Hypothesis generation and testing system (HGTS) for generative AI, agentic AI, contextual AI correlation analysis, iterative self-learning systems and artificial general intelligence (AGI) frameworks

A computer-implemented method for autonomous reasoning, the method comprising:

• generating, by at least one processor, a plurality of hypothesis objects, wherein each hypothesis object has a lifecycle state selected from the group consisting of at least one of:
• new, under evaluation, validated, rejected, and deprecated, and includes at least a set of independent variables, a set of dependent variables, a machine learning model, and metadata stored in a data structure;
• storing the plurality of hypothesis objects in a hypothesis database;
• providing a plurality of agents and at least one meta-reasoning agent configured to interact with the plurality of agents to perform at least one of:
• allocating computational resources, directing exploration of hypothesis space, iteratively fine-tuning, retraining, re-weighting and recursively refining hypothesis objects into sub-hypotheses;
• evaluating, by the at least one processor, at least one hypothesis object of the plurality of hypothesis objects;
• and
• updating the hypothesis database with evaluation results associated with the at least one hypothesis object.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor, cause the processor to perform agentic operations comprising:

• generating a plurality of hypothesis objects, wherein each hypothesis object has a lifecycle state selected from the group consisting of at least one of:
• new, under evaluation, validated, rejected, and deprecated, and includes at least a set of independent variables, a set of dependent variables, a machine learning model, and metadata stored in a data structure;
• storing the plurality of hypothesis objects in a hypothesis database;
• evaluating, using a plurality of agents, at least one hypothesis of the plurality of hypothesis objects;
• and
• updating the hypothesis database with results of the evaluation using at least one meta-reasoning agent configured to interact with the plurality of agents to perform at least one of:
• allocating computational resources, directing exploration of hypothesis space, iteratively fine-tuning, retraining, re-weighting and recursively refining hypothesis objects into sub-hypotheses.