Maximum Extractable Value (MEV) Mitigation Approaches in Ethereum and Layer-2 Chains: A Comprehensive Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Maximal Extractable Value (MEV) represents a pivotal challenge within the Ethereum ecosystem; it impacts the fairness, security, and efficiency of both Layer 1 (L1) and Layer 2 (L2) networks. MEV arises when miners or validators manipulate transaction ordering (e.g., front-running) to extract additional value, often at the expense of other network participants. This not only affects user experience by introducing unpredictability and potential financial losses but also threatens the underlying principles of decentralization and trust. Given the growing complexity of blockchain applications, particularly with the increase of Decentralized Finance (DeFi) protocols, it is crucial to address the issue and reduce the impact of MEV. This paper presents a comprehensive survey of MEV mitigation techniques as applied to both Ethereum’s L1 and various L2 solutions. We provide a novel categorization of mitigation strategies. We also describe the challenges, ranging from transaction sequencing and cryptographic methods to reconfiguring decentralized applications (DApps) to reduce front-running opportunities. We investigate their effectiveness, implementation challenges, and impact on network performance. By synthesizing current research, real-world applications, and emerging trends, this paper aims to provide a detailed roadmap for researchers, developers, and policymakers to understand and combat MEV in an evolving blockchain landscape.
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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 | 0.001 |
| 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 it