GIANT: A Relational Anchor-Based Programming Language

GIANT (Generalized Intelligent Anchor-Based Notation) is a programming language designed to make relational and tolerance-based reasoning explicit in computation. Unlike traditional languages that treat numeric values as absolute scalars, GIANT represents values together with anchors: structured constraints that include a reference value, a tolerance range, and a comparison relation. Programs evaluate whether values satisfy these anchor constraints, making approximate reasoning and context-dependent comparisons first-class operations. The language is motivated by real-world domains such as measurement validation, threshold-based decision systems, and rule-based classification where exact equality is unrealistic. GIANT’s core contribution is a formal semantic model that treats anchors as primary values, along with operational rules that define deterministic evaluation. This work does not claim machine intelligence; rather, it provides a formal framework for transparent, auditable relational computation. Future work includes static typing for anchors, compositional relations, and tooling for visualization and integration with decision systems.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-19
DOI
https://doi.org/10.5281/zenodo.18302080
Primary Topic
Constraint Satisfaction and Optimization
Type
article
Field-Weighted Citation Impact
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article

GIANT: A Relational Anchor-Based Programming Language

Itunuayo Adebiyi
Zenodo (CERN European Organization for Nuclear Research)
Constraint Satisfaction and Optimization
article

GIANT: A Relational Anchor-Based Programming Language

Itunuayo Adebiyi
article en

Abstract

GIANT (Generalized Intelligent Anchor-Based Notation) is a programming language designed to make relational and tolerance-based reasoning explicit in computation. Unlike traditional languages that treat numeric values as absolute scalars, GIANT represents values together with anchors: structured constraints that include a reference value, a tolerance range, and a comparison relation. Programs evaluate whether values satisfy these anchor constraints, making approximate reasoning and context-dependent comparisons first-class operations. The language is motivated by real-world domains such as measurement validation, threshold-based decision systems, and rule-based classification where exact equality is unrealistic. GIANT’s core contribution is a formal semantic model that treats anchors as primary values, along with operational rules that define deterministic evaluation. This work does not claim machine intelligence; rather, it provides a formal framework for transparent, auditable relational computation. Future work includes static typing for anchors, compositional relations, and tooling for visualization and integration with decision systems.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 87%
Constraint Satisfaction and Optimization
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