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

• URL: http://arxiv.org/abs/2605.04471v1
• Date: Wed, 06 May 2026 03:49:23 GMT
• Title: Order Flow Exclusivity and Value Extraction Mechanisms: An Analysis of Ethereum Builder Centralization
• Abstract summary: This study investigates the rapid centralization of the builder market under the Proposer-Builder Separation (PBS) architecture.<n>We analyze transactions from September 2023 to August 2025 to characterize Exclusive Order Flows (EOFs) and non-atomic Maximal Extractable Value (MEV)<n>A longitudinal analysis of builder strategies delineates the market's evolution into four distinct eras.
• Score: 10.100078660896108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• ComplexMCP: Evaluation of LLM Agents in Dynamic, Interdependent, and Large-Scale Tool Sandbox [61.862814740220806]
  $textbfComplexMCP$ is a benchmark designed to evaluate agents in rigorous conditions.<n>Built on the Model Context Protocol (MCP), $textbfComplexMCP$ provides over 300 meticulously tested tools.
  arXiv  Detail & Related papers  (2026-05-11T16:20:51Z)
• Hierarchical Mixture-of-Experts with Two-Stage Optimization [84.70724165894501]
  We propose Hi-MoE, a grouped MoE framework that decomposes routing control into two coupled levels.<n>We observe consistent improvements over recent sparse-routing and grouped-MoE baselines across NLP and vision benchmarks.<n>In large-scale pre-training on 58B tokens, Hi-MoE-7B achieves a 5.6% perplexity reduction and a 40% improvement in expert balance over OLMoE-7B.
  arXiv  Detail & Related papers  (2026-05-08T09:21:46Z)
• AlphaCrafter: A Full-Stack Multi-Agent Framework for Cross-Sectional Quantitative Trading [26.48283300875701]
  Building quantitative strategies that remain profitable demands the continuous coupling of factor discovery, regime-adaptive selection, and risk-constrained execution.<n>We introduce AlphaCrafter, a full-stack multi-agent framework that closes this gap through a continuously adaptive factor-to-execution pipeline.<n>Experiments on CSI 300 and S&P 500 demonstrate that AlphaCrafter consistently outperforms state-of-the-art baselines in risk-adjusted returns.
  arXiv  Detail & Related papers  (2026-05-07T01:58:30Z)
• SEA-Eval: A Benchmark for Evaluating Self-Evolving Agents Beyond Episodic Assessment [52.87618765740484]
  This paper defines the Self-Evolving Evolutionary Agent and introduces SEA-Eval -- the first benchmark designed specifically for evaluating SEAs.<n> SEA-Eval establishes $SR$ and $T$ as primary metrics and enables through sequential task stream design the independent of evolutionary gain, evolutionary stability, and implicit alignment convergence.
  arXiv  Detail & Related papers  (2026-04-10T05:49:50Z)
• LongCat-Flash-Prover: Advancing Native Formal Reasoning via Agentic Tool-Integrated Reinforcement Learning [46.294745464571456]
  LongCat-Flash-Prover is an open-source MoE model for agentic tool-integrated reasoning.<n>It sets a new state-of-the-art for open-weights models in both auto-formalization and theorem proving.<n>It achieves a 97.1% pass rate on MiniF2F-Test using only 72 inference budget per problem.
  arXiv  Detail & Related papers  (2026-03-22T05:16:09Z)
• SoK: The Evolution of Maximal Extractable Value, From Miners to Cross-Chain [0.0]
  We organize the fragmented literature on MEV into a unified chronological framework, beginning with EraI (August 2014 - August 2020)<n>We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena.<n>Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure.
  arXiv  Detail & Related papers  (2026-03-08T16:37:41Z)
• MEME: Modeling the Evolutionary Modes of Financial Markets [11.120179118542518]
  We introduce MEME, designed to reconstruct market dynamics through the lens of evolving logics.<n>MEME employs a multi-agent extraction module to transform noisy data into high-fidelity Investment Arguments.<n>Experiments on three heterogeneous Chinese stock pools from 2023 to 2025 demonstrate that MEME consistently outperforms seven SOTA baselines.
  arXiv  Detail & Related papers  (2026-02-12T13:16:05Z)
• Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets [57.179679246370114]
  In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices.<n>During deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.<n>Traditional robust RL approaches address this model misspecification by optimizing the worst-case performance over a set of uncertainties.<n>We develop a novel class of elliptic uncertainty sets, enabling efficient and tractable robust policy evaluation.
  arXiv  Detail & Related papers  (2025-10-22T18:22:25Z)
• Trade in Minutes! Rationality-Driven Agentic System for Quantitative Financial Trading [57.28635022507172]
  TiMi is a rationality-driven multi-agent system that architecturally decouples strategy development from minute-level deployment.<n>We propose a two-tier analytical paradigm from macro patterns to micro customization, layered programming design for trading bot implementation, and closed-loop optimization driven by mathematical reflection.
  arXiv  Detail & Related papers  (2025-10-06T13:08:55Z)
• Measuring CEX-DEX Extracted Value and Searcher Profitability: The Darkest of the MEV Dark Forest [9.219248112124703]
  We estimate a total of 233.8M USD extracted by 19 major CEX-DEX searchers from 7,203,560 identified CEX-DEX arbitrages.<n>Our analysis reveals increasing centralization trends as three searchers captured three-quarters of both volume and extracted value.<n>These insights illuminate the darkest corner of the MEV landscape and highlight the critical implications of CEX-DEX arbitrages for decentralization.
  arXiv  Detail & Related papers  (2025-07-17T11:50:42Z)
• Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation [55.2788567621326]
  We introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation.<n>By curating financial news headlines, FIN-FORCE supports LLM based forward counterfactual generation.<n>This paves the way for scalable and automated solutions for exploring and anticipating future market developments.
  arXiv  Detail & Related papers  (2025-05-26T02:41:50Z)
• The Early Days of the Ethereum Blob Fee Market and Lessons Learnt [4.181969992118843]
  EIP-4844 introduced blob transactions designed to meet the data availability needs of layer 2 protocols.<n>This work constitutes the first rigorous and comprehensive empirical analysis of transaction- and mempool-level data since March 13, 2024.
  arXiv  Detail & Related papers  (2025-02-18T15:47:49Z)
• Joint Latent Topic Discovery and Expectation Modeling for Financial Markets [45.758436505779386]
  We present a groundbreaking framework for financial market analysis. This approach is the first to jointly model investor expectations and automatically mine latent stock relationships. Our model consistently achieves an annual return exceeding 10%.
  arXiv  Detail & Related papers  (2023-06-01T01:36:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.