# 1st Report Of March 2025
### Mainnet Performance • Validator Nodes: 107 • Miner Nodes: 1,201 • New Wallets: +57
### Project Updates #### 1. Large Model Initialization **• Optimization of Morpheus as the Base Large Model for AI Agents: 47%** • Developed a multi-task learning module, enabling Morpheus to train across different tasks in parallel, improving efficiency and application scope. • Optimized task scheduling and resource allocation mechanisms to ensure efficient parallel execution in multi-task and multi-agent systems. • Completed the design and testing of a multi-task training framework to ensure Morpheus’ stability and performance in complex environments. **• Language Understanding, Reasoning, and Communication Abilities: 0%** • **Enhanced Features:** Introduction of foundational personality modules (logical, emotional, creative) for personalized agent development: 0%. **• Integration of DeepSeek V3’s MOE Model Framework: 43%** • Expanded the system architecture to support multi-expert models efficiently. #### 2. Support for Distributed Privacy Computing • Expanding the MANTA computing framework to support privacy-preserving training and inference required by AI agents: 0%. • Optimizing the MANTA computing framework for MOE model training and inference required by DeepSeek V3: 0%. #### 3. Agent Model Center (MVP) **• Initial Version Deployment: 31%** • MATRIX AI provided pre-initialized Agent Tools, enabling users to assemble AI Agents using the platform. • Integrated core AI model functionalities such as classification, regression, and reinforcement learning, ensuring seamless operation on the platform. • Designed an AI Agent development framework that allows users to create and train custom AI Agents. • Introduced scalable agent functionality modules, including state management, behavior decision-making, and strategy optimization. • Extension of Compatible Baseline Models: 33% • Integrated DeepSeek V3 as one of the baseline models in the Agent Model Center. • Expanded system architecture to support multiple baseline models, ensuring interoperability. • Implemented a model-switching mechanism, allowing users to seamlessly switch between different baseline models. • Optimized data input and output interfaces for diverse model requirements, ensuring smooth data flow. • Enhanced multi-model parallel training performance, optimizing resource allocation and scheduling. #### 4. AI Agent MVP **• Providing a Morpheus-based AI Agent MVP for User Testing: 40%** • Launched the first AI Agent: Web 3.0 Mentor. #### 5. Launch of Brainwave Distributed Database • Deployment of NeuraMATRIX’s first ecosystem application, initiating data collection and anonymization processes: 22%. #### 6. Others {% embed url="" %} {% embed url="" %} {% embed url="" %} {% embed url="" %}