How a finite context window turns selection into a scarce-resource problem with unequal opportunities for source influence.
Top-k creates a hard participation threshold
Top-k means retaining only the highest-priority k candidates for later processing. If k equals five, a source ranked sixth may receive no opportunity to influence the answer.
The foundational GEO benchmark fixed the context by supplying the top five Google results. That design measures post-retrieval effects well, but it cannot show whether a rewrite changes organic top-k inclusion.
Context exposure measures how much the model can use
Martinez represents context exposure as K: position, top-k inclusion, and allocated tokens. Two sources can both survive selection while receiving very different exposure—several passages for one, a short excerpt for another.
Exposure describes opportunity, not guaranteed influence. The generator can still ignore material it receives.
Effective context remains scarce
Even large context windows require decisions about which documents and passages deserve attention, how much text each receives, and what must be omitted. Five sources can share 10,000 usable tokens very unevenly.
Presence is binary; context opportunity is graduated.
The context bottleneck narrows the source pool
Many retrievable sources compete for few context slots; fewer become citations; fewer still substantially influence the response. GEO therefore requires both entering the candidate pool and surviving the context bottleneck.
Questions about this topic
What does top-k mean?+
It is the limited subset of highest-priority candidates retained after ranking or reranking for downstream processing.
What is context exposure?+
It describes whether a source enters context, its effective rank, the passages selected, and how much token or context share it receives.
Does entering context guarantee citation?+
No. A source may enter context but be ignored, used without attribution, or omitted from the final response.
Why is context a scarce resource with large windows?+
Systems still allocate computation, attention, passages, and answer space selectively; the effective usable context is not unlimited.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5–16). Association for Computing Machinery. https://doi.org/10.1145/3637528.3671900
- Zhang, K., He, X., & Yao, J. (2026). From citation selection to citation absorption: A measurement framework for generative engine optimization across AI search platforms [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.25707