# When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy

arXiv: https://arxiv.org/abs/2609.17897 · HTML: https://mlsanity.com/paper/2609.17897/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 19 references that are in the map.
- First posted on arXiv 15 Sep 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 14 of 14)

- [Quantifying Blockchain Extractable Value: How dark is the forest?](https://mlsanity.com/paper/2101.05511/) (2021)
- [Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries](https://mlsanity.com/paper/2601.04583/) (2026)
- [The Agent Economy: A Blockchain-Based Foundation for Autonomous AI Agents](https://mlsanity.com/paper/2602.14219/) (2026)
- [Eliminating Sandwich Attacks with the Help of Game Theory](https://mlsanity.com/paper/2202.03762/) (2022)
- [Automated Market Making and Arbitrage Profits in the Presence of Fees](https://mlsanity.com/paper/2305.14604/) (2023)
- [Time to Bribe: Measuring Block Construction Market](https://mlsanity.com/paper/2305.16468/) (2023)
- [Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm](https://mlsanity.com/paper/2507.08249/) (2025)
- [MEV Makes Everyone Happy under Greedy Sequencing Rule](https://mlsanity.com/paper/2309.12640/) (2023)
- [Non-Atomic Arbitrage in Decentralized Finance](https://mlsanity.com/paper/2401.01622/) (2024)
- [Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance](https://mlsanity.com/paper/2501.17335/) (2025)
- [Unity is Strength: A Formalization of Cross-Domain Maximal Extractable Value](https://mlsanity.com/paper/2112.01472/) (2021)
- [Ethereum's Proposer-Builder Separation: Promises and Realities](https://mlsanity.com/paper/2305.19037/) (2023)
- [The Walls Have Ears: Unveiling Cross-Chain Sandwich Attacks in DeFi](https://mlsanity.com/paper/2511.15245/) (2025)
- [Sandwiched and Silent: Behavioral Adaptation and Private Channel Exploitation in Ethereum MEV](https://mlsanity.com/paper/2512.17602/) (2025)

## Abstract (arXiv)

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.