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arXiv · 2609.27424

EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harness

Yan Wen·Zichun Cai·Iliya Mirzaei·Xiaohua Cai·Mohammad Javad Amiri·Haoxian Chen·Chenyuan Wu
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Abstract

Maximal Extractable Value (MEV) has evolved into a major economic force in blockchain ecosystems, yet its capture is dominated by experienced teams, and both strategy design and implementation rely on manual expert work that scales poorly across heterogeneous protocols and chains. We present EVAGE, the first fully autonomous multi-agent framework for end-to-end MEV strategy generation and adaptation. Equipped with three specialized operation modes, it automatically discovers novel MEV variants, adapts execution logic across disparate protocols, and ports strategies between chains, including Layer-1 and Layer-2 networks. To avoid inference latency on the critical MEV execution path, EVAGE generates and refines MEV bot code offline rather than making real-time decisions directly. Under the coordination of an orchestrator agent, three specialized subagents collectively implement and repair the full MEV bot workflow via closed-loop diagnostics, eliminating human intervention while producing validated and deterministic Proof-of-Concept implementations. We evaluate EVAGE on over 1.5M blocks from each of Ethereum, Base, and BNB Smart Chain (BSC). On Ethereum, EVAGE uncovers five novel MEV strategy variants, yielding a profit increase of 1.02$\times$ to 15.97$\times$. It also successfully adapts 11 MEV strategies from CPMM to both CLMM and Balancer V2 and ports strategies from Ethereum to Base and BSC, all with less than 60 dollars in LLM token costs.

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Yan Wen, Zichun Cai, Iliya Mirzaei, Xiaohua Cai, Mohammad Javad Amiri, Haoxian Chen, Chenyuan Wu. 2026-09-23. EVAGE: Autonomous MEV Generation and Adaptation via Multi-Agent Harness. https://arxiv.org/abs/2609.27424

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