Over the first two months we will perform an in-depth survey of recent neural-symbolic research (post-2022) with focus on PyNeuraLogic and KANs and map their capabilities against PRIMUS/Hyperon requirements. We’ll define the overall NeuroLogica architecture specify module interfaces design rule templates for experiential and higher-order logic and establish development environments data pipelines and Atomspace integration patterns.

Comprehensive research brief summarizing SOTA architectures tools and integration strategies. Detailed architecture specification document with component diagrams and data flows. Development environment configured: Hyperon sandbox PyNeuraLogic connector repo PyKAN service scaffold MeTTa wrappers.

$16,000 USD

Research brief and architecture spec reviewed and approved by the core team. Development environments provisioned and smoke-tested (e.g., sample rule read/write in Atomspace, KAN service startup). Clear project roadmap and risk register established.

During months 3–6 we will build and validate prototype implementations of the two core NeuroLogica modules. The PyNeuraLogic connector will load rules from Atomspace perform differentiable inference on sample graphs and write back new facts. The KAN service will train on continuous datasets expose prediction APIs and support post-training function extraction. We’ll also create MeTTa scripts to orchestrate end-to-end flows and develop data converters for smart-grid and clinical samples.

PyNeuraLogic module integrated with Atomspace and MeTTa capable of rule ingestion inference and fact export. KAN service with CLI/API for training prediction and function-curve export. MeTTa orchestration scripts demonstrating a simple workflow: ingest rules → run inference → call KAN predictor. Sample datasets and converters for power-grid and healthcare scenarios.

$32,000 USD

Both modules pass unit and integration tests against defined use cases. End-to-end demo (via MeTTa) executes without errors and produces expected outputs. Team coin-reviews confirm code quality, documentation, and reproducibility.

In the final three months we will execute two full proof-of-concepts: a smart-grid forecasting/control demo and a clinical decision-support assistant. We’ll run experiments to measure forecasting accuracy rule-based inference fidelity and explainability then refine models and integrations. Comprehensive documentation and user guides will be prepared to ensure reproducibility and ease of adoption.

Smart-grid POC: live dashboard showing rule triggers and KAN forecasts with extracted function visualizations. Clinical POC: diagnostic assistant that outputs risk assessments alongside logical justification traces. Evaluation report detailing metrics on accuracy data efficiency and interpretability with comparisons to baseline systems. Final code release deployment scripts developer guides and tutorial notebooks.

$32,000 USD

POCs achieve or exceed target metrics (e.g., ≥90% of baseline accuracy with ≤20% data, clear rule/fn explanations). Stakeholder demonstration sessions completed with sign-off. All deliverables published in the project repository with full documentation and reproducible pipelines.