1st Report Of June 2025
June 10, 2025
Last updated
June 10, 2025
Last updated
Validator Nodes: 104 Miner Nodes: 1,069 New Wallets: +97
Optimization of Morpheus as the Base Large Model for AI Agents: 89%
All tuning tasks are complete. Recent efforts focused on refining the interaction flow between Morpheus and integrated tools based on testing from the first internal Agent application. Integration with the Agent Model Center remains the final technical step before full
deployment.Language Understanding, Reasoning, and Communication Abilities: 60%
Core capabilities have been developed and are undergoing integration with upcoming personality modules to enable dynamic, human-like interaction in agents.
Integration of DeepSeek V3’s MOE Model Framework: 87%
Framework remains stably integrated, supporting efficient multi-expert routing within Morpheus to improve training and inference performance.
Expansion of the MANTA Architecture for Privacy-Preserving Agent Training and Inference: 0% Optimization of MANTA for DeepSeek V3 MOE Model Requirements: 0%
Initial Version Deployment: 96%
System interface and user interaction workflows have been further optimized. The Agent Center now supports streamlined development of modular agents using MATRIX-provided tools.
Extension of Compatible Baseline Models – Integration of DeepSeek V3: 85%
DeepSeek V3 is maintained as a baseline model, enabling enhanced training pipelines for agents within the platform.
Version Based on Morpheus: 80%
The first version of the MAC (Morpheus-based Agent Core) has been successfully developed and deployed. Demonstrates practical use cases for Morpheus in end-user-facing intelligent agents.
Deployment of NeuraMATRIX’s First Ecosystem Application & Data Collection: 17%
Ongoing data collection and anonymization continue as foundational infrastructure for EEG-driven agent training and real-time adaptation.
Development and Deployment of Contextus v1 Completed The first version of Contextus, the contextual task orchestration engine, is now live—enabling deeper semantic understanding and more effective multi-agent coordination.
