Newest papers

Do Rollups Self-Organize? Multi-Agent Reinforcement Learning in the EIP-4844 Blob Fee Market

Alessio Brini · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.15256 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

因此,EIP-4844给了EthereumRollups一个单独的数据收费维度,由一次性更新规则来管理,该规则针对每个区块的收费为3个“blobs”。这个目标取决于有多少独立的、自私的Rollups选择发布数据,一个问题是,封闭式模型只针对一个单一的代理人,适合后DencunEthereum数据或静态平衡。我们建议对blob收费市场进行校准的多代理增强学费学习环境,其中blob基础收费是内在的,由EIP-4844规则确定,对异性变形的集成决策的规则仅适用于后DencunEthereum数据。在确认环境重现了已知的单代理分析增强收费最佳环境后,我们表明,

Machine translation (Meta M2M100); the English original follows.

Abstract

EIP-4844 gave Ethereum rollups a separate fee dimension for data, governed by a multiplicative update rule that targets three ``blobs'' per block. Whether this target is reached depends on how many independent, self-interested rollups choose to post data, a question that closed-form models address only for a single agent under exogenous prices or in a static equilibrium. We propose a calibrated multi-agent reinforcement learning environment for the blob fee market in which the blob base fee is endogenous, set by the EIP-4844 rule on the aggregated decisions of heterogeneous rollups fit to post-Dencun Ethereum data. After confirming that the environment reproduces the known single-agent analytical optimum, we show that independent learners, with no convergence guarantee, still settle near the target without coordination ($3.32\pm0.01$ blobs per block over five seeds). Over a fiftyfold demand range ($10\times$ to $500\times$), supply stays at the target from $10\times$ to $200\times$ and rises above it at higher demand, where the fee can no longer hold it. Posting frequency increases with rollup size, an emergent analog of the size threshold predicted by prior theory. A costless layer-1 calldata outside option pushes the fee down by roughly an order of magnitude while aggregate supply stays at the target, so the fee rule acts as a negative-feedback controller on decentralized supply.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Multiplicative function, Dimension (graph theory), Set (abstract data type), Range (aeronautics), Aggregate (composite), Reinforcement learning

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange