To Wait or To Probe: Arbitrage Competition on High-Throughput Blockchains

2026-05-30

AI Summary

This study addresses the impact of arbitrage bots on high-throughput blockchains, where off-chain wait-and-probe or on-chain blind-search strategies significantly influence spam transactions, block space consumption, and protocol fee revenue. For the first time, this work empirically distinguishes and labels these two search architectures, integrating game-theoretic modeling, on-chain tracing, and machine learning to construct a fine-grained classifier based on cyclic arbitrage data from the Base chain between June 2025 and February 2026. The analysis reveals that although only 23% of arbitrage activity employs probabilistic search, it generates 95% of spam transactions and consumes 20% of total gas. Furthermore, adjusting protocol parameters leads to fee revenue becoming more concentrated among successful arbitrageurs and results in a marked reduction in spam transactions.

Abstract

Maximal Extractable Value (MEV) on high-throughput blockchains can be captured through targeted search, where bots identify opportunities off-chain and submit route-committed transactions, or through probabilistic search, where bots submit repeated attempts that resolve opportunity discovery during on-chain execution. This distinction has direct implications for spam, blockspace consumption, and protocol fee revenue. We model how ordering granularity, fee floors, and opportunity-access shocks shape competition between these architectures. Using cyclic arbitrage data on Base from June 2025 to February 2026, we develop a trace-level classifier for search architectures and show that the resulting labels correspond to distinct execution behavior. We test the model across three episodes: Flashblocks selects against broad on-chain probabilistic scanners; token-launch opportunity shocks temporarily revive probabilistic search; and higher fee floors select against probabilistic bots whose opportunity flow cannot sustain repeated attempts. In our sample, probabilistic search accounts for only 23% of arbitrage activity but produces 95% of spam and consumes 20% of Base gas. After Base's configuration changes, protocol fee revenue shifts toward successful arbitrages and away from spam, probabilistic bots pay higher priority fees, and spam consumes a smaller share of blockspace.

Problem

• Maximal Extractable Value
• arbitrage competition
• probabilistic search
• blockchain spam
• blockspace consumption

Innovation

• Maximal Extractable Value (MEV)
• probabilistic search
• targeted search
• blockchain spam
• gas consumption