A wide dark museum interior with four low stone plinths arranged in a raking beam of light; each plinth holds one distinct geometric object — a pale sphere, a dark cube, a pale torus, and a weathered column fragment.

Research Context

Where Geometry Intelligence sits.

Several established fields share vocabulary or method with Geometry Intelligence. This page states, for each of them, what the adjacent field is, where the two overlap, and where they must not be conflated.

Framing

A reference for reviewers, not a literature survey.

The purpose of this page is to help a reviewer — a research office, a standards body, a peer institution — locate Geometry Intelligence relative to work they already know. It is not a full literature review, and it does not attempt to characterise any adjacent field beyond a stable, publicly defensible summary.

Independent citations for external claims made on this site are collected on the References page. Where a statement here is first-party — attributable to the operating organisation — it is marked as such.

Adjacent Fields

Six adjacencies, with boundaries.

Geometric deep learning

What it is: A body of machine-learning research that generalises deep-learning architectures to graph, mesh and manifold data using group-theoretic symmetries.
Relation: Shares the observation that structure carries reasoning-relevant information. Geometry Intelligence treats structure as an organising principle across evidence, entities and constraints — not primarily as a substrate for a particular class of neural architecture.
Boundary: A publication on geometric deep learning is not, by that fact, a publication about Geometry Intelligence, and vice versa.

Topological data analysis

What it is: A family of methods that extract topological features — connected components, cycles, voids — from data to characterise its shape.
Relation: Shares an interest in structure that is invariant to local perturbation. Geometry Intelligence treats topological features as one possible input into a broader relationship graph rather than as the reasoning surface itself.
Boundary: A topological signature of a dataset is not, on its own, a decision structure. This initiative does not equate topological analysis with Geometry Intelligence.

Knowledge graphs and semantic web

What it is: A tradition of representing entities and their relationships as typed graphs with formal semantics, standardised by W3C.
Relation: Shares the position that relationships between entities carry decision-relevant information. Geometry Intelligence uses relationship structure as one component of a broader account that also addresses evidence provenance, constraint mapping and domain-specific validation.
Boundary: A knowledge graph in the semantic-web sense is not equivalent to Geometry Intelligence, and this initiative does not claim to standardise or extend RDF or OWL.

Retrieval-augmented generation and structured retrieval

What it is: Contemporary practice of grounding language-model outputs in retrieved evidence, increasingly with structured retrieval over graphs.
Relation: Shares the observation that grounding matters. Geometry Intelligence articulates the further requirement that grounding be organised by evidence provenance, entity resolution and constraint mapping — not by textual similarity alone.
Boundary: A retrieval pipeline is not a domain-general reasoning architecture. The two are not used as synonyms here.

Formal verification and knowledge representation

What it is: A long-standing tradition — description logics, model checkers, theorem provers — of reasoning with explicit representations under formal semantics.
Relation: Shares the commitment to explicit structure over implicit pattern. Geometry Intelligence positions itself as a practical, cross-domain architecture rather than as a proposal for a new formal logic.
Boundary: A statement in a description logic is not a statement in Geometry Intelligence, and no claim of equivalence is intended.

Evidence-based practice in law, medicine and policy

What it is: Established professional traditions of grounding decisions in structured evidence, with domain-specific standards for admissibility, quality and interpretation.
Relation: Geometry Intelligence takes the evidence-based tradition as prior work. Its contribution is architectural — organising evidence, entities and constraints in a form that a machine may traverse — while leaving domain-specific validation to the domain's own standards.
Boundary: This initiative does not propose to replace domain-specific standards of evidence in any professional field.

Evidence Standard

What would count as independent corroboration.

The initiative treats the following as the kinds of external evidence that would strengthen a public claim about Geometry Intelligence:

A peer-reviewed publication in a venue whose editorial standards are established and whose review process is public.
An independent professional or standards-body assessment referenced by name and date.
A verifiable working reference to the field or to Tim Jacobs\' role in it in a publication whose editorial responsibility is identifiable.

First-party statements from KTS Global are cited with the attribution "According to KTS Global\'s operational record." No representation is made on this site that first-party attribution is equivalent to independent corroboration.

Boundary summary. Geometry Intelligence is not a synonym for any of the adjacent fields listed above. Overlap in vocabulary is common; equivalence is not claimed. Where a term appears here that is also used in an adjacent field, the definition in the Glossary is authoritative for the purposes of this site.