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arxiv: 2604.23266 · v1 · submitted 2026-04-25 · 💻 cs.DC

Recognition: unknown

The Blockchain Execution Dilemma: Optimizing Revenue XOR Fair Ordering

Artjom Pugatsov , Can Umut Ileri , J\'er\'emie Decouchant
Authors on Pith no claims yet

Pith reviewed 2026-05-08 07:13 UTC · model grok-4.3

classification 💻 cs.DC
keywords blockchaintransaction orderinggenetic algorithmfair orderingMEVvalidator revenueexecution layercongestion
0
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The pith

A genetic algorithm for transaction sequencing increases validator profits by about 15% while accelerating congestion relief by up to 58%.

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

The paper tries to resolve the conflict between maximizing validator revenue through flexible transaction ordering and enforcing fairness to limit MEV attacks. It models sequencing as a continuous process where block executions can overlap and introduces an anytime genetic algorithm that takes gas prices, object sets, and predicted execution times as inputs to build schedules. Tests on real datasets from Sui and Ethereum show the method raises profits by roughly 15 percent and clears congestion up to 58 percent faster. The work also measures that strict fair-ordering rules cut revenue by 50 to 60 percent in busy periods and erase the gains from advanced sequencing. A reader would care because the results point to a concrete way for blockchains to improve both economic returns and ordering equity at the same time.

Core claim

We introduce a blockchain-independent model for transaction sequencing in a continuous setting where block executions can overlap. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that our algorithm increases validator profit by approximately 15% and accelerates congestion relief by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing they can reduce validator revenue by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict 4

What carries the argument

The anytime genetic algorithm operating inside a continuous model of overlapping block executions, taking gas prices, object sets, and predicted execution times to produce optimized transaction schedules.

If this is right

  • Validator profit rises by approximately 15% when using the optimized schedules.
  • Congestion relief accelerates by up to 58%.
  • Strict fair-ordering constraints reduce validator revenue by 50% to 60% during high congestion.
  • Enforcing strict fair ordering nullifies the advantages of advanced sequencing.

Where Pith is reading between the lines

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

  • Validators could embed similar real-time optimizers into block production to capture more value while still satisfying some fairness requirements.
  • The continuous overlapping-execution model suggests future blockchains could move toward pipelined or streaming transaction processing.
  • Practical success hinges on improving the accuracy of execution-time predictions from transaction properties alone.
  • The same tradeoff between revenue and ordering constraints may appear in other distributed systems that allocate scarce execution resources.

Load-bearing premise

That predicted execution times derived from transaction properties remain accurate enough in real time for the genetic algorithm to produce usable schedules before the next block must be produced.

What would settle it

Deploying the genetic algorithm on a live blockchain such as Sui or Ethereum and directly measuring whether validator profits rise by the claimed 15% and congestion clears 58% faster than with current ordering methods.

Figures

Figures reproduced from arXiv: 2604.23266 by Artjom Pugatsov, Can Umut Ileri, J\'er\'emie Decouchant.

Figure 1
Figure 1. Figure 1: Modular architecture of a lazy blockchain. The se view at source ↗
Figure 2
Figure 2. Figure 2: Greedy sequencing of transactions based on gas price view at source ↗
Figure 3
Figure 3. Figure 3: Processing of blocks b1 to b5 by the different blockchain layers. Note the increased sequencing time for b3, which is triggered by congestion detected during the execution of b1. Furthermore, the sequencing of b3 was terminated early to satisfy continuous execution requirement, since a worker becomes free after executing b2 while other workers are still executing b3. Algorithm 1: Greedy scheduler input : L… view at source ↗
Figure 4
Figure 4. Figure 4: High level overview of the genetic algorithm for view at source ↗
Figure 5
Figure 5. Figure 5: Log-scaled distribution and density plots for gas price, execution time, and total number of touched objects per transaction view at source ↗
Figure 6
Figure 6. Figure 6: Log-scaled distribution and density plots for gas price and execution time for Ethereum data. Includes a least-squares view at source ↗
Figure 7
Figure 7. Figure 7: Average Jaccard similarity (computed over object sets) view at source ↗
Figure 11
Figure 11. Figure 11: Total absolute number of transactions deferred under view at source ↗
Figure 10
Figure 10. Figure 10: Total absolute number of transactions deferred under view at source ↗
Figure 13
Figure 13. Figure 13: Total normalized validator profit under sustained view at source ↗
Figure 16
Figure 16. Figure 16: Total absolute number of transactions deferred under view at source ↗
Figure 17
Figure 17. Figure 17: Total absolute number of transactions deferred under view at source ↗
read the original abstract

The successive generations of consensus algorithms have progressively shifted the performance bottleneck of blockchains to the execution layer. While recent works address this by parallelizing transaction execution, they often overlook the critical role of transaction sequencing. Historically, transaction ordering was left to validator discretion, a practice prone to Maximal Extractable Value (MEV) attacks, or rigid fair-ordering protocols that limit validator revenue. In this work, we address the tension between validator revenue and order fairness using a dynamic optimization framework. We introduce a blockchain-independent model for transaction sequencing in a continuous setting where block executions can overlap. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that our algorithm increases validator profit by approximately 15% and accelerates congestion relief by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing they can reduce validator revenue by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict fair ordering effectively nullifies the advantages of advanced sequencing.

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 / 1 minor

Summary. The paper introduces a blockchain-independent dynamic optimization framework for transaction sequencing in a continuous overlapping-block execution model. It proposes an anytime genetic algorithm that selects and orders transactions based on gas prices, object sets, and predicted execution times. Using real-world traces from Sui and Ethereum, the work claims that this algorithm increases validator profit by approximately 15%, accelerates congestion relief by up to 58%, and that imposing strict fair-ordering constraints reduces validator revenue by 50-60% during high congestion, effectively nullifying benefits of advanced sequencing.

Significance. If the performance numbers hold after validation of the execution-time predictions, the result would be significant for blockchain systems research: it provides the first quantitative evidence of the revenue-fairness trade-off in execution ordering and demonstrates a practical, blockchain-agnostic scheduler that can operate under real-time constraints. The use of two distinct real-world datasets is a clear strength.

major comments (2)
  1. [Evaluation section] The central claims (15% profit increase, 58% congestion relief, 50-60% revenue reduction) rest on the genetic algorithm's use of predicted execution times, yet the evaluation provides no direct accuracy metric (e.g., mean absolute percentage error or correlation with congestion level) between these predictions and measured runtimes on the same Sui and Ethereum transactions. This is load-bearing for the real-time applicability claim.
  2. [Abstract and Evaluation section] The abstract and evaluation report performance numbers without describing the simulator, baseline schedulers, measurement methodology, error bars, or how the continuous overlapping-block model was instantiated on the traces. Without these details the reported gains cannot be reproduced or assessed for robustness.
minor comments (1)
  1. [Algorithm description] Clarify the exact fitness function and termination criteria of the anytime genetic algorithm, including how overlapping block executions are modeled in the continuous setting.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed review. The comments highlight important areas for improving reproducibility and validation of our claims. We address each major comment below and commit to revisions that strengthen the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [Evaluation section] The central claims (15% profit increase, 58% congestion relief, 50-60% revenue reduction) rest on the genetic algorithm's use of predicted execution times, yet the evaluation provides no direct accuracy metric (e.g., mean absolute percentage error or correlation with congestion level) between these predictions and measured runtimes on the same Sui and Ethereum transactions. This is load-bearing for the real-time applicability claim.

    Authors: We acknowledge that the submitted manuscript does not report direct accuracy metrics for the execution-time predictions. The predictions are generated from a lightweight model based on historical gas usage, object access patterns, and transaction size from the traces, but no quantitative validation (such as MAPE or congestion correlation) was included. In the revised version, we will add a dedicated subsection in Evaluation that replays the Sui and Ethereum transactions in a controlled environment to compute actual runtimes, then reports MAPE, root-mean-square error, and Pearson correlation between predictions and measurements, stratified by congestion level (measured via pending transaction queue depth). This will directly support the real-time applicability claim. revision: yes

  2. Referee: [Abstract and Evaluation section] The abstract and evaluation report performance numbers without describing the simulator, baseline schedulers, measurement methodology, error bars, or how the continuous overlapping-block model was instantiated on the traces. Without these details the reported gains cannot be reproduced or assessed for robustness.

    Authors: We agree that the current manuscript lacks sufficient methodological detail for full reproducibility. In the revision, we will expand the Evaluation section with: (i) a complete description of the discrete-event simulator, including how the continuous overlapping-block model is implemented (e.g., allowing concurrent execution of non-conflicting transactions across block boundaries with explicit timing of state commits); (ii) precise definitions of all baseline schedulers (gas-price greedy, FCFS, and strict fair-ordering variants); (iii) the exact formulas and data-processing steps used to compute validator profit, congestion-relief time, and revenue reduction on the traces; (iv) error bars or confidence intervals derived from multiple trace segments or randomized runs; and (v) a step-by-step account of trace preprocessing and model instantiation for both Sui and Ethereum datasets. We will also revise the abstract to reference the expanded Evaluation section for these details. revision: yes

Circularity Check

0 steps flagged

No significant circularity; claims rest on external dataset evaluation

full rationale

The paper's central claims (15% profit increase, 58% congestion relief, 50-60% revenue reduction under fair ordering) are obtained by running an anytime genetic algorithm on real-world Sui and Ethereum transaction traces. No derivation step reduces a reported performance metric to a quantity defined by the paper's own fitted parameters or self-citations. The 'predicted execution times' are treated as an external input to the scheduler; their accuracy is an unverified modeling assumption rather than a self-referential definition. This places the work in the normal 0-2 range for papers whose results are benchmarked against independent data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claims rest on the ability to predict execution times from transaction features and on the genetic algorithm finding high-quality schedules within block-production time limits; no free parameters or invented entities are explicitly named in the abstract.

axioms (1)
  • domain assumption Execution times of transactions can be predicted with sufficient accuracy from gas prices, object sets, and historical data
    Required for the genetic algorithm to optimize schedules in advance of actual execution.

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discussion (0)

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