AI agents improve cross-chain arbitrage with adaptive trading paths
When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy
Cryptography and SecurityComputational Engineering, Finance, and Science
Summary
Making money by trading price differences between blockchains can be tricky and slow due to delays and risks. This paper looks at how autonomous AI agents do this cross-chain trading more efficiently than traditional bots or humans. The authors create a smart method for choosing the best trading routes and sizes to maximize profits and reduce risks. They tested their approach on real Ethereum and related chains data and found it works better and can lower the chances of value being unfairly taken by others.
What this means in practice
- •For cryptocurrency traders: Use AI-driven adaptive trade routing to improve profits and lower risks during cross-chain arbitrage between popular blockchains.
- •For blockchain infrastructure teams: Implement adaptive path selection in multi-chain systems to reduce Maximal Extractable Value exploitation and improve transaction fairness.
Authors
Wei Ye, Jingyan Xu, Yuanhong Wu
Abstract
We study cross-chain arbitrage when autonomous AI agents, rather than humans or bots, are the searchers. We model agents as both arbitrage extractors and Maximal Extractable Value targets, derive the optimal trade size for a risk-averse agent under mean-variance utility with stochastic bridge delays, and formalize multi-chain path selection as a belief-weighted online learning problem whose belief estimates converge under a Robbins-Monro schedule. Using 23,000 Uniswap V3 swap events across Ethereum, Arbitrum, and Base, we find that Ethereum-Arbitrum price gaps average 0.044% at 10-second resolution and Arbitrum--Base gaps average 0.013%, so $10,000 trades clear in 63% of L2-L2 windows via CCTP while L1-L2 routes require $50,000 or more for comparable viability. Our adaptive path-selection algorithm outperforms standard baselines by 11% on average, and moderate randomization cuts MEV exposure by over 50% with only modest profit loss.