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Multipli Series 11/AI Engine's Continuous Learning Structure: Analyzing Multipli's Data Feedback Mechanism Finally, I have risen to the top gamer position in Multipli's @multiplifi 7D ranking. I jumped from 44th to 9th place last week! This is my method. Just like with Alora, when entering a long-term battle, I push through quietly at the beginning without worrying about not rising, just focusing on the CT series!! It always shows good results like this. (Though it can be tough on the body^^) This week, I need to keep running to receive crystals! ^^ Start ! Multipli's AI is building a "Feedback Learning System" that continuously evolves based on the market data that accumulates daily. In a way, it seems that all DeFi projects have a similar structure. Regardless, this structure will inevitably serve as a key driver to enhance the "Adaptability" that is central to DeFi operations. 1. Overview of Multipli's AI Learning Structure Multipli's AI engine operates based on a 4-stage feedback loop. 1-1: Data Collection (Data Input) It collects real-time data such as on-chain transaction information, pool liquidity, price fluctuations, and market sentiment. 1-2: Model Analysis (Model Processing) It patterns the collected data and detects abnormal movements. 1-3: Strategy Execution (AI Execution) It makes decisions on asset allocation, rebalancing, and liquidity movement based on the analysis results. 1-4: Result Evaluation (Feedback) It feeds the execution results (returns, losses, market reactions) back into the model learning data. By repeating this process, Multipli's AI will make increasingly optimized judgments in the market environment. However, this mechanism is very similar to the PDCA cycle for improving work in organizations ^^ (occupational hazard) 2. Role of the Feedback Mechanism Multipli's feedback system is designed as a learning structure where the AI corrects its own errors. For example, if the AI's asset movement at a certain point leads to a loss, it learns by tracing back the cause (market distortion, prediction error, etc.) and lowering the action weight for future actions under the same conditions. In other words, Multipli's AI continuously adjusts its risk-avoidance algorithm to avoid repeating failures. 3. Data Quality Management The AI's judgment ultimately depends on the quality of the data. Multipli places the utmost importance on accuracy and representativeness during the data collection process. To achieve this, three levels of data management systems have been introduced. Level 1: Automatic verification of on-chain data (removing anomalous transactions) Level 2: Filtering the reliability of external data sources (oracles, price feeds) Level 3: Evaluating the pattern similarity of current data against historical data Through this process, Multipli's AI maintains a focus on "quality" rather than "quantity" in data learning. 4. Application of Reinforcement Learning Multipli's learning structure is designed not as a traditional predictive model but as a reinforcement learning (Reward-Based Learning) structure. In other words, the AI evaluates rewards for each action in real-time and adjusts its strategy towards obtaining higher rewards. For example, If conservative asset allocation yields higher returns at a certain time --> strengthen the weight of that policy Conversely, if excessive exposure to volatility leads to losses --> automatically reduce that strategy This process allows the AI to evolve in a way that it understands the "nature of the market" and develops strategic thinking on its own. 5. Minimizing and Complementing Human Intervention Multipli's AI aims for an autonomous system, but, for risk management in complete automation, a human monitoring (Manual Oversight) layer also exists. *AI's judgment results are verified in real-time on-chain *Human review processes are conducted in parallel when anomalies are detected *Ultimately, model updates are reflected after community governance approval This structure can be referred to as a "Hybrid Trust Model" that combines the autonomy of AI with the verification power of the community. 6. Long-term Significance of the Feedback System Multipli's feedback structure holds great significance in that it implements a system where DeFi automation becomes smarter over time. Once this system has learned sufficiently, it can quickly detect market patterns, adjust risks faster than humans, and create sustainable profit curves. Conclusion Multipli's AI engine is a continuous learning asset management system that improves its own judgment based on data. Through this structure, Multipli is building the foundation of an autonomous DeFi ecosystem where AI can judge, learn, and evolve on its own without being swayed by short-term market fluctuations.

The last workshop of the week! We’ll learn how to fetch real-time pricing data using Pyth and explore how their architecture works. Pyth Price Feeds: Pulling the Data you Deserve 📅 November 6, 3 PM UTC 📍 Live on X This could be the week’s best, we'll see 👀