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arxiv: 2506.04940 · v4 · submitted 2025-06-05 · 💰 econ.GN · cs.DC· q-fin.EC

Becoming Immutable: How Ethereum is Made

Andrea Canidio , Vabuk Pahari This is my paper

Pith reviewed 2026-05-19 11:27 UTC · model grok-4.3

classification 💰 econ.GN cs.DCq-fin.EC
keywords ethereumnon-winning blockstransaction routingexecution qualityarbitrage botsdecentralized exchangesblock compositionmarket quality
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The pith

Non-winning Ethereum blocks reveal that 21% of user transactions are delayed by fragmented routing and that execution quality varies with block composition.

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

Standard blockchain records only show the final winning block, hiding how transactions move through a chain of intermediaries before inclusion. The authors built a dataset of 15,097 proposed but unselected blocks to observe this hidden process directly. They find that 21% of user transactions appear in candidate blocks yet are omitted from the winning block, indicating that routing choices affect inclusion timing. Execution outcomes for the same swap also differ across candidate blocks in both probability and price. Competition from arbitrage bots trading between decentralized and centralized exchanges further shapes these outcomes depending on trade direction relative to the bots.

Core claim

The paper constructs a novel dataset of 15,097 non-winning Ethereum blocks to study the otherwise unobservable path transactions take through intermediaries. It shows that 21% of user transactions are delayed because they reach candidate blocks but not the selected block. For identical swaps, execution probability and price vary substantially across proposed blocks. When arbitrage bots are also present, user swaps in the same direction as the bots execute less often and at worse prices, while opposite-direction swaps execute more often and at better prices.

What carries the argument

Dataset of non-winning blocks that makes visible the transactions routed through intermediaries but excluded from the final chain.

If this is right

  • 21% of user transactions experience inclusion delays because they reach candidate blocks but not the winning block.
  • For the same swap, execution probability and price differ across different proposed blocks.
  • User swaps in the same direction as arbitrage bots are less likely to execute and receive worse prices.
  • User swaps in the opposite direction to arbitrage bots are more likely to execute and receive better prices.

Where Pith is reading between the lines

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

  • Transaction routers become a key determinant of user outcomes, suggesting users may benefit from selecting routers that minimize competition exposure.
  • Block composition effects point to potential market-design improvements that reduce adverse interactions between user trades and bot activity.
  • The findings extend to other blockchains with proposer-builder separation, where similar routing fragmentation could affect execution.

Load-bearing premise

The 15,097 non-winning blocks are representative of all proposed blocks and that observed differences in execution stem from routing and bot competition rather than other selection effects.

What would settle it

A larger or differently sampled set of non-winning blocks showing no 21% delay rate or no systematic execution differences once routing paths are fully accounted for.

Figures

Figures reproduced from arXiv: 2506.04940 by Andrea Canidio, Vabuk Pahari.

Figure 1
Figure 1. Figure 1: ETH/USDC price on Binance during our study period [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Bids and value of blocks for different builders (note the changes in the time scale) [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Proportion of transactions shared between blocks by different builders, at different moments [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Competition between Rsync-bot and Titan-bot on the WETH/MOG Uniswap v3 pool. [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Competition between Rsync-bot and Titan-bot on the WETH/USDT and WETH/USDC [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Proportion of transactions shared between blocks by different builders, at different moments [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Block 21322626 [PITH_FULL_IMAGE:figures/full_fig_p026_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Block 21322630 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time since last block 0.0 0.2 0.4 0.6 0.8 Implied Price +3.536e3 USDC titan rsync Binance Price (a) USDC Pool 1 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time since last block 0.0 0.2 0.4 0.6 0.8 Implied Price +3.536e3 USDC titan rsync Binance Price (b) USDC Pool 2 [PITH_FULL_IMAGE:figures/full_fig_p027_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Block 21322631 [PITH_FULL_IMAGE:figures/full_fig_p027_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Block 21322648 11.0 11.5 12.0 12.5 13.0 13.5 14.0 Time since last block 3541.4 3541.6 3541.8 3542.0 3542.2 3542.4 Implied Price USDC titan rsync Binance Price (a) USDC Pool 1 11.0 11.5 12.0 12.5 13.0 13.5 14.0 Time since last block 3541.8 3542.0 3542.2 3542.4 3542.6 3542.8 Implied Price USDT titan rsync Binance Price (b) USDT Pool 1 [PITH_FULL_IMAGE:figures/full_fig_p028_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Block 21322649 [PITH_FULL_IMAGE:figures/full_fig_p028_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Block 21322657 - 21322660 12 14 16 18 20 Time since last block 3535.0 3537.5 3540.0 3542.5 3545.0 3547.5 3550.0 Implied Price DAI titan rsync Binance Price (a) DAI Block 21322635 11.0 11.5 12.0 12.5 13.0 13.5 14.0 14.5 15.0 Time since last block 3550 3552 3554 3556 3558 3560 Implied Price DAI titan rsync Binance Price (b) DAI Block 21322659 11.0 11.5 12.0 12.5 13.0 13.5 14.0 Time since last block 3550 355… view at source ↗
Figure 15
Figure 15. Figure 15: DAI all blocks [PITH_FULL_IMAGE:figures/full_fig_p029_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: UNI all blocks. Note that panels (e) and (f) plot the same data but at different time scales. [PITH_FULL_IMAGE:figures/full_fig_p030_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: AAVE Block 21322658 11.0 11.5 12.0 12.5 13.0 13.5 14.0 Time since last block 0.03755 0.03760 0.03765 0.03770 Implied Price WBTC titan rsync Binance Price [PITH_FULL_IMAGE:figures/full_fig_p030_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: WBTC Block 21322641 [PITH_FULL_IMAGE:figures/full_fig_p030_18.png] view at source ↗
read the original abstract

Blockchain's economic value lies in enabling financial and economic transactions without relying on trusted, centralized intermediaries. In practice, however, transactions pass through a fragmented chain of intermediaries before being included on-chain. Because standard blockchain data reveal only the winning block, this process is largely unobservable. We address this limitation by constructing a novel dataset of 15,097 non-winning Ethereum blocks, that is, blocks proposed but not selected for inclusion. We show that 21% of user transactions are delayed: they appear in candidate blocks but not in the winning block, implying that fragmented routing materially affects inclusion time. We further show that execution quality varies substantially across candidate blocks: for the same swap, both execution probability and execution price differ across proposed blocks. To study these differences, we examine competition between two arbitrage bots trading between decentralized and centralized exchanges. We find that, conditional on inclusion in a block that also contains transactions from these bots, user swaps in the same (opposite) direction are less likely (more likely) to execute and receive worse (better) prices. These results show that routing and block composition are central determinants of execution quality and market quality in on-chain markets.

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 manuscript constructs a novel dataset of 15,097 non-winning Ethereum blocks (i.e., blocks proposed but not selected for inclusion) to examine transaction routing and execution in a fragmented intermediary environment. It reports that 21% of user transactions appear in candidate blocks but not the winning block, implying material delays from routing fragmentation. It further documents substantial cross-block variation in swap execution probability and price, and shows that co-inclusion with two arbitrage bots reduces (increases) execution likelihood and worsens (improves) prices for same-direction (opposite-direction) user swaps.

Significance. If the dataset is representative and the attribution to routing and bot competition is robust, the paper offers a rare empirical window into the unobservable pre-inclusion stage of Ethereum transactions. This is valuable for understanding how intermediaries affect inclusion times and market quality in on-chain trading. The creation of a new data source on non-winning blocks is a clear strength that could enable follow-on work.

major comments (1)
  1. [Dataset construction and methods] The central quantitative claims (21% delay rate; substantial variation in execution quality; conditional bot effects) rest on the 15,097 non-winning blocks being representative of the full population of proposed but unselected blocks. The data-collection pipeline is not described in sufficient detail to rule out selection bias arising from monitoring only a subset of relays and builders. If unobserved proposals differ systematically in transaction mix, gas usage, or competitive intensity, the reported delay frequency and bot-conditional effects could be artifacts rather than consequences of fragmented routing. Please add a dedicated methods subsection detailing the relays, builders, and observers used, coverage statistics, and any validation (e.g., comparison of observable block characteristics to known Ethereum proposer statistics).
minor comments (2)
  1. [Abstract] The abstract refers to 'two arbitrage bots' without naming them or providing a brief characterization; adding this context would improve accessibility for readers outside the narrow DeFi bot literature.
  2. [Results on transaction delays] Table or figure presenting the 21% delay statistic should include the exact numerator/denominator and any conditioning variables used in the calculation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments and recommendation for major revision. We agree that greater transparency on data collection is needed to evaluate representativeness and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: The central quantitative claims (21% delay rate; substantial variation in execution quality; conditional bot effects) rest on the 15,097 non-winning blocks being representative of the full population of proposed but unselected blocks. The data-collection pipeline is not described in sufficient detail to rule out selection bias arising from monitoring only a subset of relays and builders. If unobserved proposals differ systematically in transaction mix, gas usage, or competitive intensity, the reported delay frequency and bot-conditional effects could be artifacts rather than consequences of fragmented routing. Please add a dedicated methods subsection detailing the relays, builders, and observers used, coverage statistics, and any validation (e.g., comparison of observable block characteristics to known Ethereum proposer statistics).

    Authors: We agree that a dedicated methods subsection is warranted. Although the current manuscript briefly notes the use of multiple relays and builders in the Data section, we did not provide exhaustive coverage statistics or formal validation against the full population of proposed blocks. In the revision we will add a new subsection 'Data Collection Pipeline and Coverage' that specifies the exact relays (Flashbots, BloXroute, Eden, and others) and builders monitored, the observer infrastructure, the fraction of total proposed blocks captured during the sample window, and validation comparisons of observable characteristics (gas usage, transaction counts, and proposer distributions) to publicly available beacon-chain data on all proposed blocks. This will allow readers to assess potential selection bias directly. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical dataset construction and descriptive analysis

full rationale

The paper constructs a novel dataset of 15,097 non-winning Ethereum blocks via direct observation of candidate blocks and reports empirical frequencies (21% user transaction delay) plus conditional effects on execution probability and price. These quantities are computed directly from the collected data without any fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations. No equations or derivations appear that reduce the reported results to the inputs by construction. The analysis is self-contained against external benchmarks of block proposal data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claims rest on empirical observation of a newly constructed dataset of non-winning blocks and statistical comparisons of execution outcomes; no free parameters, axioms, or invented entities are introduced in the abstract.

pith-pipeline@v0.9.0 · 5733 in / 1103 out tokens · 50499 ms · 2026-05-19T11:27:55.299102+00:00 · methodology

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Reference graph

Works this paper leans on

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