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

Russian

EIP-4844 дал Ethereum rollups отдельное измерение сборов для данных, регулируемое правилом умножения обновлений, которое нацелено на три «blobs» за блок. Достижение этой цели зависит от того, сколько независимых, самозаинтересованных rollups выбирают для публикации данных, вопрос, который закрытые формы моделей адресованы только для одного агента под экзогенные цены или в статическом балансе. Мы предлагаем калиброванную мульти-агент усилитель учебно-обучающую среду для рынка сборов blob, в которой база сборов blob является эндогенной, установленной EIP-4844 правила на агрегированные решения гетерогенных rollups на пост-Dencun Ethereum данных. После подтверждения того, что окружающая среда воспроизводит извест

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