Our Team

Prasad - 6+ years in Web3 R&D and protocol design, with deep expertise in decentralized ledger consensus, incentive models, and smart-contract architectures. Bachelor’s in Computer Science & Engineering.

Siva - Academic background in cryptography and blockchain. Computer Engineering degree with strong systems background; Advocates for statistically sound evaluation; has designed and executed large-scale benchmark suites for consensus protocols and cryptographic primitives.

Company Name (if applicable)

Project details

NeuroLogica is a hybrid neural-symbolic research initiative that combines differentiable logic programming with advanced continuous-function networks to achieve both experiential learning and higher-order reasoning. Our goal is to embed symbolic rules—derived either from agent-driven exploration systems or from human experts—directly into deep neural network architectures, and to complement this with compact, interpretable function approximators. By integrating PyNeuraLogic and KANs within the PRIMUS cognitive framework on Hyperon, NeuroLogica will demonstrate a novel, reproducible approach to building AI systems that learn from limited data, reason over complex structures, and explain their inferences in human-readable form.

Motivation & Rationale
Pure deep learning models excel at pattern recognition but lack transparency and struggle with small datasets or structured reasoning. Pure symbolic engines offer clear logic but are brittle when faced with noisy or high-dimensional inputs. NeuroLogica bridges this divide by:

• Leveraging Prior Knowledge: Symbolic rules guide neural learning, reducing data requirements.

• Ensuring Explainability: Learned models preserve rule structure and yield interpretable functions.

• Enhancing Reasoning: Logic inference and numeric prediction work together to solve structured and continuous tasks.
  This synergy aligns with the RFP’s emphasis on experiential rule embedding, higher-order reasoning, data efficiency, and integration into PRIMUS.

Core Components

1. PyNeuraLogic Module

• Implements differentiable forward-chaining inference: symbolic clauses become neural computation nodes with trainable weights.

• Supports graph and ontology data: relational structures are handled natively, enabling tasks like knowledge-graph completion and rule-guided classification.

• Facilitates dynamic rule updates: rules generated by AIRIS or provided by users are loaded into Atomspace, and PyNeuraLogic adjusts its inference graph accordingly.

2. KAN Module

• Realizes learnable univariate functions on each network edge, replacing fixed activations and scalar weights.

• Achieves superior approximation efficiency: compact networks reach target accuracies with fewer parameters and less training data.

• Exposes edge functions for post-training visualization: researchers can extract and inspect mathematical curves that the model learned, translating them into domain-relevant formulas.

Integration with PRIMUS/Hyperon
NeuroLogica’s components will be orchestrated via MeTTa scripts within Hyperon’s Atomspace knowledge graph. Key integration pathways include:

• Rule Exchange: PyNeuraLogic reads AIRIS-generated rules and writes back inferred facts, all represented as Atomspace atoms.

• Numeric Services: KANs are exposed as Python services callable from MeTTa; prediction results and extracted functions are stored in Atomspace for downstream planning and explanation.

• Cognitive Loop: Experience → Rule Generation → Logic-Neural Inference → Numeric Prediction → Planning → New Experience, enabling continual learning and reasoning.

NeuroLogica will validate its approach through two primary POCs [TBD - but this is just a few basic ones we could thiink of right now]:

• Smart-Grid Forecasting & Control: Embed grid-topology and safety rules via PyNeuraLogic; forecast load fluctuations with KANs; demonstrate how combined reasoning and prediction yield actionable insights and clear explanations for operators.

• Clinical Decision Support: Encode medical ontology rules (symptoms, diagnoses, drug interactions) in PyNeuraLogic; learn patient-specific response curves with KANs; deliver a diagnostic assistant that provides both risk scores and logical justifications for recommendations.

NeuroLogica rbings a true convergence of neural learning and symbolic reasoning within a scalable AGI framework. By seamlessly embedding experiential and abstract rules into neural architectures, and by harnessing function-rich approximators, it fulfills the RFP’s vision of adaptive + explainable AI. At project close, NeuroLogica will deliver a versatile toolkit, ready for extension into additional domains, and a blueprint for integrating hybrid architectures in next-generation cognitive systems.

Open Source Licensing

Links and references

Pitch Deck - https://drive.google.com/file/d/1MwU1DdiRAv4TY-TLPrdqdJqvg4v7MZ4b/view?usp=sharing