2nd Report Of April 2025

Mainnet Performance

• Validator Nodes: 103
• Miner Nodes: 1,174
• New Wallets: +43

Project Updates

1. Large Model Initialization

Optimization of Morpheus as the Base Large Model for AI Agents: 67%

• Model tuning and optimization are fully complete; current efforts are focused on integration testing with the Agent Model Center.
• Work is being done to optimize API interfaces to ensure Morpheus integrates seamlessly with other models and tools in the platform.

Integration of DeepSeek V3’s MOE Model Framework: 87%

• Final version of the MOE framework has been fully integrated and is now stably running within Morpheus.
• Conducted comprehensive performance testing to evaluate training speed, accuracy, and memory consumption.

2. Support for Distributed Privacy Computing (On Hold)

• Expansion of the MANTA Architecture for Privacy-Preserving Agent Training and Inference: 0%
• Optimization of MANTA for DeepSeek V3 MOE Model Requirements: 0%

3. Agent Model Center (MVP)

Initial Version Deployment: 77%

• First version of the Model Center has been released with Agent Tools from MATRIX AI, allowing users to build custom agents from available modules.
• Introduced a visual development environment enabling drag-and-drop agent design, configuration-based customization, and code-level scripting.

Extension of Compatible Baseline Models — Integration of DeepSeek V3: 70%

• Improved the model selection and comparison interface to help users quickly find and utilize suitable baseline models for training and inference.

4. AI Agent MVP

Testable MVP Version Based on Morpheus: 30%

• Development may shift from releasing isolated agents to fully integrating functionality into the Agent Center.

5. Launch of Brainwave Distributed Database

Deployment of NeuraMATRIX’s First Ecosystem Application & Data Collection: 17%

• Continued user data collection and anonymization.
• MetaTron firmware updated to improve EEG signal recognition accuracy.

6. Others

MCP Framework Integrated into MANTA

• MCP performs semantic analysis of input tasks and decomposes them into semantic subtasks, each with clear context (e.g., input data type, execution goals, dependencies, and preconditions).
• These subtasks offer deeper intent comprehension compared to traditional task slicing, allowing more accurate distribution to optimal nodes in a decentralized environment.

Open-Source MCP Router Tool Released

• Built on MATRIX’s distributed AI Agent service network technology, this router allows users to direct tasks to suitable nodes in a decentralized agent architecture.

The Matrix AI Network was founded in 2017. In 2023, we enter Matrix 3.0 blending neuroscience with our previous work to realize the vision of the Matrix films.

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Owen Tao (CEO) | Steve Deng (Chief AI Scientist) | Eric Choy (CMTO)