Dubai, UAE) — a commercial AI/CX platform
Topical match and list position dominated, with pricing information and a recent timestamp next. The authors ran 252,000 pairwise comparisons injecting two candidate documents that differed in **exactly one factor**. Topical mismatch, missing pricing, an old timestamp and second position in a list were conditions that lost essentially always, and the gap between assertive and hedged phrasing was also large.
arXiv:2605.00012 arXiv preprint (14 pages, no conference publication listed)
Exploring LLM biases to manipulate AI search overview
Roman Smirnov · Single author (no affiliation stated)
The author reports that it can. He trained a small language model with reinforcement learning to rewrite search snippets, and writes that the rewritten snippets drew the LLM overview's selection in most cases. He also shows that selection depends on **relative comparison between candidates** rather than absolute quality, and that context poisoning attacks can produce inaccurate or harmful results. No success rate is reported numerically.