AI Models & Mathematical Framework

Supervised Learning Models These models are trained on labeled historical data that includes past arbitrage events, market movements, and trade outcomes. By identifying recurring patterns such as price spreads, gas spikes, or liquidity shifts, these models can generalize and classify future scenarios as potentially profitable or not. This forms the foundation for real-time opportunity detection.

Reinforcement Learning LYNO employs reinforcement learning to continuously adapt and refine its arbitrage execution strategies. The protocol simulates a trading agent in various market environments and rewards outcomes that yield higher profitability with lower risk. Over time, the model learns to take optimal actions in dynamic environments through trial-and-error feedback loops.

Time Series Analysis Using statistical and deep learning techniques, LYNO models temporal price behaviors to predict short-term convergence or divergence across DEXs. This helps the protocol time entries and exits more effectively, enhancing arbitrage precision even under volatile conditions.

Graph Neural Networks (GNNs) GNNs are utilized to understand complex relationships across liquidity pools, tokens, and exchanges represented as nodes and edges. This enables LYNO to efficiently compute the best multi-hop trade paths within and across chains, optimizing both execution cost and slippage. These models operate in tandem to maximize the identification of arbitrage opportunities, reduce risk exposure, and enhance execution timing.

Execution Layer The Execution Layer implements the strategies determined by the AI layer through a series of smart contracts across multiple chains.

Settlement & Reporting Layer This layer handles the post-execution processes including profit distribution, reporting, and data feedback.