This foundational phase focuses on the detailed architecture of the Model Context Protocol (MCP) and the integration strategy for neural-symbolic components. We will define MCP's core: tool contracts (for MeTTa reasoner PyNeuraLogic GNN component) semantic dispatching logic and context management. A comprehensive research plan for LLM-to-MeTTa translation techniques will be established. We will design the specific PyNeuraLogic component including how it will represent and process structured rules. A critical sub-task is the AIRIS applicability study: defining data structures and processes to capture "vibe coding" interactions from the "vhybZ" mobile app (user prompts VLM outputs refinement actions) as suitable input for an AIRIS-like rule generation pipeline. This includes specifying how these learned experiential rules can be translated into formats usable by PyNeuraLogic (e.g. graph structures relational logic) and/or directly by MeTTa. The plan will detail how these NeSy elements will interact within MCP to enhance LLM capabilities in reasoning and learning from experience. We will also outline the agile breakdown of tasks for subsequent milestones risk assessment and a preliminary framework design for the overall system.

1. Detailed Research Plan & MCP Architecture Document (PDF): * Comprehensive MCP design: protocol specifications tool contract formats semantic dispatcher logic. * Strategy for LLM-to-MeTTa translation. * Detailed design of the PyNeuraLogic component and its rule representation. * Agile breakdown of tasks for M2 & M3 with timelines. * Initial overall framework design sketch. 2. AIRIS Integration Feasibility Report & Data Specification (LaTeX): * Analysis of AIRIS applicability to "vhybZ" app data. * Detailed specification for capturing and structuring "vibe coding" interactions (prompts VLM outputs user actions) for input into an AIRIS-like rule generation process. * Proposed formats for representing AIRIS-generated rules for PyNeuraLogic and/or MeTTa. 3. Initial MeTTa & PyNeuraLogic Setup Report: Document confirming setup of development environments for MeTTa and PyNeuraLogic including library installations and basic "hello world" style tests.

$8,000 USD

1. SingularityNET approval of the Research Plan and MCP Architecture, confirming methodological soundness and alignment with RFP objectives (NeSy integration, PRIMUS compatibility). 2. AIRIS Integration Feasibility Report demonstrates a clear, viable pathway for generating symbolic rules from "vhybZ" app interactions and representing them for PyNeuraLogic/MeTTa. Data specification is complete. 3. Design for the PyNeuraLogic component is well-defined and suitable for the types of rules expected from the AIRIS-like process. 4. LLM-to-MeTTa translation strategy is clearly outlined with identified techniques for development. 5. Development environments for MeTTa and PyNeuraLogic are operational.

This phase focuses on the initial development and implementation of the core MCP framework and its key neural-symbolic tool integrations. We will build the MCP dispatcher logic tool registration system and the communication protocols based on the M1 design. The `metta_reasoner` tool will be implemented including the initial version of the LLM-to-MeTTa translation module enabling basic symbolic queries to be processed. The selected PyNeuraLogic component for handling structured/experiential rules will be developed. A significant effort will be dedicated to the AIRIS-inspired Proof-of-Concept: developing scripts and methods to process sample (anonymized if necessary) "vibe coding" interaction logs from the "vhybZ" app to generate a preliminary set of symbolic rules. We will then implement the pipeline to embed these generated rules using the PyNeuraLogic component or represent them directly in MeTTa. Preliminary testing of individual components (MCP dispatcher MeTTa reasoner PyNeuraLogic module with AIRIS rules) and their basic integration via MCP will be conducted. The "MeTTa digest tool" concept will be prototyped for capturing interaction data.

1. **MCP Framework v0.1 Codebase (e.g. Git repository):** * Core MCP dispatcher tool registration and communication modules. * Implemented `metta_reasoner` tool with initial LLM-to-MeTTa translation capability. * Implemented PyNeuraLogic component for rule embedding. 2. **AIRIS-Inspired Rule Generation & Embedding PoC:** * Scripts/tools for processing sample "vhybZ" interaction data to generate symbolic rules. * Demonstration of these rules being embedded via PyNeuraLogic or loaded into MeTTa. * Sample set of generated rules. 3. **Initial Testing Results & Analysis Report (PDF):** * Results from unit tests of MCP components MeTTa reasoner and PyNeuraLogic module. * Report on the AIRIS rule generation PoC performance and quality of initial rules. * Analysis against standard benchmarks (if applicable at this stage) or qualitative assessment of functionality. * Draft implementation of the "MeTTa digest tool" for capturing interactions.

$16,000 USD

1. MCP framework v0.1 successfully dispatches tasks to the `metta_reasoner` and the PyNeuraLogic component based on defined tool contracts and semantic triggers. 2. The `metta_reasoner` can process basic symbolic queries translated from pseudo-natural language. 3. The AIRIS-inspired PoC successfully generates a preliminary set of symbolic rules from sample "vhybZ" app data, and these rules are demonstrably usable by the PyNeuraLogic component or MeTTa. 4. Initial testing results show functional correctness of individual components and basic integration. 5. "MeTTa digest tool" prototype successfully captures interaction KPs in MeTTa. 6. Clear progress towards addressing RFP's functional requirements for NeSy DNN integration and logic rule inclusion.

This concluding phase will deliver a fully integrated Proof-of-Concept (PoC) system conduct comprehensive evaluations and produce all final documentation. The PoC will showcase an LLM/VLM utilizing the MCP framework to dynamically call upon the `metta_reasoner` (for higher-order reasoning) and the PyNeuraLogic component (processing AIRIS-inspired rules from "vhybZ" app data) to solve complex tasks or enhance its responses. This demonstration will highlight improvements in reasoning consistency and adaptation based on experiential learning. We will conduct a thorough evaluation using the performance metrics defined in M1 comparing the MCP-augmented LLM against baseline LLM performance. The functionality and impact of the "MeTTa digest tool" on context relevance and knowledge evolution will be assessed. This phase includes detailed analysis of how the system addresses human interpretability learning from small data bridging data-driven/symbolic reasoning and applicability to structured learning domains. All code will be finalized and documented. The final report will encompass the project's research design implementation evaluation and recommendations for future PRIMUS integration and further research.

1. **Final Comprehensive Report (PDF):** * Complete project overview: architecture methodologies implementation details. * Detailed performance analysis based on defined metrics including comparison with baselines. * Evaluation of how the system addresses RFP criteria: interpretability small data learning bridging NeSy gap structured learning. * Analysis of the "MeTTa digest tool" and AIRIS-PyNeuraLogic pipeline. * Recommendations for PRIMUS integration and future research directions. 2. **Fully Integrated Proof-of-Concept (PoC) System & Demonstration:** * Complete documented codebase (Python MeTTa scripts) for the MCP framework MeTTa reasoner PyNeuraLogic component AIRIS rule generation pipeline and "MeTTa digest tool." * A live or recorded demonstration of the PoC system showcasing an end-to-end LLM task enhanced by MCP-orchestrated NeSy components. 3. **Complete Project Documentation:** System architecture diagrams user/developer guides for the framework and tools and setup instructions. 4. (If applicable) Websites or other dissemination materials.

$16,000 USD

1. Submission of a high-quality Final Report that comprehensively details all research, development, and evaluation activities, and directly addresses all key RFP objectives and evaluation criteria. 2. Successful demonstration of the fully integrated PoC system, clearly showing an LLM/VLM leveraging MeTTa and PyNeuraLogic (with AIRIS-derived rules) via MCP to achieve improved reasoning, consistency, or experiential adaptation on defined tasks. 3. The PoC effectively illustrates the core neural-symbolic concepts explored (logic rule embedding, reasoning over DNN outputs, experiential learning). 4. All code is well-documented, functional, and reproducible as per RFP requirements. 5. The project provides clear evidence and analysis regarding improvements in interpretability, learning from small data, and bridging the data-driven/symbolic gap. 6. Actionable recommendations for PRIMUS integration are provided. 7. All deliverables are accepted by SingularityNET.