This milestone lays the foundation for the Meta-Cognitive Neurosymbolic Reasoning Engine (MCNRE). We will finalize the architecture for the lambda calculus engine, meta-cognitive monitoring layer, and integration pathways with MeTTa and MORK. The focus will be on designing the type system for knowledge representation, defining reasoning workflows, and specifying the boundary contracts for external knowledge graph interaction. This phase also includes creating a functional prototype for lambda-based term evaluation, testing early reasoning patterns, and documenting abstraction strategies and contradiction handling logic. We will also plan infrastructure setup, prepare testing pipelines, and initiate light coordination with SingularityNET's technical team. The result is a complete blueprint for the MCNRE system and a verified path to integration with the SingularityNET ecosystem.

Architecture Document: Detailed system architecture for MCNRE including component responsibilities, data flow, reasoning layers, and interface plans Prototype Lambda Engine: Early-stage lambda calculus reasoning engine with support for basic term parsing, normalization, and type inference Knowledge Representation Specification: Formal definition of the type system, including entity, relation, and higher-order types used for reasoning Integration Plan: MeTTa expression mapping strategy, MORK interface alignment, and roadmap for phased compatibility implementation Reasoning Trace Format: Specification for how transparent reasoning steps will be recorded, structured, and verified Development Roadmap: Full breakdown of subsequent tasks, milestones, integration risks, and dependencies for future phases

$15,000 USD

A prototype lambda engine is operational and can process simple expressions with normalization and type validation The architecture document is completed, reviewed, and approved by the SingularityNET technical team The MeTTa/MORK integration plan is reviewed with initial ecosystem feedback and feasibility confirmed A clear type system and knowledge schema specification is delivered and validated through mock reasoning tests The reasoning trace structure is defined, with sample traces generated for early lambda operations Infrastructure setup and testing environments are initialized for rapid expansion in Milestone 2

This phase focuses on building out the core functionality of the MCNRE. We will implement a fully functional lambda calculus engine capable of handling typed knowledge representations, compositional reasoning, and pattern abstraction. The meta-cognitive monitoring layer will also be implemented to support reasoning strategy selection, contradiction detection, and trace logging. We will build adapters to connect with standard knowledge graph formats (RDF, property graphs, REST APIs), enabling ingestion and query translation. Initial integration with MeTTa and MORK will be completed, including expression mapping, data conversion, and early benchmarking. This milestone shifts the system from prototype to a working version that can reason over real-world graph data with traceable, auditable outputs. Integration testing and performance evaluation begin during this phase, setting the stage for final optimization in Milestone 3.

Functional Lambda Reasoning Engine: Full support for term parsing, normalization, abstraction, and inference over typed graph data Meta-Cognitive Monitoring System: Initial implementation supporting consistency checks, confidence scoring, and strategy selection Knowledge Graph Adapters: Connectors for RDF, property graphs, REST/GraphQL APIs to allow diverse data ingestion and interaction Initial MeTTa/MORK Integration: Working bidirectional interface with MeTTa S-expressions and MORK’s hypergraph data format Benchmark & Test Suite: Performance testing framework to validate reasoning speed, contradiction detection, and output traceability Two Demonstrated Use Cases: Real-world scenarios showing MCNRE’s reasoning, trace generation, and MeTTa/MORK interaction Updated Documentation: Developer documentation for system architecture, integration endpoints, and usage examples

$29,934 USD

The lambda engine can reason over structured knowledge graphs and return typed, explainable results The meta-cognitive layer detects basic contradictions, assigns confidence levels, and produces meta-traces The system demonstrates seamless ingestion from at least two standard knowledge graph formats Integration with MeTTa allows reasoning inputs to flow from S-expressions into MCNRE and return verified outputs MORK is successfully queried using lambda-translated operations, and benchmark tests run at meaningful scale At least two use cases (e.g. contradiction detection, scientific query mapping) are executed and documented All work is validated through performance metrics and shared in technical documentation for reviewer feedback

This milestone completes the MCNRE system, adding advanced reasoning features, performance tuning, and production-readiness. We will finalize the neural-symbolic fusion layer, enabling tensorized lambda operations and hybrid inference between neural models and symbolic structures. The MeTTa and MORK integrations will be fully optimized for performance, including hypergraph reasoning, parallel query dispatching, and large-scale batch processing. The reasoning trace system will be extended with signed meta-traces and abstraction control for different user roles. Additional adapters and APIs will be added to ensure full ecosystem compatibility. We will conduct system-wide testing, refine contradiction resolution strategies, and document all reasoning capabilities, interfaces, and deployment workflows. This milestone delivers a production-grade, end-to-end transparent reasoning engine for SingularityNET.

Finalized MCNRE System: Full implementation of lambda engine, meta-cognitive layer, neural-symbolic integration, and graph adapters Neural-Symbolic Fusion Engine: Tensorized lambda calculus operations with hybrid inference mechanisms and embedding interfaces Advanced MeTTa/MORK Integration: Performance-optimized connectors, query pipelines, and memory-efficient data handling Reasoning Trace Enhancements: Signed meta-trace support, confidence layers, abstraction levels, and provenance tagging Enterprise-Ready API Layer: RESTful interfaces with auth controls, developer documentation, and deployment configurations (Docker/Kubernetes) Deployment Package: Packaged version of MCNRE with installation scripts, setup guides, and sample projects Final Report & Benchmarks: Detailed performance metrics, reasoning validation, documentation, and future roadmap

$30,066 USD

MCNRE performs complex reasoning over large-scale knowledge graphs with full transparency and traceability Neural-symbolic layer supports hybrid logic + learning, delivering enriched inference capabilities MeTTa and MORK integrations are optimized and can handle billion-node scale workloads efficiently Signed meta-traces are generated for all reasoning tasks with role-based abstraction support REST API endpoints function securely and return structured, typed responses with confidence scores System is deployed in a containerized environment with full documentation for users and developers Final benchmarks meet latency, consistency, and scalability targets, and all components are ready for adoption by the SingularityNET ecosystem