当AI代理遇上MEV：代理经济中的跨链套利
When AI Agents Meet MEV: Cross-Chain Arbitrage in the Agentic Economy
浏览论文内容
中文总结 AI 辅助
本研究针对AI代理作为搜索者的跨链套利问题，提出最优交易规模模型与自适应路径选择算法，利用真实数据验证，该算法平均优于基线11%，并显著降低MEV暴露。
中文摘要 AI 辅助
我们研究了当自主AI代理（而非人类或机器人）作为搜索者时的跨链套利问题。我们将代理同时建模为套利提取者和最大可提取价值（MEV）目标，在均值-方差效用和随机桥接延迟下推导了风险厌恶代理的最优交易规模，并将多链路径选择形式化为一个信念加权在线学习问题，其信念估计在Robbins-Monro调度下收敛。利用以太坊、Arbitrum和Base上23,000个Uniswap V3交换事件，我们发现以太坊-Arbitrum价格差距在10秒分辨率下平均为0.044%，Arbitrum-Base差距平均为0.013%，因此10,000美元的交易在63%的L2-L2窗口中通过CCTP完成，而L1-L2路线需要50,000美元或更多才能达到相当的可行性。我们的自适应路径选择算法平均比标准基线高出11%，适度随机化可将MEV暴露减少超过50%，且仅带来适度的利润损失。
英文摘要
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.
发表机构
- Fordham University（福特汉姆大学）
机构由 AI 辅助整理，请以论文原文为准。
