# 2nd Report Of March 2025

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### Mainnet Performance

* Validator Nodes: 105
* Miner Nodes: 1,182
* New Wallets: +44

***

### Project Updates

### Large Model Initialization

* **Optimization of Morpheus as the Base Large Model for AI Agents: 50%**
* * Optimized computational efficiency to reduce latency in training and inference while enhancing large-scale task processing capabilities.
  * Introduced distributed training mechanisms to ensure Morpheus operates efficiently across multiple GPU/TPU nodes, increasing computational throughput.
  * Completed model compression and quantization to reduce memory usage, ensuring efficient performance on resource-constrained devices.
  * Conducted performance testing and fine-tuning to ensure stability across diverse hardware platforms.
* **Language Understanding, Reasoning, and Communication Abilities: 50%**
* **Enhanced Features:** Introduction of foundational personality modules (logical, emotional, creative) for personalized agent development: 5%
* **Integration of DeepSeek V3's MOE Model Framework: 50%**
* * Completed large-scale distributed training of the MOE model to handle vast datasets.
  * Implemented MOE model applications in multi-task learning, supporting expert selection for different tasks.
  * Conducted large-scale dataset performance evaluation, balancing training speed and accuracy.

### Support for Distributed Privacy Computing (Paused) Agent Model Center (MVP)

* **Initial Version Deployment: 39%**
* * Developed AI Agent training tools, including data preprocessing, training monitoring, and optimization strategies.
  * Provided custom hyperparameter tuning to help users optimize training and model performance.
  * Integrated automated training and model evaluation features for streamlined debugging and validation.
  * Implemented parallel training mechanisms to support large-scale training tasks efficiently.
* **Extension of Compatible Baseline Models: 23%**
* * Enhanced training workflows for DeepSeek V3 and other baseline models, optimizing data preprocessing, training configurations, and model tuning.
  * Developed automated model deployment features to simplify the production launch process.
  * Introduced real-time monitoring and feedback mechanisms to track model performance during training.
  * Improved inference efficiency for deployed models, ensuring high-performance operation in production environments.

### AI Agent MVP

* **Web 3.0 Mentor MVP:** First version completed
* **Second MVP:** Under research

Launch of Brainwave Distributed Database

* Deployment of NeuraMATRIX’s first ecosystem application, initiating data collection and anonymization processes: 17%

**Others： Submitted listing application to Bitfinex**

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