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Title: SQHWYD CTO Anya Volkov Outlines Reinforcement Learning-Based Architecture for Cross-Chain Liquidity

Anya Volkov, Chief Technology Officer of SQHWYD GLOBAL Ltd., today presented the technical framework behind the company’s core architecture, the Unity Layer(TM). The presentation focused on the application of distributed systems and machine learning to manage cross-chain liquidity and addressed current technical challenges within digital asset infrastructure.During the technical briefing, Volkov noted that the security and stability of cross-chain bridging protocols remain key focus areas for the industry. She stated that system architecture choices significantly impact asset security and transaction reliability. SQHWYD's technical approach focuses on optimizing resource scheduling through fundamental architecture design rather than relying solely on application-level interface aggregation.According to the released technical documentation, the Unity Layer(TM) deploys deep reinforcement learning models designed to dynamically analyze and optimize routing paths across Centralized Finance (CeFi) and Decentralized Finance (DeFi) protocols . The system aims to aggregate fragmented liquidity sources to improve the efficiency of order execution.Regarding system performance and data consistency, Volkov described the platform's adoption of a microservices architecture and in-memory processing technologies. The architecture utilizes asynchronous message queues, such as Apache Kafka, to manage high-concurrency transaction requests, with the objective of maintaining data durability and consistency under high-load environments .On the topic of security, Volkov emphasized the integration of cryptographic frameworks into the transaction lifecycle. SQHWYD utilizes Multi-Party Computation (MPC) wallet technology. This approach distributes key shards across different nodes for collaborat...

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