Maria Silva
Maria is a Researcher at Matter Labs, working on various topics related to DeFi, MEV, and economics more broadly. Her interests include time-series data mining, network science, and applications of data science to finance and economics. She previously worked in the fraud detection and cyber crime spaces. She holds a Ph.D. in Information Management, where she used high-frequency telematics data to extract information about driving behavior.
Matter Labs; NOVA Information Management School
Session
There has been a growing interest in shared sequencing solutions, in which transactions for multiple rollups are processed together. Their proponents argue that these solutions allow for better composability and can potentially increase sequencer revenue by enhancing MEV extraction. However, little research has been done on these claims, raising the question of understanding the actual impact of shared sequencing on arbitrage profits, the most common MEV strategy in rollups. To address this, we develop a model to assess arbitrage profits under atomic execution across two Constant Product Market Marker liquidity pools and demonstrate that switching to atomic execution does not always improve profits. We also discuss some scenarios where atomicity may lead to losses, offering insights into why atomic execution may not be enough to convince arbitrageurs and rollups to adopt shared sequencing.