# 2nd Report Of April 2025

### Mainnet Performance

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

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### 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.

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#### 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%**

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#### 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.

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#### 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.

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#### 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.

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#### 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.

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