The study proposes a quote-driven predictive automated market maker (AMM) platform with on-chain custody and settlement functions, alongside off-chain predictive reinforcement learning capabilities to improve liquidity provision of real-world AMMs. The proposed AMM architecture is an augmentation to the Uniswap V3, a cryptocurrency AMM protocol, by utilizing a novel market equilibrium pricing for reduced divergence and slippage loss. Further, the proposed architecture involves a predictive AMM capability, utilizing a deep hybrid Long Short-Term Memory (LSTM) and Q-learning reinforcement learning framework that looks to improve market efficiency through better forecasts of liquidity concentration ranges, so liquidity starts moving to expected concentration ranges, prior to asset price movement, so that liquidity utilization is improved. The augmented protocol framework is expected have practical real-world implications, by (i) reducing divergence loss for liquidity providers, (ii) reducing slippage for crypto-asset traders, while (iii) improving capital efficiency for liquidity provision for the AMM protocol. To our best knowledge, there are no known protocol or literature that are proposing similar deep learning-augmented AMM that achieves similar capital efficiency and loss minimization objectives for practical real-world applications.

翻译：本研究提出了一种报价驱动的预测性自动做市商（AMM）平台，该平台具备链上托管与结算功能，并集成链下预测性强化学习能力，以改善现实世界中自动做市商的流动性供应。所提出的自动做市商架构是对加密货币自动做市商协议Uniswap V3的增强改进，通过采用创新的市场均衡定价机制来降低偏离损失与滑点损失。此外，该架构包含基于深度混合长短期记忆（LSTM）与Q学习强化学习框架的预测性自动做市能力，通过更精准地预测流动性集中区间来提升市场效率——这使得流动性能够在资产价格变动前提前转移至预期集中区间，从而优化流动性利用率。该增强协议框架预计具有实际应用价值，具体表现为：（i）降低流动性提供者的偏离损失，（ii）减少加密资产交易者的滑点损失，同时（iii）提高自动做市商协议的流动性供应资本效率。据我们所知，目前尚无协议或文献提出类似采用深度学习增强的自动做市商方案，能够在现实应用中实现同等的资本效率提升与损失最小化目标。