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arxiv: 2606.00720 · v1 · pith:VFU2QRWBnew · submitted 2026-05-30 · 💻 cs.CE

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

Fei Wu , Burak \"Oz This is my paper

Pith reviewed 2026-06-28 18:01 UTC · model grok-4.3

classification 💻 cs.CE
keywords MEVarbitrageprobabilistic searchtargeted searchspamBasefee floorsblockspace
0
0 comments X

The pith

Probabilistic search accounts for 23% of Base arbitrage but 95% of spam and 20% of gas.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper distinguishes targeted search, where bots discover opportunities off-chain before submitting committed transactions, from probabilistic search, where bots submit repeated on-chain attempts that discover opportunities during execution. It models how ordering rules, fee floors, and sudden opportunity shocks determine which approach prevails. Trace data from Base shows probabilistic bots produce nearly all spam and consume substantial blockspace despite low overall activity. Configuration changes that raise fee floors and alter ordering shift protocol revenue toward completed arbitrages and reduce the relative cost of spam.

Core claim

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.

What carries the argument

trace-level classifier that labels cyclic arbitrage transactions as targeted or probabilistic search based on execution patterns

If this is right

  • Flashblocks ordering selects against broad probabilistic scanners.
  • Token-launch shocks temporarily increase probabilistic search volume.
  • Higher fee floors eliminate probabilistic bots whose opportunity flow cannot cover repeated attempts.
  • Protocol revenue moves from spam to completed arbitrages after the configuration shifts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same spam-to-activity ratio may appear on other high-throughput chains that adopt similar fee and ordering rules.
  • Protocols could test temporary fee increases during known launch events to suppress revived probabilistic activity.
  • Improving classifier precision on live traces would let chains monitor and adjust parameters in real time to limit gas waste.

Load-bearing premise

The classifier built on cyclic arbitrage traces correctly separates the two search styles and those labels track real differences in spam and gas use.

What would settle it

New on-chain traces from a period or chain where the same classifier labels fail to predict higher spam rates or gas shares for the probabilistic group.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper distinguishes targeted vs. probabilistic search architectures for MEV arbitrage on high-throughput chains, models how ordering granularity, fee floors, and opportunity shocks affect competition between them, and uses a trace-level classifier on Base cyclic-arbitrage traces (June 2025–Feb 2026) to show that probabilistic search is only 23% of activity yet produces 95% of spam and 20% of gas; three natural experiments (Flashblocks, token-launch shocks, fee-floor changes) are used to test selection effects, with post-change revenue shifting toward successful arbitrages.

Significance. If the classifier is reliable, the work supplies the first large-scale empirical decomposition of MEV search modes and their differential impact on spam and fee revenue, together with falsifiable predictions about how Flashblocks and fee floors alter the mix; the episode tests and the explicit linkage of architecture labels to execution behavior are strengths that would be valuable to the MEV and blockchain-design literature.

major comments (1)
  1. [section describing the trace-level classifier and its validation] The trace-level classifier (developed from cyclic-arbitrage data and used to label all subsequent statistics and episode tests) is load-bearing for the headline claims (23% activity / 95% spam / 20% gas) and for the three configuration-change results, yet the manuscript reports no precision, recall, confusion matrix, inter-rater agreement, or external ground-truth comparison. Without these metrics it is impossible to bound the effect of label noise on the spam attribution or on the claimed selection effects of Flashblocks and fee floors.
minor comments (2)
  1. [Abstract] The abstract states that the labels 'correspond to distinct execution behavior' but does not indicate which execution metrics (gas, success rate, priority-fee distribution, etc.) were used for this check or whether they were pre-specified.
  2. [results section reporting aggregate statistics] Table or figure presenting the 23/95/20 percentages should include the underlying sample size (number of traces, number of blocks) and the exact definition of 'spam' employed by the classifier.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the classifier validation. We agree that quantitative metrics are needed to support the load-bearing empirical claims and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: The trace-level classifier (developed from cyclic-arbitrage data and used to label all subsequent statistics and episode tests) is load-bearing for the headline claims (23% activity / 95% spam / 20% gas) and for the three configuration-change results, yet the manuscript reports no precision, recall, confusion matrix, inter-rater agreement, or external ground-truth comparison. Without these metrics it is impossible to bound the effect of label noise on the spam attribution or on the claimed selection effects of Flashblocks and fee floors.

    Authors: We agree the manuscript currently lacks these quantitative validation metrics. In the revision we will add a dedicated subsection on classifier construction and validation. This will include precision, recall, and a confusion matrix computed on a held-out set of manually labeled traces, plus discussion of inter-rater agreement where applicable and sensitivity checks showing that the headline statistics (23%/95%/20%) and episode results remain qualitatively unchanged under plausible label noise. These additions will allow readers to bound the impact of any misclassification. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical classifier and statistics are independent of any self-referential derivation.

full rationale

The paper develops a trace-level classifier from cyclic arbitrage data on Base and applies the resulting labels to produce descriptive statistics (23% activity, 95% spam, 20% gas). No equations, fitted parameters renamed as predictions, or self-citations appear in the provided text. The classifier is presented as a measurement tool whose validity rests on external validation (distinct execution behavior), not on any reduction to its own outputs by construction. The modeling of ordering granularity and fee floors is described as theoretical but does not reduce to the empirical labels. This is a standard empirical pipeline with no load-bearing self-definition or self-citation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the classifier and opportunity-access shock assumptions are implicit but not detailed.

pith-pipeline@v0.9.1-grok · 5740 in / 1053 out tokens · 23689 ms · 2026-06-28T18:01:40.564011+00:00 · methodology

discussion (0)

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Reference graph

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