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Does Timeboost Reduce MEV-Related Spam? Theory and Evidence from Layer-2 Transactions

Agostino Capponi, Brian Zhu

TL;DR

The paper tackles MEV-related spam induced by FCFS sequencing on Layer-2s by proposing Timeboost, an Express Lane mechanism that auctions a time advantage. It develops a theory comparing baseline FCFS spam with a Timeboost-augmented environment, deriving unique equilibria where winning traders submit more copies while losers scale back, and showing that the auction reduces overall spam and increases sequencer revenue. The empirical analysis uses cross-chain difference-in-differences and a within-Arbitrum study to show a sizable drop in repeated transactions and a rise in revenue post-Timeboost, with time-boosted transactions sometimes reverting more often than non-timeboosted ones. Overall, the results suggest Timeboost effectively reallocates rents from wasted blockspace to auction payments, mitigating MEV-related spam while improving market efficiency on Arbitrum and across major L2s.

Abstract

Maximal extractable value opportunities often induce spam in Layer-2 blockchains: many identical transactions are submitted near simultaneously, most of which revert, wasting blockspace. We study Timeboost, a mechanism on Arbitrum that auctions a timestamp advantage, crucial under first-come first-served sequencing rules. We develop a game-theoretic model in which users choose the number of transaction copies to submit, and extend upon the baseline setting by modeling the Timeboost auction and subsequent transaction submission behavior. We show that Timeboost reduces spam and increases sequencer/DAO revenue in equilibrium relative to the baseline, transferring user payments from revert costs to auction bids. Empirically, we assemble mempool data from multiple Layer-2 networks, measuring spam via identical transactions submitted in narrow time intervals, and conduct an event study around Timeboost adoption on Arbitrum using other L2s as contemporaneous benchmarks. We find a decline in MEV-related spam and an increase in revenue on Arbitrum post-adoption, consistent with model predictions.

Does Timeboost Reduce MEV-Related Spam? Theory and Evidence from Layer-2 Transactions

TL;DR

The paper tackles MEV-related spam induced by FCFS sequencing on Layer-2s by proposing Timeboost, an Express Lane mechanism that auctions a time advantage. It develops a theory comparing baseline FCFS spam with a Timeboost-augmented environment, deriving unique equilibria where winning traders submit more copies while losers scale back, and showing that the auction reduces overall spam and increases sequencer revenue. The empirical analysis uses cross-chain difference-in-differences and a within-Arbitrum study to show a sizable drop in repeated transactions and a rise in revenue post-Timeboost, with time-boosted transactions sometimes reverting more often than non-timeboosted ones. Overall, the results suggest Timeboost effectively reallocates rents from wasted blockspace to auction payments, mitigating MEV-related spam while improving market efficiency on Arbitrum and across major L2s.

Abstract

Maximal extractable value opportunities often induce spam in Layer-2 blockchains: many identical transactions are submitted near simultaneously, most of which revert, wasting blockspace. We study Timeboost, a mechanism on Arbitrum that auctions a timestamp advantage, crucial under first-come first-served sequencing rules. We develop a game-theoretic model in which users choose the number of transaction copies to submit, and extend upon the baseline setting by modeling the Timeboost auction and subsequent transaction submission behavior. We show that Timeboost reduces spam and increases sequencer/DAO revenue in equilibrium relative to the baseline, transferring user payments from revert costs to auction bids. Empirically, we assemble mempool data from multiple Layer-2 networks, measuring spam via identical transactions submitted in narrow time intervals, and conduct an event study around Timeboost adoption on Arbitrum using other L2s as contemporaneous benchmarks. We find a decline in MEV-related spam and an increase in revenue on Arbitrum post-adoption, consistent with model predictions.

Paper Structure

This paper contains 27 sections, 6 theorems, 67 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Timeboost Details
  3. Related Literature
  4. Model
  5. Timeboost Auction
  6. Modified Model and Equilibrium
  7. Comparison with Baseline Setting
  8. Data
  9. Empirical Analysis
  10. Across Layer 2s
  11. Empirical Specification and Identification.
  12. Discussion of Results.
  13. Within Arbitrum
  14. Empirical Specification.
  15. Discussion of Results.
  16. ...and 12 more sections

Key Result

proposition 2.1

There exists a unique symmetric pure-strategy Nash equilibrium where the number of copies submitted by each arbitrageur is

Figures (2)

  • Figure 1: Top: Repeated transactions across chains. Bottom: Revenue in ETH from reverted transactions and auction proceeds for Arbitrum after April 17 (log scales).
  • Figure 2: Top: Percentage of repeated transactions that failed on Arbitrum, separated by timeboosted status. Bottom: Percentage of repeated transactions that failed on L2s.

Theorems & Definitions (8)

  • proposition 2.1
  • proposition 3.1
  • proposition 3.2
  • proposition 3.3
  • lemma 1
  • proof
  • lemma 2
  • proof