  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.
