Адаптивні гібридні ролапи: інтелектуальна маршрутизація між ZK та оптимістичною верифікацією

Abstract

This article examines the limitations of existing hybrid rollup solutions and presents an adaptive L2 architecture model that leverages artificial intelligence mechanisms. It is shown that current approaches to combining optimistic and ZK verification are largely based on static rules or manual mode selection, which prevents them from effectively accounting for load dynamics, risk profiles, and domain-specific properties of applications. Based on an analysis of optimistic, ZK, and hybrid rollups, an adaptive hybrid rollup model with AI-based transaction routing is proposed. This model combines transaction classification, GNN-based decision making, LSTM-based network condition forecasting, a dual-path execution system, and a continuous learning module. The article describes a Predictive Routing Algorithm that performs proactive selection between ZK and optimistic paths, taking into account cost, latency, security, and risk profile, as well as a Dynamic Resources Allocation mechanism that dynamically redistributes resources between the paths. The proposed multi-criteria optimization framework demonstrates the ability to tune objective weights to the specifics of different classes of DeFi and Web3 protocols. It is shown that the implementation of such a model is promising for systems with high transactional intensity, as it enables a shift from manual configurations to automated, data-driven policies for resource and risk management in hybrid rollup architectures.