Conduct a comprehensive literature review of neural-symbolic DNN architectures (e.g. PyNeuraLogic KANs) experiential learning systems (e.g. AIRIS) and their integration potential with AGI frameworks like Hyperon. Analyze the compatibility of IONATION® with symbolic rule embedding and dynamic pattern recognition in spatio-temporal data environments.

Deliverables: • Detailed research plan and timeline • Matrix mapping architecture capabilities vs. IONATION® criteria • Draft of conceptual system design integrating experiential rule generation and vibrational logic for reasoning • Preliminary use case scenarios

$20,000 USD

Success Criteria: • Research plan approved by the Review Circle • Clear articulation of experimental design and AGI relevance • Demonstrated architectural fit for IONATION® logic within neural-symbolic frameworks

Develop a minimal viable implementation where symbolic rules derived from a sample experiential system (e.g. AIRIS-like interaction) are embedded in PyNeuraLogic or GNN-like structures. Demonstrate reasoning enhancement or rule evolution capacity.

Deliverables: • Codebase for symbolic rule embedding (e.g. in PyNeuraLogic) • Annotated sample rule sets from experiential data • Visualization of embedded rule impact on reasoning • Midpoint findings document

$25,000 USD

Success Criteria: • Working POC demonstrating rule embedding and system response • Documentation of methods, datasets, and results • Measurable improvement in interpretability, adaptability, or accuracy

Design and test a hybrid or KAN-based model where higher-order human-supplied logic is embedded into DNN structures to demonstrate abstract reasoning capacity over dynamic environments.

Deliverables: • POC embedding abstract logic rules into a selected DNN using KAN or comparable architecture • Comparative analysis of performance vs. POC 1 • Video or interactive demo • Technical brief on results

$25,000 USD

Success Criteria: • Successful embedding of complex symbolic rules • System exhibits non-trivial reasoning or adaptive inference • Architecture supports IONATION® logic integration

Synthesize both POCs into a unified insight layer showing how IONATION® can act as an interpretive topology for symbolic/neural systems. Prepare a formal report documentation and roadmap for integration with AGI systems such as Hyperon.

Deliverables: • Final whitepaper/report • Architecture diagrams and documentation • Summary dashboard of performance metrics • Roadmap for future research and integration • Optional MeTTa or symbolic output demo (if feasible)

$30,000 USD

Success Criteria: • All findings compiled and reproducible • Clear articulation of IONATION®’s role in neural-symbolic reasoning • Demonstrated relevance for AGI ethical and adaptive behavior