Briefing

The foundational problem of Maximal Extractable Value (MEV) centers on sub-optimal transaction ordering policies that lead to a latency-based arms race for value extraction, predominantly benefiting searchers and validators. This research proposes and analyzes a new mechanism, Time-Advantaged Arbitrage (Timeboost), which auctions a guaranteed time window for transaction inclusion, fundamentally decoupling MEV extraction from raw execution speed. The breakthrough is the formal, game-theoretic proof that this system shifts the extraction dynamic from a competitive race to a calculated economic decision, enabling liquidity pools (AMMs) to adapt their fee structures to capture a significant portion of the arbitrage value, which is the single most important implication for future DeFi architecture and fair value distribution.

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Context

Prior to this work, transaction ordering in decentralized systems primarily relied on two models → First-Come-First-Serve (FCFS), which incentivizes infrastructure investment for latency advantage, or Priority Gas Auctions (PGAs), which lead to a competitive bidding war for immediate inclusion. Both models fail to efficiently capture the full value of the ordering preference for the protocol or liquidity provider, allowing the majority of arbitrage and liquidation profits to be extracted by sophisticated, low-latency searchers. This established limitation resulted in a constant, high-cost, zero-sum game that eroded network fairness and stability.

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Analysis

The core mechanism, Timeboost, operates by allowing a single actor to purchase exclusive access to a time advantage ($T_w$) for transaction submission, guaranteeing their transactions are sequenced ahead of all others for a defined interval. This fundamentally changes the arbitrageur’s strategy from a continuous race to a discrete, optimal-timing problem modeled using dynamic programming. The new primitive is a sequential game where the liquidity pool first sets an adaptive fee structure, and the time-advantaged arbitrageur then best responds by choosing the optimal moment to submit their extraction transaction. This mechanism allows the AMM to become an active participant in MEV capture, directly claiming value based on the time-advantaged transaction’s characteristics, thereby internalizing a portion of the external value that was previously lost to searchers.

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Parameters

  • Arbitrageur Value Share → 50% (The percentage of the total arbitrage value captured by the time-advantaged arbitrageur in the game’s economic equilibrium, assuming the pool adapts its fee structure.)
  • Liquidity Pool Value Share → 25% (The percentage of the total arbitrage value captured by the Automated Market Maker pool when it optimally adjusts its fee structure to the time advantage.)
  • Extraction Type Focus → Arbitrage Opportunities (The study focuses on price discrepancies between an AMM and an external market, which is one of the largest sources of MEV today.)

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Outlook

The Timeboost model introduces a new dimension to mechanism design, suggesting that future blockchain architectures will move beyond simple fee-based or FCFS ordering toward more complex, time-based auction primitives. This research opens new avenues for liquidity protocols to integrate active MEV capture directly into their smart contract logic, transforming them from passive victims of arbitrage into active economic agents. In the next three to five years, this principle could be generalized to other forms of MEV, such as liquidations, leading to a new class of “MEV-aware” DeFi protocols that capture a higher percentage of transaction value, ultimately improving capital efficiency and user experience.

The Time-Advantaged Arbitrage mechanism provides a robust, economically sound primitive for transaction ordering, proving that MEV can be efficiently captured and redistributed by foundational DeFi protocols.

transaction ordering mechanism, maximal extractable value, MEV capture, arbitrage strategy, automated market maker, AMM fee structure, sequencer policy, time advantage auction, economic equilibrium, dynamic programming model, latency competition, value distribution Signal Acquired from → arxiv.org

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