[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122867-en":3,"doc-seo-122867-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122867,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value Auctions","Blockchain technologies have intensified the extraction of market inefficiencies such as arbitrage via Miner Extractable Value (MEV) in DeFi smart contracts. When MEV payments (“bribes”) move from the public transaction pool to Flashbots-like private channels, auctions become sealed and highly competitive first-price MEV auctions emerge. The study analyzes transaction behavior across Flashbots’ operational period and builds machine learning models to forecast winning bids and improve profitability. Results show winning rates above 50% versus existing strategies, and demonstrate adaptive constant bidding advantages in sandwich MEV auctions.","Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value Auctions  \nChristoffer Raun∗ ETH Zurich Switzerland  \nchristoffer.raun@inf.ethz.ch  \nKaihua Qin Imperial College London  \nUnited Kingdom [kaihua.qin@imperial.ac.uk](kaihua.qin@imperial.ac.uk)  \nBenjamin Estermann∗  \nETH Zurich Switzerland [estermann@ethz.ch](estermann@ethz.ch)  \nRoger Wattenhofer ETH Zurich Switzerland  \n[wattenhofer@ethz.ch](wattenhofer@ethz.ch)  \nLiyi Zhou  \nImperial College London United Kingdom [liyi.zhou@imperial.ac.uk](liyi.zhou@imperial.ac.uk)  \nArthur Gervais  \nUniversity College London United Kingdom [a.gervais@ucl.ac.uk](a.gervais@ucl.ac.uk)  \nYe Wang University of Macau  \nChina [wangye@um.edu.mo](wangye@um.edu.mo)  \nABSTRACT  \nThe emergence of blockchain technologies as central components of financial frameworks has amplified the extraction of market inefficiencies, such as arbitrage, through Miner Extractable Value (MEV) from Decentralized Finance (DeFi) smart contracts. Exploiting these opportunities often requires fee payment to miners and validators, colloquially termed as bribes. The recent development of centralized MEVrelayers has led to these payments shifting from the public transaction pool to private channels, with the objective of mitigating information leakage and curtailing execution risk. This transition instigates highly competitive first-price auctions for MEV. However, effective bidding strategies for these auctions remain unclear.  \nThis paper examines the bidding behavior of MEV bots using Flashbots’ private channels, shedding light on the opaque dynamics of these auctions. We gather and analyze transaction data for the entire operational period of Flashbots, providing an extensive view of the current Ethereum MEV extraction landscape. Additionally, we engineer machine learning models that forecast winning bids whilst increasing profitability, capitalizing on our comprehensive transaction data analysis. Given our unique status as an adaptive entity, the findings reveal that our machine learning models can secure victory in more than 50% of Flashbots auctions, consequently yielding superior returns in comparison to current bidding strategies in arbitrage MEV auctions. Furthermore, the study highlights the relative advantages of adaptive constant bidding strategies in sandwich MEV auctions.  \n∗ Both authors contributed equally to the paper  \n1 INTRODUCTION  \nDeFi has become a significant catalyst for recent blockchain adoption, transitioning trading activities from centralized intermediaries like custodians, banks, and brokers to transparent and immutable on-chain smart contracts. DeFi offers a broad range of financial products, such as borrowing and lending [20], exchanges [4], and leveraged trading [1] .  \nJust as in traditional finance, High Frequency Trading (HFT) opportunities such as arbitrage and liquidation have emerged within DeFi. This evolving DeFi landscape is primarily driven by the competition for MEV among market participants [9] . MEV represents the potential profit achievable by exploiting financial opportunities within a blockchain through transactions [25] . Because blockchain operations are entirely transparent, all transactions and smart contracts are publicly accessible [7] . Additionally, due to the asynchronous nature of a blockchain’s Peer-to-Peer (P2P) network, there’s an inherent time delay between the initiation (e.g., the signing of a transaction) and execution (i.e., the mining) of a transaction [9] . This allows HFT traders to monitor transactions on the P2P network and infer their competitors’actions and reactions, turning HFT within DeFi into a highly competitive game [38] .  \nMiners, motivated by financial gain, prioritize transactions that offer the highest bribe per unit of computation (also known as a bid). Consequently, DeFi users engage \"MEV auctions\" to gain an advantage through front-running [9] . The growing demand forMEV has led to increased fees, inefficient tra","cbCaigLJqLoQribz","https://ap.wps.com/l/cbCaigLJqLoQribz","pdf",22154648,1,31,"English","en",105,"# Abstract\n# Introduction\n## MEV, DeFi, and MEV Auctions\n## Flashbots Private Channels and Sealed Bidding\n# Machine Learning for Winning Bids\n## Prediction Goals and Model Inputs","[{\"question\":\"What problem does the paper address in MEV auctions?\",\"answer\":\"The paper examines why effective bidding strategies for sealed, first-price MEV auctions remain unclear once MEV payments shift to private channels like Flashbots’ relays.\"},{\"question\":\"How do the authors study bidding behavior in the Flashbots setting?\",\"answer\":\"They gather and analyze transaction data across Flashbots’ operational period, focusing on submitted transaction types, timing, and miner bribes (payments to validators).\"},{\"question\":\"How do the proposed machine learning models perform and what do they optimize?\",\"answer\":\"The models forecast winning bids to increase profitability, achieving victories in more than 50% of Flashbots auctions and producing higher returns than current arbitrage MEV bidding strategies, with advantages for adaptive constant bidding in sandwich MEV auctions.\"}]","Leveraging Machine Learning for Bidding Strategies in Miner Extractable Value Auctions | 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problem does the paper address in MEV auctions?","Question",{"text":75,"@type":76},"The paper examines why effective bidding strategies for sealed, first-price MEV auctions remain unclear once MEV payments shift to private channels like Flashbots’ relays.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the authors study bidding behavior in the Flashbots setting?",{"text":80,"@type":76},"They gather and analyze transaction data across Flashbots’ operational period, focusing on submitted transaction types, timing, and miner bribes (payments to validators).",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed machine learning models perform and what do they optimize?",{"text":84,"@type":76},"The models forecast winning bids to increase profitability, achieving victories in more than 50% of Flashbots auctions and producing higher returns than current arbitrage MEV bidding strategies, with advantages for adaptive constant bidding in sandwich MEV 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