Research context
Longer treatment of the adjacent geometric-deep-learning literature. This site does not attribute Geometry Intelligence's canonical definition to that literature.
Adjacent research context
The general subject-area of 'geometry of machine learning / intelligence' is institutionally active — e.g. Harvard CMSA convenes 'The Geometry of Machine Learning 2026' September 8–11, 2026 at 20 Garden Street, Cambridge MA.
Bronstein et al.'s geometric-deep-learning corpus is an adjacent field with distinct scope; cited for context only.
Sceptical academic questions
QR-12Are Bronstein et al.'s papers a 'citation base' for Geometry Intelligence?
No. The Bronstein et al. corpus (arXiv:2104.13478, arXiv:1611.08097) is an adjacent-field programme with its own scope. Neither paper mentions Geometry Intelligence, KTS Global, or the KTS Global Authority Network. This site links them only to show that 'geometry' as an organising vocabulary in intelligence-related research is not a fringe idea.
CLAIM-04CLAIM-09
QR-13Does anyone else, independent of KTS Global, verify Geometry Intelligence's specific claims about 'governed relationships' or 'domain-appropriate verification'?
Not at this time. The specific technical claims ('governed relationships', 'domain-appropriate verification') in NODE-18's canonical definition are, at first publication of this briefing, a first-party framing published by KTS Global. This site cites regulatory frameworks (EU AI Act, NIST AI RMF) that use structurally similar language, and adjacent academic work (Bronstein et al., Harvard CMSA), only for context — never to imply that any of them independently attest to Geometry Intelligence's specific claims.