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RediSwap: MEV Redistribution Mechanism for CFMMs

Mengqian Zhang, Sen Yang, Fan Zhang

TL;DR

RediSwap features an MEV-redistribution mechanism that manages MEV opportunities arising from the rebalancing arbitrage and pending transactions within the AMM pool, and offers a new framework for MEV redistribution in decentralized finance.

Abstract

Automated Market Makers (AMMs) are essential to decentralized finance, offering continuous liquidity and enabling intermediary-free trading on blockchains. However, participants in AMMs are vulnerable to Maximal Extractable Value (MEV) exploitation. Users face threats such as front-running, back-running, and sandwich attacks, while liquidity providers (LPs) incur the loss-versus-rebalancing (LVR). In this paper, we introduce RediSwap, a novel AMM designed to capture MEV at the application level and refund it fairly among users and liquidity providers. At its core, RediSwap features an MEV-redistribution mechanism that manages arbitrage opportunities within the AMM pool. We formalize the mechanism design problem and the desired game-theoretical properties. A central insight underpinning our mechanism is the interpretation of the maximal MEV value as the sum of LVR and individual user losses. We prove that our mechanism is incentive-compatible and Sybil-proof, and demonstrate that it is easy for arbitrageurs to participate. We empirically compared RediSwap with existing solutions by replaying historical AMM trades. Our results suggest that RediSwap can achieve better execution than UniswapX in 89% of trades and reduce LPs' loss to under 0.5% of the original LVR in most cases.

RediSwap: MEV Redistribution Mechanism for CFMMs

TL;DR

RediSwap features an MEV-redistribution mechanism that manages MEV opportunities arising from the rebalancing arbitrage and pending transactions within the AMM pool, and offers a new framework for MEV redistribution in decentralized finance.

Abstract

Automated Market Makers (AMMs) are essential to decentralized finance, offering continuous liquidity and enabling intermediary-free trading on blockchains. However, participants in AMMs are vulnerable to Maximal Extractable Value (MEV) exploitation. Users face threats such as front-running, back-running, and sandwich attacks, while liquidity providers (LPs) incur the loss-versus-rebalancing (LVR). In this paper, we introduce RediSwap, a novel AMM designed to capture MEV at the application level and refund it fairly among users and liquidity providers. At its core, RediSwap features an MEV-redistribution mechanism that manages arbitrage opportunities within the AMM pool. We formalize the mechanism design problem and the desired game-theoretical properties. A central insight underpinning our mechanism is the interpretation of the maximal MEV value as the sum of LVR and individual user losses. We prove that our mechanism is incentive-compatible and Sybil-proof, and demonstrate that it is easy for arbitrageurs to participate. We empirically compared RediSwap with existing solutions by replaying historical AMM trades. Our results suggest that RediSwap can achieve better execution than UniswapX in 89% of trades and reduce LPs' loss to under 0.5% of the original LVR in most cases.

Paper Structure

This paper contains 37 sections, 7 theorems, 24 equations, 9 figures, 3 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. This Work
  3. Our Contributions
  4. Related Work
  5. Model
  6. The Basic Model
  7. MEV-Redistribution Mechanism
  8. Desired Properties
  9. Optimal MEV with Public Orders
  10. Optimal MEV Strategy
  11. Optimal MEV = LVR + Each User's Loss
  12. Strawman Design
  13. Mechanism Description
  14. Analysis of Strawman Mechanism
  15. Our Mechanism
  16. ...and 22 more sections

Key Result

Theorem 1

The arbitrageur's maximal MEV is $\phi(s_0, v^*) + \sum_{j\in[m]} V(\textsf{TX}_j)$. Furthermore, Algorithm alg:optimalMEV can construct the bundle that obtains the maximal MEV in polynomial time.

Figures (9)

  • Figure 1: An illustration of the optimal MEV strategy for \ref{['ex:optimalMEV']}. Blue dotted lines represent pending transactions from users, while red solid lines represent MEV transactions from the arbitrageur. The transactions in each subgraph form an MEV bundle. Pool states $(x,y)$ in the figure are shown in ascending order based on the value of $x$.
  • Figure 2: The bundle constructed by our mechanism for \ref{['ex:ourmechanism']}.
  • Figure 3: Percentage of orders with execution prices better than those on UniswapX or CoWSwap. Each marker represents the overall result under different settings of arbitrageurs and swap fees.
  • Figure 4: Candlestick charts of Binance and UniswapX ETH/USDT prices over time. Missing data indicates that our dataset does not contain relevant orders on UniswapX for those days.
  • Figure 5: The CDF of LVR reduction for WETH-USDC and WETH-USDT using RediSwap for different numbers of arbitrageurs and price distributions. A smaller LVR reduction ratio on the x-axis indicates greater reduction efficiency.
  • ...and 4 more figures

Theorems & Definitions (29)

  • Definition 1: Bundle Generation Rule
  • Definition 2: Payment Rule
  • Definition 3: Refund Rule
  • Definition 4: MEV-Redistribution Mechanism
  • Definition 5: Arbitrageur Utility Function
  • Definition 6: Truthful
  • Definition 7: User Utility Function
  • Definition 8: Sybil-proof
  • Theorem 1
  • Example 1
  • ...and 19 more