发表机构
Princeton University; Talarion（普林斯顿大学; Talarion）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出广义奖励模型和ε-最优策略计算方法，分析MEV存在下RANDAO操纵的敏感性，发现奖励滚动或超线性增长会放大操纵激励，并证明尾部罚没可恢复诚实均衡。
AI 中文摘要
以太坊的随机信标（RANDAO）众所周知是可操纵的，先前的工作[AW24]计算了策略性提议者可以提议区块的精确比例。然而，提议区块的比例只是参与者奖励的代理指标。我们提出了一个广义奖励模型，该模型涵盖了许多典型的奖励形式：共识奖励滚动、多区块MEV、CEX-DEX套利、预言机操纵等。我们提供了一种方法论，可以为模型中的任何奖励方案（特别是上述奖励的任何组合）计算ε-最优策略。最后，我们将我们的方法论应用于几个典型示例，并确立了RANDAO操纵对底层奖励的敏感性。我们发现，如果奖励部分滚动，或随连续区块超线性增长，操纵RANDAO的激励会被放大。最后，我们研究了尾部时隙罚没（tail-slot slashing），它可以被建模为奖励函数，并表明诚实的均衡可以被恢复。
英文摘要
Ethereum's randomness beacon (RANDAO) is well-known to be manipulable, and prior work [AW24] computes the precise fraction of blocks a strategic proposer can propose. The fraction of blocks proposed, however, is only a proxy for participants' rewards. We propose a generalized reward model capturing many canonical forms of rewards: consensus reward rollover, multi-block MEV, CEX-DEX arbitrage, oracle manipulation, and others. We provide a methodology that computes an ε-optimal strategy for any reward scheme in our model (and in particular, any combination of the above rewards). Finally, we apply our methodology to several canonical examples, and establish the sensitivity of RANDAO manipulation to the underlying rewards. We find that if rewards partially roll over, or scale super-linearly with consecutive blocks, the incentive to manipulate RANDAO is amplified. Lastly, we investigate tail-slot slashing, which can be modeled as a reward function, and show that honest equilibria can be recovered.
Comments23 pages, 7 figures, full version of the AFT 2026 paper
