/Model-driven Constraint-based Transformation, Control, And Evaluation Of Computational Systems
Abstract

A computer-implemented system and method for controlling, testing and specifying behaviour of a computational system, including machine learning models, based on a structured model are disclosed. The structured model includes components, relationships, and configuration parameters. One or more requirements are obtained from the structured model or external sources, and constraints are generated based on the requirements and an execution context. The constraints are represented as structured constraint objects and may be included in a machine-readable contract. One or more prompts or documents are generated based on the structured constraint representation to control, test or specify behavior of a computational system, including, but not limited to, a machine learning model. Outputs generated by the computational system may be evaluated against the structured constraint representation using deterministic evaluation rules, and prompts or inputs may be modified to regenerate outputs until the outputs correspond to the constraints.

Full Text

What is claimed is:

A computer-implemented system and method for controlling, testing and specifying behaviour of a computational system, including machine learning models, based on a structured model are disclosed. The structured model includes components, relationships, and configuration parameters. One or more requirements are obtained from the structured model or external sources, and constraints are generated based on the requirements and an execution context. The constraints are represented as structured constraint objects and may be included in a machine-readable contract. One or more prompts or documents are generated based on the structured constraint representation to control, test or specify behavior of a computational system, including, but not limited to, a machine learning model. Outputs generated by the computational system may be evaluated against the structured constraint representation using deterministic evaluation rules, and prompts or inputs may be modified to regenerate outputs until the outputs correspond to the constraints.
Timeline
Filed
04/30/2026
Published
09/03/2026
Granted
Not Available
IPC Codes(1)
G06N 20/00:Machine learning