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Presenc AI — GEO academic papers 2026

curated listoptimizationactive
Presenc AI's GEO academic papers page, published 23 April 2026 by its CTO, gathers 34 papers and reports on generative engine optimization in five themes, each with a vendor note on what it means for practitioners.
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Entity

Canonical namePresenc AI — GEO academic papers 2026
Addresshttps://presenc.ai/research/geo-academic-papers-2026
OperatorPresenc AI (byline: Ramanath, CTO and co-founder) op
Kindcurated list
Rolesoptimization
Lifecycleactive (published and modified 23 April 2026)
Observation2026-10-28: read
Unitpaper or report on generative engine optimization op
What it records per itemtitle; authors and affiliation; venue and year; link; key finding; the vendor's 'what it means for practitioners' note op
Count34 (as of 2026-04-23) op
Notevendor-authored; the page states 'The Presenc AI audit framework is directly anchored in this literature'. A sibling list, 'Essential GEO Research Papers, 10 Must-Read Studies', is on the same site; the Overview's card dates the 34-paper summary 15 April 2026, the page's byline reads 'Last updated: April 2026' and its HTML metadata gives 23 April. The vendor's name is corrected by Google to 'presence' before it is read (the capture of 2026-10-29)

op operator statement · 3p third-party report · obs our observation · rd our reading

What it is

Presenc AI, a commercial AI-visibility vendor, groups 34 items (conference papers, preprints, a thesis, a journal article, and blog-style and whitepaper items) under five themes: content-level optimization, structural and metadata signals, adversarial security, measurement and governance, and comparative or sector studies. Each carries title, authors, venue, link and key finding, and a Presenc note.

How it reads the literature

The page states that "the Presenc AI audit framework is directly anchored in this literature," ties several findings to its features, and closes on a waitlist. Eight entries are authored by "AIVO Standard," whose relation to Presenc the page does not give.

Place in the field

It is the fullest of the optimization-side lists, and the literature it gathers is read toward its product. Its selection is the GEO literature as a vendor needs it.

Relations

Neighbours in the basin

Captures: the composition layer's account

The /non row

Provenanceevery source read, with its date1

Cite this record

Lee Sharks, ed. "Presenc AI — GEO academic papers 2026." MMRS meta-aggregator, Machine-Mediated Reception Studies, compiled 2026-10-29. https://www.machinemediation.org/aggregators/presenc-geo-papers/

Record https://www.machinemediation.org/aggregators/presenc-geo-papers/ · entity https://www.machinemediation.org/aggregators/presenc-geo-papers/#entity

Editor: Lee Sharks (ORCID 0009-0000-1599-0703). This page is the MMRS record of the entity above: its reading, fields and relations are MMRS's, and each field carries its source and basis. Record https://www.machinemediation.org/aggregators/presenc-geo-papers/ · entity https://www.machinemediation.org/aggregators/presenc-geo-papers/#entity · compiled 2026-10-29