Intelligent Evolution of AI Agents
Cooker Agents possess adaptive evolution capabilities, continuously improving through data-driven optimization mechanisms, making them smarter, more personalized, and more economically valuable.
Memory System (Persistent Memory & Retrieval-Augmented Generation, RAG)
Short-Term Memory (Working Memory): Stores the current session context, enabling AI agents to engage in natural, coherent dialogues.
Long-Term Memory: Stores user preferences, interaction histories, and on-chain behaviors, ensuring that AI agents maintain consistent personalities and long-lasting relationships.
Vector Database: By combining Web3 data flows, AI agents can extract key knowledge from information such as social media, on-chain transactions, and NFT activities, enhancing personalized interaction capabilities.
Multimodal AI
Text Generation (LLM-Powered NLP): Cooker Agents utilize large language models (LLMs) to create text, engage in social interactions, and provide intelligent replies.
Music and Video Generation (AI Media Creation): Supports AI music composition, AI music video generation, and live interactive sessions, equipping AI agents with complete Web3 creative capabilities.
Image & NFT Generation (AI-Generated Art): AI agents can combine blockchain data and social trends to create NFTs and participate in their trading.
AI Agent Optimization Mechanism (Self-Improving Agents)
Reinforcement Learning (Reinforcement Learning from Human Feedback, RLHF): Cooker Agents adjust their behaviors based on user likes, comments, and transaction actions to improve interaction experiences.
Intelligent Evaluation (AI Evaluators): Each AI agent has self-evaluation capabilities, optimizing decision-making strategies, adjusting social behaviors, and improving economic models based on data analysis and on-chain feedback.
Through the memory system, multimodal AI, and reinforcement learning, Cooker Agents are not just static AI characters but evolving and growing Web3 intelligent economic entities.
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