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Briefing

The core research problem is the systemic risk of Maximal Extractable Value (MEV) in Automated Market Makers (AMMs), which incentivizes block producers to centralize transaction ordering and extract value at the expense of users. The foundational breakthrough is the proposal of a novel AMM mechanism that processes all transactions within a block as a single batch, governed by a rule that maintains a constant potential function. This application-layer mechanism design solution is proven to satisfy arbitrage resilience on legacy blockchains and, critically, strategy proofness when combined with sequencing fairness. The single most important implication is the demonstration of a new theoretical paradigm ∞ that provable guarantees against harmful MEV can be achieved at the smart contract level, circumventing the perceived impossibilities of solving the problem solely within the consensus protocol.

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Context

Prior to this work, the prevailing theoretical limitation was the belief that MEV mitigation required fundamental, often complex, changes to the underlying consensus protocol, such as Proposer-Builder Separation (PBS) or sophisticated order-fairness mechanisms. The core academic challenge was the impossibility of fully eliminating MEV at the consensus level while maintaining other desirable properties. The centralized control over transaction sequencing inherent in mainstream consensus protocols created a persistent, risk-free profit opportunity for block producers, which fostered an off-chain ecosystem that actively undermines the decentralization goal.

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Analysis

The paper’s core mechanism re-architects the AMM’s state transition function. Instead of processing transactions sequentially, the mechanism aggregates all transactions in a block into a single batch and applies a set of pre-defined rules. The key logic is the enforcement of a constant potential function across the batch execution.

This function ensures that the state change from the beginning to the end of the batch does not allow any single actor, including the block producer, to extract risk-free arbitrage profit. This approach fundamentally differs from previous methods by shifting the MEV defense from a sequencing problem at the consensus layer to a mechanism design problem at the application layer, guaranteeing that the honest strategy is the best response for individual users under fair sequencing.

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Parameters

  • Arbitrage Resilience ∞ A mathematical guarantee that a miner/block producer cannot extract risk-free profit from the mechanism, proven for legacy single-proposer blockchains.
  • Strategy Proofness ∞ A strictly stronger guarantee ensuring an individual user’s best response is to follow the honest strategy, proven under sequencing-fairness assumptions.
  • Constant Potential Function ∞ The mathematical rule maintained by the batch-processing mechanism that prevents value extraction by ensuring the mechanism’s state remains consistent.

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Outlook

This research opens a new avenue for decentralized application (dApp) developers to build provably MEV-resistant protocols, shifting the burden of fairness from the protocol’s core consensus to its smart contract logic. In the next 3-5 years, this theoretical foundation will likely lead to a new generation of DeFi primitives ∞ such as AMMs, lending protocols, and oracles ∞ that are strategy-proof by construction. The research also provides a formal mathematical articulation of how sequencing fairness, a property of next-generation consensus, can be leveraged to achieve the strongest possible incentive compatibility at the application layer, thereby accelerating the deployment of fair-sequencing protocols.

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Verdict

This work establishes a critical new principle for blockchain architecture, proving that application-layer mechanism design can achieve incentive compatibility and eliminate systemic MEV, a guarantee previously thought to be exclusive to the consensus layer.

Mechanism design, Automated market makers, Strategy proofness, Arbitrage resilience, Maximal extractable value, Application layer solution, Constant potential function, Batch transaction processing, Decentralized finance, Transaction ordering fairness, Consensus-level problem, On-chain centralization, Game theoretic model, Permissionless blockchain, Risk-free profit, Sequential game, Equilibrium analysis, Welfare implications, Sequencing fairness, Smart contract design. Signal Acquired from ∞ arxiv.org

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constant potential function

Definition ∞ A constant potential function is a mathematical construct utilized in automated market makers (AMMs) and decentralized exchanges (DEXs) to maintain a specific invariant across liquidity pools.

consensus protocol

Definition ∞ A consensus protocol is a set of rules and procedures that distributed network participants follow to agree on the validity of transactions and the state of the ledger.

potential function

Definition ∞ Potential Function describes the inherent capabilities or possible applications that a system, protocol, or asset possesses, which may not yet be fully developed or utilized.

application layer

Definition ∞ The Application Layer refers to the topmost layer of a network architecture where user-facing applications and services operate.

arbitrage resilience

Definition ∞ Arbitrage resilience refers to a system's capacity to withstand and maintain stability amidst opportunistic price discrepancies across different markets.

strategy proofness

Definition ∞ Strategy Proofness refers to a property of a mechanism or system where participants cannot achieve a better outcome by misrepresenting their true preferences or actions.

mechanism

Definition ∞ A mechanism refers to a system of interconnected parts or processes that work together to achieve a specific outcome.

incentive compatibility

Definition ∞ Incentive Compatibility describes a system design where participants are motivated to act truthfully and in accordance with the system's rules, even if they could potentially gain by misbehaving.

mechanism design

Definition ∞ Mechanism Design is a field of study concerned with creating rules and incentives for systems to achieve desired outcomes, often in situations involving multiple participants with potentially conflicting interests.