Quantifying Fairness Granularity as a Fair Ordering Policy Towards MEV Mitigation for Rollups
Bibliographic record
Abstract
Ethereum marked the beginning of stateful and Turing-Complete blockchains, where the final result of transactions depends on their execution order. This subtle distinction is of great import, especially in Decentralized Finance (DeFi) applications like exchanges or lending platforms, where execution order plays a key role in making profits or losses and gives adversarial actors enormous incentives to manipulate or influence the ordering of transactions on blockchains. Maximal Extractable Value (MEV) represents the potential profit block producers can gain by manipulating transaction inclusion within a block they create. Other blockchain participants can also extract MEV, often through tactics such as front-running attacks. The MEV problem also affects Layer-2 (L2) networks, which are a subset of stateful chains created to improve scalability for Layer-1 (L1) chains like Ethereum. Prominent examples of L2 networks include rollups such as Arbitrum and Optimism. To mitigate the MEV problem, many rollups are characterized by a single sequencer that employs the First-Come-First-Served (FCFS) transaction ordering policy, which prevents greedy reordering based on the value extracted per transaction. While FCFS policy guarantees order fairness by processing transactions according to receive times, it has some drawbacks, such as encouraging spam transactions to ensure early inclusion in a block, and sequencer orderings favoring users with lower latency. To reduce the risks of the FCFS ordering algorithm, we propose a fair ordering mechanism by adding fairness granularity to the original FCFS policy. We then introduce a method to measure the granularity interval of the Arbitrum chain, using a statistical technique that can be adapted for use with other L2 chains. We evaluate our proposed ordering algorithm using a dataset based on Arbitrum network specifications and quantify the accuracy of our final ordering by measuring its proximity to the ideal ordering. Our results show a high accuracy with different network latencies and different datasets. We also assess the effectiveness of our approach for MEV mitigation by reducing front-running compared to FCFS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".