Technical Architecture

The MOEW AI Agent utilizes the most popular AI technical architectures and tools orchestrated and optimized for crypto space. We believe that technology has no borders. We have implemented and have a plan to integrate the following technologies for the MOEW AI Agent. We will keep exploring the hottest and advanced technologies, leverage their strengths with the core value of freedom, equality, peace and love.

Base LLM (Large Language Models)

The LLM models we use include but are not limited to the following options:

• GPT-4o

• LLaMA 70b

• Claude 3.5 Sonnet

• DeepSeek R1

Trusted Execution Environment (TEE):

• Secure Data Processing: TEE creates an isolated and secure enclave within the device, ensuring that sensitive data, such as private keys and transaction details, remain protected even if the device is compromised, thus supporting AI agents in maintaining autonomous control over operations.

• Encrypted Communications: By facilitating encrypted channels within the TEE, it allows AI agents to securely communicate with other network participants and ensure data privacy. This helps in executing transactions that are both autonomous and secure from external threats.

• Privacy Protection Protocols: TEE supports robust privacy protection mechanisms, ensuring that sensitive AI agent actions are concealed from unauthorized access, enabling agents to perform operations with a high degree of autonomy and confidentiality.

• AI Agent Autonomous Control: The TEE provides a secure environment for AI agents to execute operations independently, ensuring that actions are protected from external interference and data integrity is maintained, offering autonomous control over digital asset management.

• Verifiable On-chain Transactions: TEE ensures that all transactions processed by AI agents are securely logged and verifiable on the blockchain, maintaining transparency and integrity of the transaction process, thus enhancing trust in digital asset exchanges.

Data Plugin and Workflow Integration

• Comprehensive Real-time market data and news feeds

• Powerful Social media and Community sentiment analysis

MCP (Model Context Protocol) technology

• Enhanced context awareness: Improves AI's understanding of context by allowing access to current data and specialized tools.

• Standardized interaction: Establishes a common language for AI models and external tools, promoting interoperability.

• Simplified development: Reduces the need for custom integrations, streamlining AI application development.

• Lower computational load: Eliminates the need for embeddings and vector searches, leading to improved efficiency and lower costs.

• Scalability: Allows easy connection of new tools and data sources without extensive code changes.

RAG (Retrieval-Augmented Generation) and long-term memory

• RAG allows AI models to access external knowledge sources, improving factual consistency and reducing "hallucinations". RAG with long-term memory employs advanced techniques for dynamically storing, retrieving, and updating information.

• RAG architecture can be enhanced with long-term memory capabilities, enabling AI assistants to retain knowledge over extended periods

• This technology enables AI systems to remember past conversations and user preferences, improving experiences in applications like customer support and personal assistants.

Reinforcement Learning

• Behavior optimization: exploring Value-based Methods, Policy-based Methods and Actor-Critic Methods.

• Decision-making improvement: leveraging Model-based Reinforcement Learning, Exploration Strategies, etc.

• Adaptive response systems: Implementing Hierarchical Reinforcement Learning, Reward Shaping and Transfer Learning in Reinforcement Learning.

Fine-tuning Systems

• More unified and customized personality development for MOEW: including Leveraging supervised fine-tuning and transferring learning to align MOEW's responses with a consistent, branded personality, and incorporating community feedback loops to iteratively refine tone and interaction patterns based on user preferences.

• Better Multimodal and natural language understanding and generation for crypto space: including applying domain-specific fine-tuning to improve comprehension of crypto jargon, tokenomics, and market trends, and combining multi-task learning for simultaneous text, code, and visual data processing (e.g., charts, memes).

• Community feedback integration: includes employing prompt tuning to dynamically adjust responses based on real-time community interactions, integrating partial fine-tuning strategies to prioritize high-impact parameter updates from user feedback.