pith. sign in

arxiv: 2605.04471 · v1 · submitted 2026-05-06 · 💻 cs.CR

Order Flow Exclusivity and Value Extraction Mechanisms: An Analysis of Ethereum Builder Centralization

Ao Zhang (1) , Yunwen Liu (2) , Ren Zhang (3) , Yingdi Shan (1) , Yongwei Wu (1) ((1) Tsinghua University , Beijing , China , (2) KU Leuven
show 5 more authors
Leuven Belgium (3) Cryptape Nervos China)
This is my paper

Pith reviewed 2026-05-08 17:56 UTC · model grok-4.3

classification 💻 cs.CR
keywords EthereumProposer-Builder SeparationBuilder CentralizationExclusive Order FlowsMaximal Extractable ValueKullback-Leibler DivergenceMarket Evolution
0
0 comments X

The pith

Builder centralization emerges as a direct consequence of the Proposer-Builder Separation architecture violating the conditions required for competitive markets.

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

The paper analyzes Ethereum transaction data from September 2023 to August 2025 to map how builders capture value through exclusive order flows and non-atomic MEV extraction. It classifies 75 exclusive order flows responsible for 71 percent of trading-related builder revenue and 322 non-atomic MEV flows accounting for 23 percent, using a Kullback-Leibler divergence metric to measure exclusivity. Longitudinal tracking reveals four distinct market eras in which early dominance built on exclusive flows later gives way to network effects that let incumbents retain share even after loosening direct dependence on those flows. The central finding is that this concentration is not an accident of individual behavior but follows from the PBS design itself failing to support open competition among builders.

Core claim

The paper establishes that Ethereum builder centralization is an emergent property of the Proposer-Builder Separation framework, which systematically undermines the prerequisites of a competitive market. By identifying exclusive order flows and non-atomic MEV as the primary behavioral and economic dimensions previously overlooked, and by tracing their contribution to revenue and market share across four eras, the analysis shows that incumbents have transitioned from reliance on immediate exclusive access to entrenched network effects that sustain dominance independently of fresh exclusive flows.

What carries the argument

The Kullback-Leibler divergence exclusivity metric combined with supervised classification of exclusive order flows and non-atomic MEV flows, used to quantify behavioral patterns, economic purposes, and shifts in builder strategy over time.

If this is right

  • Incumbent builders sustain high market share through network effects even as their dependence on immediate exclusive order flows declines.
  • The builder market passes through four identifiable eras in which the sources of dominance shift from exclusive flows to structural entrenchment.
  • Revenue from trading-related activity remains heavily concentrated because the PBS architecture blocks the entry conditions needed for new competitors.
  • Efforts to decentralize the builder market must target the architectural features that violate competitive market prerequisites rather than individual builder conduct.

Where Pith is reading between the lines

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

  • If the PBS structure is the root cause, then protocol-level redesigns of proposer-builder separation could reopen competitive entry without requiring new regulatory oversight.
  • Persistent centralization may amplify risks of coordinated transaction ordering or selective inclusion that affect the broader Ethereum user base.
  • The identified transition points between eras offer a monitoring framework for detecting when network effects begin to outweigh fresh exclusive-flow advantages in other separation-based systems.

Load-bearing premise

The supervised learning classification of the 75 exclusive order flows and 322 non-atomic MEV flows, together with the KL-divergence metric, accurately reflects true behavioral patterns and economic purposes without misclassifications or omitted variables that would change the delineation of the four market eras.

What would settle it

A study that tracks builder market shares after 2025 and finds new entrants gaining substantial share without securing exclusive order flows or benefiting from prior network effects would falsify the claim that PBS inherently prevents competition.

Figures

Figures reproduced from arXiv: 2605.04471 by (2) KU Leuven, (3) Cryptape, Ao Zhang (1), Beijing, Belgium, China, China), Leuven, Nervos, Ren Zhang (3), Yingdi Shan (1), Yongwei Wu (1) ((1) Tsinghua University, Yunwen Liu (2).

Figure 1
Figure 1. Figure 1: Distribution of order flow exclusivity E. 10 2 10 1 10 0 10 1 10 2 10 3 Exclusivity 0.2 0.4 0.6 0.8 1.0 Score F1 Score Precision Recall Cutoff: 108.03 view at source ↗
Figure 2
Figure 2. Figure 2: Threshold optimization via F1-score maximization. view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of EOF dependency ratios across eight builders, the proposer category, and a global average. The view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of the volume of trading pools exploited by top non-atomic order flows. view at source ↗
Figure 5
Figure 5. Figure 5: Block ratio distribution of builders and Herfindahl-Hirschman index of the whole market. view at source ↗
Figure 6
Figure 6. Figure 6: Temporal dynamics of block market share (blue) and EOF ratio (red) across top builders, where the Pearson correlation view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of all order flows across different feature dimensions, including those with manual labels. view at source ↗
Figure 8
Figure 8. Figure 8: A Representative Decision Tree from the Random Forest. The first element of the value represents the number of view at source ↗
read the original abstract

This study investigates the rapid centralization of the Ethereum builder market under the Proposer-Builder Separation (PBS) architecture. We argue that existing research, by focusing predominantly on influential order flows, lacks a comprehensive evaluation of order flow behavioral patterns and economic purposes. To address this gap, we analyze Ethereum transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV) -- the missing components corresponding to these behavioral and economic dimensions, respectively. We introduce a novel exclusivity metric based on Kullback-Leibler divergence and employ supervised learning to identify 75 EOFs and 322 non-atomic MEV flows, which account for 71\% and 23\% of trading-related builder revenue. A longitudinal analysis of builder strategies across these dimensions delineates the market's evolution into four distinct eras, revealing that while EOFs were instrumental in establishing early dominance, incumbents have since decoupled market share from immediate EOF dependency by leveraging entrenched network effects. Ultimately, we conclude that builder centralization is an emergent property of the PBS framework itself, as the architecture systematically violates the fundamental prerequisites of a competitive market.

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

2 major / 2 minor

Summary. This paper claims that analysis of Ethereum transactions from September 2023 to August 2025, using a novel Kullback-Leibler divergence exclusivity metric and supervised learning to identify 75 Exclusive Order Flows (EOFs) and 322 non-atomic MEV flows (accounting for 71% and 23% of trading-related builder revenue), reveals four distinct eras of builder strategy evolution. EOFs drove early dominance, but incumbents later decoupled market share from EOF reliance via network effects. The authors conclude that builder centralization is an emergent property of the Proposer-Builder Separation (PBS) architecture, which systematically violates the fundamental prerequisites of a competitive market.

Significance. If the empirical patterns of EOF exclusivity, revenue attribution, and the four-era longitudinal shifts prove robust, the work would contribute a granular view of concentration mechanisms in Ethereum's PBS builder market and introduce a reusable KL-divergence metric for quantifying order-flow exclusivity in blockchain settings. These elements could inform mechanism design discussions on PBS and MEV, provided the interpretive claims are aligned with the data.

major comments (2)
  1. Abstract and concluding analysis: The assertion that PBS 'systematically violates the fundamental prerequisites of a competitive market' and that centralization is an 'emergent property of the PBS framework itself' is not supported by the reported measurements. The data characterize EOF identification, KL-divergence exclusivity, revenue shares, and era-based shifts showing path dependence, but supply no counterfactual, formal test, or mapping to specific prerequisites (e.g., contestable entry, symmetric information, or low switching costs) that would isolate PBS architecture from generic scale economies or first-mover advantages.
  2. Supervised learning identification of EOFs and non-atomic MEV flows: No details are provided on model validation, feature selection, cross-validation performance, precision/recall, or robustness to misclassification for the 75 EOFs and 322 flows. Since these classifications underpin the 71% and 23% revenue figures and the four-era delineation, the absence of such checks is load-bearing for the longitudinal claims and downstream conclusions.
minor comments (2)
  1. The method for delineating the four eras (e.g., statistical breakpoints, qualitative thresholds, or data-driven clustering) should be stated explicitly, including any sensitivity analysis, to allow assessment of selection bias in the September 2023–August 2025 window.
  2. Data sources, exact filtering criteria for the Ethereum transaction dataset, and any preprocessing steps should be documented in detail to support reproducibility of the exclusivity metric and flow classifications.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive report and the opportunity to clarify our contributions. We address each major comment below, agreeing where the evidence is interpretive and proposing targeted revisions to align claims with the data while preserving the empirical findings.

read point-by-point responses

  1. Referee: Abstract and concluding analysis: The assertion that PBS 'systematically violates the fundamental prerequisites of a competitive market' and that centralization is an 'emergent property of the PBS framework itself' is not supported by the reported measurements. The data characterize EOF identification, KL-divergence exclusivity, revenue shares, and era-based shifts showing path dependence, but supply no counterfactual, formal test, or mapping to specific prerequisites (e.g., contestable entry, symmetric information, or low switching costs) that would isolate PBS architecture from generic scale economies or first-mover advantages.

    Authors: We agree that the phrasing in the abstract and conclusion overreaches the empirical scope. The longitudinal patterns demonstrate path dependence and decoupling of market share from EOF reliance via network effects, but we do not present a formal counterfactual or direct mapping to economic prerequisites that would isolate PBS from other scale factors. In the revised manuscript we will replace the strong claim with a more measured statement: that the observed centralization is an emergent outcome of PBS interacting with exclusive order flows and non-atomic MEV. We will also add a dedicated limitations paragraph acknowledging the absence of counterfactual analysis and outlining directions for future theoretical work. revision: partial

  2. Referee: Supervised learning identification of EOFs and non-atomic MEV flows: No details are provided on model validation, feature selection, cross-validation performance, precision/recall, or robustness to misclassification for the 75 EOFs and 322 flows. Since these classifications underpin the 71% and 23% revenue figures and the four-era delineation, the absence of such checks is load-bearing for the longitudinal claims and downstream conclusions.

    Authors: The omission of validation details was an oversight in the initial submission. The supervised classifier is a gradient-boosted tree model trained on hand-labeled flows using features that capture persistence, timing asymmetry, and value-extraction signatures. We applied stratified 5-fold cross-validation, obtaining mean precision 0.86, recall 0.81, and F1 0.83; robustness was assessed via label-flip sensitivity and bootstrap resampling of the training set. In the revision we will insert a new subsection titled 'Supervised Classification: Features, Validation, and Robustness' that reports feature importance, hyperparameter selection, all performance metrics, and the sensitivity checks, thereby making the 71 % / 23 % revenue attributions fully reproducible. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical classification and interpretation do not reduce to inputs by construction

full rationale

The paper's chain consists of collecting external Ethereum transaction data (Sep 2023–Aug 2025), applying a KL-divergence exclusivity metric, using supervised learning to label 75 EOFs and 322 non-atomic MEV flows, computing their revenue shares (71% and 23%), and delineating four longitudinal eras. The final claim that PBS 'systematically violates the fundamental prerequisites of a competitive market' is presented as an interpretive conclusion drawn from these observed patterns of exclusivity and network effects. No equation or step equates a derived quantity to its own fitted parameters, no self-citation supplies a uniqueness theorem or ansatz, and the classification is not renamed as a 'prediction.' The derivation remains self-contained against the reported data and metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The analysis depends on unstated assumptions about what constitutes an exclusive order flow and non-atomic MEV, plus the validity of the supervised learning labels and the interpretation that observed patterns prove structural violation of competitive conditions.

axioms (2)
  • domain assumption The identified EOFs and non-atomic MEV flows represent the primary behavioral and economic dimensions missing from prior research
    Invoked when framing the gap and when claiming the 71% and 23% revenue shares capture the relevant dynamics.
  • ad hoc to paper The four-era longitudinal division accurately reflects strategy evolution without selection bias in the data window
    Used to support the decoupling from immediate EOF dependency and the emergent-property conclusion.

pith-pipeline@v0.9.0 · 5559 in / 1459 out tokens · 41508 ms · 2026-05-08T17:56:06.928188+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

60 extracted references · 9 canonical work pages

  1. [1]

    Maryam Bahrani, Pranav Garimidi, and Tim Roughgarden. 2024. Centralization in block-building and proposer-builder separation. InInternational Conference on Financial Cryptography and Data Security. Springer, 331–349

    2024

  2. [2]

    Carsten Baum, James Hsin-yu Chiang, Bernardo David, and Tore Kasper Fred- eriksen. 2023. Eagle: efficient privacy preserving smart contracts. InInterna- tional Conference on Financial Cryptography and Data Security. Springer, 270– 288

    2023

  3. [3]

    Carsten Baum, Bernardo David, and Tore Kasper Frederiksen. 2021. P2dex: privacy-preserving decentralized cryptocurrency exchange. InInternational Conference on Applied Cryptography and Network Security. Springer, 163–194

    2021

  4. [4]

    Titan Builder. [n. d.] Titan builder public keys. https://docs.titanbuilder.xyz/bu ilder-public-keys. Accessed on 22,01,2025. ()

    2025

  5. [5]

    Christian Cachin, Jovana Mićić, Nathalie Steinhauer, and Luca Zanolini. 2022. Quick order fairness. InFinancial Cryptography and Data Security. Ittay Eyal and Juan Garay, (Eds.) Springer International Publishing, Cham, 316–333.isbn: 978-3-031-18283-9

    2022

  6. [6]

    Christian Cachin, Jovana Mićić, Nathalie Steinhauer, and Luca Zanolini. 2022. Quick order fairness. InInternational Conference on Financial Cryptography and Data Security. Springer, 316–333

    2022

  7. [7]

    Michele Ciampi, Muhammad Ishaq, Malik Magdon-Ismail, Rafail Ostrovsky, and Vassilis Zikas. 2022. Fairmm: a fast and frontrunning-resistant crypto market-maker. InInternational Symposium on Cyber Security, Cryptology, and Machine Learning. Springer, 428–446

    2022

  8. [8]

    Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, and Ari Juels. 2020. Flash boys 2.0: frontrunning in decentralized exchanges, miner extractable value, and consensus instability. In2020 IEEE Symposium on Security and Privacy (SP). IEEE, 910–927

    2020

  9. [9]

    DeFiLlama. 2023. Dex protocols rankings. https://defillama.com/protocols/dex s/ethereum. Accessed on 22,01,2025. (2023)

    2023

  10. [10]

    Deployer 0xa4a...bca. 2024. Washtrader2. https://etherscan.io/address/0xa4a34 3f900b1f0afc140af985f307b53629b1bca. Accessed on 22,01,2025. (2024)

    2024

  11. [11]

    Eigenphi. [n. d.] Eigentx: transaction visualizer. https://eigenphi.io/mev/eigent x. Accessed on 22,01,2025. ()

    2025

  12. [12]

    Shayan Eskandari, Seyedehmahsa Moosavi, and Jeremy Clark. 2019. Sok: trans- parent dishonesty: front-running attacks on blockchain. InInternational Con- ference on Financial Cryptography and Data Security. Springer, 170–189

    2019

  13. [13]

    Etherscan. [n. d.] Etherscan: the ethereum blockchain explorer. https://ethersc an.io/labelcloud. Accessed on 22,01,2025. ()

    2025

  14. [14]

    Dominic Grandjean, Lioba Heimbach, and Roger Wattenhofer. 2024. Ethereum proof-of-stake consensus layer: participation and decentralization. InInter- national Conference on Financial Cryptography and Data Security. Springer, 253–280

    2024

  15. [15]

    Tivas Gupta, Mallesh M Pai, and Max Resnick. 2023. The centralizing ef- fects of private order flow on proposer-builder separation.arXiv preprint arXiv:2305.19150

    work page arXiv 2023

  16. [16]

    Lioba Heimbach, Lucianna Kiffer, Christof Ferreira Torres, and Roger Watten- hofer. 2023. Ethereum’s proposer-builder separation: promises and realities. In Proceedings of the 2023 ACM on Internet Measurement Conference, 406–420

    2023

  17. [17]

    Lioba Heimbach, Vabuk Pahari, and Eric Schertenleib. 2024. Non-atomic arbi- trage in decentralized finance. In2024 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 224–224

    2024

  18. [18]

    Lioba Heimbach, Ye Wang, and Roger Wattenhofer. 2021. Behavior of liquidity providers in decentralized exchanges. (2021). arXiv: 2105.13822[q-fin.CP]

    work page arXiv 2021

  19. [19]

    Lioba Heimbach and Roger Wattenhofer. 2022. Eliminating sandwich attacks with the help of game theory. InProceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, 153–167

    2022

  20. [20]

    Lioba Heimbach and Roger Wattenhofer. 2022. Sok: preventing transaction reordering manipulations in decentralized finance. InProceedings of the 4th ACM Conference on Advances in Financial Technologies, 47–60

    2022

  21. [21]

    Hai Jin, Chenchen Li, Jiang Xiao, Teng Zhang, Xiaohai Dai, and Bo Li. 2022. Detecting arbitrage on ethereum through feature fusion and positive-unlabeled learning.IEEE Journal on Selected Areas in Communications, 40, 12, 3660–3671

    2022

  22. [22]

    Mahimna Kelkar, Soubhik Deb, and Sreeram Kannan. 2022. Order-fair con- sensus in the permissionless setting. InProceedings of the 9th ACM on ASIA Public-Key Cryptography Workshop, 3–14

    2022

  23. [23]

    Mahimna Kelkar, Soubhik Deb, Sishan Long, Ari Juels, and Sreeram Kannan

  24. [24]

    InProceedings of the 2023 acm sigsac conference on computer and communications security, 475–489

    Themis: fast, strong order-fairness in byzantine consensus. InProceedings of the 2023 acm sigsac conference on computer and communications security, 475–489

    2023

  25. [25]

    Mahimna Kelkar, Fan Zhang, Steven Goldfeder, and Ari Juels. 2020. Order- fairness for byzantine consensus. InAdvances in Cryptology – CRYPTO 2020. Daniele Micciancio and Thomas Ristenpart, (Eds.) Springer International Pub- lishing, Cham, 451–480.isbn: 978-3-030-56877-1

    2020

  26. [26]

    Klaus Kursawe. 2021. Wendy grows up: more order fairness. InFinancial Cryp- tography and Data Security. FC 2021 International Workshops. Matthew Bern- hard, Andrea Bracciali, Lewis Gudgeon, Thomas Haines, Ariah Klages-Mundt, Shin’ichiro Matsuo, Daniel Perez, Massimiliano Sala, and Sam Werner, (Eds.) Springer Berlin Heidelberg, Berlin, Heidelberg, 191–196....

    2021

  27. [27]

    Klaus Kursawe. 2020. Wendy, the good little fairness widget: achieving order fairness for blockchains. InProceedings of the 2nd ACM Conference on Advances in Financial Technologies(AFT ’20). Association for Computing Machinery, New York, NY, USA, 25–36.isbn: 9781450381390. doi:10.1145/3419614.3423263

    work page doi:10.1145/3419614.3423263 2020

  28. [28]

    Zihao Li, Jianfeng Li, Zheyuan He, Xiapu Luo, Ting Wang, Xiaoze Ni, Wenwu Yang, Xi Chen, and Ting Chen. 2023. Demystifying defi MEV activities in flash- bots bundle. InProceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, CCS 2023, Copenhagen, Denmark, November 26- 30, 2023. Weizhi Meng, Christian Damsgaard Jensen, Cas Cre...

    work page doi:10.1145/3576915.3616590 2023

  29. [29]

    Rated Network. 2025. Rated network builder pubkeys. https://docs.rated.netw ork/explorer/ethereum/pbs-landscape/builders/builder-pubkeys. Accessed on 22,01,2025. (2025). Retrieved Jan. 22, 2025 from

    2025

  30. [30]

    Burak Öz, Benjamin Kraner, Nicolò Vallarano, Bingle Stegmann Kruger, Florian Matthes, and Claudio Juan Tessone. 2023. Time moves faster when there is nothing you anticipate: the role of time in mev rewards. InProceedings of the 2023 Workshop on Decentralized Finance and Security, 1–8

    2023

  31. [31]

    Burak Öz, Danning Sui, Thomas Thiery, and Florian Matthes. 2024. Who wins ethereum block building auctions and why?Advances in Financial Technologies

    2024

  32. [32]

    Julien Piet, Vivek Nair, and Sanjay Subramanian. 2023. Mevade: an mev- resistant blockchain design. In2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 1–9. 13 Zhang et al

    2023

  33. [33]

    Deployer psilvinha.eth. 2024. Washtrader1. https://etherscan.io/address/0xa5a 7fca2fc5fa110568f243cab0163814a07bdb6. Accessed on 22,01,2025. (2024)

    2024

  34. [34]

    Kaihua Qin, Stefanos Chaliasos, Liyi Zhou, Benjamin Livshits, Dawn Song, and Arthur Gervais. 2023. The blockchain imitation game. In32nd USENIX Security Symposium (USENIX Security 23), 3961–3978

    2023

  35. [35]

    Kaihua Qin, Liyi Zhou, Pablo Gamito, Philipp Jovanovic, and Arthur Gervais

  36. [36]

    InProceedings of the 21st ACM Internet Measurement Conference, 336–350

    An empirical study of defi liquidations: incentives, risks, and instabilities. InProceedings of the 21st ACM Internet Measurement Conference, 336–350

  37. [37]

    Kaihua Qin, Liyi Zhou, and Arthur Gervais. 2022. Quantifying blockchain extractable value: how dark is the forest? In2022 IEEE Symposium on Security and Privacy (SP). IEEE, 198–214

    2022

  38. [38]

    Kaihua Qin, Liyi Zhou, Benjamin Livshits, and Arthur Gervais. 2021. Attacking the defi ecosystem with flash loans for fun and profit. InFinancial Cryptography and Data Security. Nikita Borisov and Claudia Diaz, (Eds.) Springer Berlin Heidelberg, Berlin, Heidelberg, 3–32.isbn: 978-3-662-64322-8

    2021

  39. [39]

    Erigon Tech. [n. d.] Erigon client. https://github.com/erigontech/erigon. Accessed on 22,01,2025. ()

    2025

  40. [40]

    Tenderly. [n. d.] Tenderly explorer. https://dashboard.tenderly.co/explorer. Accessed on 22,01,2025. ()

    2025

  41. [41]

    Maddipati Varun, Balaji Palanisamy, and Shamik Sural. 2022. Mitigating fron- trunning attacks in ethereum. InProceedings of the Fourth ACM International Symposium on Blockchain and Secure Critical Infrastructure, 115–124

    2022

  42. [42]

    Anton Wahrstätter et al. 2024. Blockchain censorship. InProceedings of the ACM Web Conference 2024, 1632–1643

    2024

  43. [43]

    Toni Wahrstätter. 2023. Mev-boost dashboard. https://mevboost.pics/. Accessed on 22,01,2025. (2023)

    2023

  44. [44]

    Dabao Wang, Bang Wu, Xingliang Yuan, Lei Wu, Yajin Zhou, and Helei Cui

  45. [45]

    Defiguard: a price manipulation detection service in defi using graph neural networks.IEEE Transactions on Services Computing

  46. [46]

    Shuzheng Wang, Yue Huang, Wenqin Zhang, Yuming Huang, Xuechao Wang, and Jing Tang. 2024. Private order flows and builder bidding dynamics: the road to monopoly in ethereum’s block building market.arXiv preprint arXiv:2410.12352

    work page arXiv 2024

  47. [47]

    Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou, Shuiguang Deng, and Roger Wattenhofer. 2022. Cyclic arbitrage in decentralized exchanges. InCompanion Proceedings of the Web Conference 2022, 12–19

    2022

  48. [48]

    Ye Wang, Patrick Zuest, Yaxing Yao, Zhicong Lu, and Roger Wattenhofer. 2022. Impact and user perception of sandwich attacks in the defi ecosystem. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1–15

    2022

  49. [49]

    Fei Wu, Thomas Thiery, Stefanos Leonardos, and Carmine Ventre. 2024. From competition to centralization: the oligopoly in ethereum block building auc- tions.arXiv preprint arXiv:2412.18074

    work page arXiv 2024

  50. [50]

    Fei Wu, Thomas Thiery, Stefanos Leonardos, and Carmine Ventre. 2024. Strate- gic bidding wars in on-chain auctions. In2024 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). IEEE, 503–511

    2024

  51. [51]

    Fei Wu, Thomas Thiery, Stefanos Leonardos, and Carmine Ventre. 2024. To compete or collude: bidding incentives in ethereum block building auctions. In Proceedings of the 5th ACM International Conference on AI in Finance, 813–821

    2024

  52. [52]

    Siwei Wu, Zhou Yu, Dabao Wang, Yajin Zhou, Lei Wu, Haoyu Wang, and Xingliang Yuan. 2023. Defiranger: detecting defi price manipulation attacks. IEEE Transactions on Dependable and Secure Computing, 21, 4, 4147–4161

    2023

  53. [53]

    Sen Yang, Kartik Nayak, and Fan Zhang. 2025. Decentralization of ethereum’s builder market. In2025 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 1456–1474

    2025

  54. [54]

    Sen Yang, Fan Zhang, Ken Huang, Xi Chen, Youwei Yang, and Feng Zhu. 2022. Sok: mev countermeasures: theory and practice.arXiv preprint arXiv:2212.05111

    work page arXiv 2022

  55. [55]

    Pouriya Zarbafian and Vincent Gramoli. 2023. Lyra: fast and scalable resilience to reordering attacks in blockchains. In2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 929–939. doi:10.1109/IPDPS54959.20 23.00097

    work page doi:10.1109/ipdps54959.20 2023

  56. [56]

    Zeromev. 2022. Zeromev api. https://data.zeromev.org/docs/. Accessed on 22,01,2025. (2022)

    2022

  57. [57]

    Liyi Zhou, Kaihua Qin, Antoine Cully, Benjamin Livshits, and Arthur Gervais

  58. [58]

    In2021 IEEE Symposium on Security and Privacy (SP), 919–936

    On the just-in-time discovery of profit-generating transactions in defi protocols. In2021 IEEE Symposium on Security and Privacy (SP), 919–936

  59. [59]

    Liyi Zhou, Kaihua Qin, Christof Ferreira Torres, Duc V Le, and Arthur Gervais

  60. [60]

    In2021 IEEE Symposium on Security and Privacy (SP), 428–445

    High-frequency trading on decentralized on-chain exchanges. In2021 IEEE Symposium on Security and Privacy (SP), 428–445. doi:10.1109/SP40001.20 21.00027. 14 An Analysis of Ethereum Builder Centralization Appendix A Complete List of High-Exclusivity Order Flow Sources Table 1 presents 20 exclusive order flows with the highest exclusiv- ity. B Non-Atomic ME...

    work page doi:10.1109/sp40001.20