When Search Eats the Web: A Model of Corpus Erosion Under Generative Extraction

p4bl01 pts0 comments

[2608.15896] When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction

Skip to main content

Search arXiv

Press Enter to search · Advanced search

-->

Computer Science > Computer Science and Game Theory

arXiv:2608.15896 (cs)

[Submitted on 16 Aug 2026]

Title:When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction

Authors:Sylvain Peyronnet<br>View a PDF of the paper titled When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction, by Sylvain Peyronnet

View PDF<br>HTML (experimental)

Abstract:Generative search engines (GSEs) answer user queries directly from crawled web content. The capture of value from the corpus without a visit returned to the source (we call this capture extraction) diverts the traffic that finances content production. In response, publishers may restrict crawler access to their websites. In this paper, we model the crawlable corpus as a common-pool resource: the crawlable commons. It is described by three quantities: volume, average quality, and lifetime. Under two types of responses of publishers we prove that extraction degrades all three at once: publishers opt out, renewal loses its funding, and content becomes more perishable. After a given erosion threshold, the corpus goes extinct. A myopic GSE can cross this threshold, a long-run oriented GSE stays below it. We extend our model to several competing engines and prove, under a concavity condition on the steady-state value of the commons, that the symmetric equilibrium extraction rate is nondecreasing in their number and converges to the threshold. Adding users who strictly prefer direct answers, the assumption most favorable to extraction, we prove that the socially optimal extraction rate lies strictly below the erosion threshold, and no higher than the single engine's sustainable optimum. Finally, we discuss seven survival mechanisms.

Comments:<br>19 pages including a 1 page appendix

Subjects:

Computer Science and Game Theory (cs.GT); Information Retrieval (cs.IR)

MSC classes:<br>91A80 (Primary), 91B76 (Secondary)

ACM classes:<br>H.3.3; J.4

Cite as:<br>arXiv:2608.15896 [cs.GT]

(or<br>arXiv:2608.15896v1 [cs.GT] for this version)

https://doi.org/10.48550/arXiv.2608.15896

Focus to learn more

arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Sylvain Peyronnet [view email]<br>[v1]<br>Sun, 16 Aug 2026 19:03:13 UTC (41 KB)

Full-text links:<br>Access Paper:

View a PDF of the paper titled When Search Eats the Web: A Model of Corpus Erosion under Generative Extraction, by Sylvain Peyronnet<br>View PDF<br>HTML (experimental)<br>TeX Source

view license

Current browse context:

cs.GT

next >

new<br>recent<br>| 2026-08

Change to browse by:

cs<br>cs.IR

References & Citations

NASA ADS<br>Google Scholar

Semantic Scholar

export BibTeX citation<br>Loading...

BibTeX formatted citation

&times;

loading...

Data provided by:

Bookmark

Bibliographic Tools

Bibliographic and Citation Tools

Bibliographic Explorer Toggle

Bibliographic Explorer (What is the Explorer?)

Connected Papers Toggle

Connected Papers (What is Connected Papers?)

Litmaps Toggle

Litmaps (What is Litmaps?)

scite.ai Toggle

scite Smart Citations (What are Smart Citations?)

Code, Data, Media

Code, Data and Media Associated with this Article

alphaXiv Toggle

alphaXiv (What is alphaXiv?)

Links to Code Toggle

CatalyzeX Code Finder for Papers (What is CatalyzeX?)

DagsHub Toggle

DagsHub (What is DagsHub?)

GotitPub Toggle

Gotit.pub (What is GotitPub?)

Huggingface Toggle

Hugging Face (What is Huggingface?)

ScienceCast Toggle

ScienceCast (What is ScienceCast?)

Demos

Demos

Replicate Toggle

Replicate (What is Replicate?)

Spaces Toggle

Hugging Face Spaces (What is Spaces?)

Spaces Toggle

TXYZ.AI (What is TXYZ.AI?)

Related Papers

Recommenders and Search Tools

Link to Influence Flower

Influence Flower (What are Influence Flowers?)

Core recommender toggle

CORE Recommender (What is CORE?)

Author

Venue

Institution

Topic

About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Which authors of this paper are endorsers? |<br>Disable MathJax (What is MathJax?)

Major funding support from

toggle search extraction arxiv corpus model

Related Articles