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Reinforcement learning optimizes MEV extraction on Polygon blockchain

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a reinforcement learning framework to optimize Maximal Extractable Value (MEV) extraction on the Polygon blockchain. This new approach addresses the challenges of dynamic, sub-second sealed-bid auctions, which are difficult for traditional game-theoretic models to handle. The system utilizes a PPO-based bidding agent that can adapt its strategy in real-time, demonstrating a significant improvement in profit capture compared to existing methods. AI

IMPACT This research could lead to more efficient and profitable MEV extraction strategies on blockchains, potentially impacting transaction ordering and network economics.

RANK_REASON The cluster contains an academic paper detailing a new methodology for blockchain MEV extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Reinforcement learning optimizes MEV extraction on Polygon blockchain

COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andrei Seoev, Leonid Gremyachikh, Anastasiia Smirnova, Yash Madhwal, Alisa Kalacheva, Dmitry Belousov, Ilia Zubov, Aleksei Smirnov, Denis Fedyanin, Vladimir Gorgadze, Yury Yanovich ·

    The Bidding Games: Reinforcement Learning for MEV Extraction on Polygon Blockchain

    arXiv:2510.14642v2 Announce Type: replace-cross Abstract: In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas A…

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