AI 中文总结
本文提出基于HotStuff的高性能BFT共识区块时间混合建模诊断方法，应用于Hyperliquid与Aptos主网，揭示二者部署异质性差异，为BFT共识分析提供实用诊断工具。
AI 中文摘要
高性能拜占庭容错（BFT）区块链旨在实现高吞吐量和低延迟，但其观测到的区块时间分布往往呈现出由网络、流水线和部署异质性引发的复杂行为。本文中，我们通过基于仲裁组的多播框架对区块时间进行建模，将每个区块间隔与仲裁组形成延迟关联，以此诊断基于HotStuff的高性能BFT共识。我们使用混合模型捕捉多模态区块时间分布，其中每个分量代表由区块信息有效传输速率表征的不同网络条件。该模型被拟合到主网区块时间数据的主体部分，同时单独分析尾部衰减以评估渐近行为。将此方法应用于Hyperliquid和Aptos主网后，我们发现Hyperliquid可由单峰分布很好地解释，这与其相对同质的验证节点部署一致；相比之下，Aptos呈现持续的多模态结构，且在共识升级后出现明显偏移，反映出异构部署和多样的通信路径。这些结果表明，区块时间的混合建模为分析和监测高性能BFT共识提供了实用且信息丰富的诊断工具。
英文摘要
High-performance Byzantine Fault Tolerant (BFT) blockchains are designed to achieve high throughput and low latency, yet their observed block time distributions often reveal complex behaviors arising from networking, pipelining, and deployment heterogeneity. In this paper, we diagnose HotStuff-based high-performance BFT consensus by modeling block times through a quorum-based multicast framework that links each block interval to quorum formation latency. We capture multimodal block time distributions using mixture models, where each component represents a distinct network condition characterized by effective transfer rate of block information. The proposed model is fitted to the bulk of mainnet block time data, while tail decay is analyzed separately to assess asymptotic behavior. Applying this methodology to Hyperliquid and Aptos mainnets, we find that Hyperliquid is well explained by a unimodal distribution, consistent with a relatively homogeneous validator deployment. In contrast, Aptos exhibits persistent multimodal structure and a pronounced shift following a consensus upgrade, reflecting heterogeneous deployments and diverse communication paths. These results demonstrate that mixture modeling of block time provides a practical and informative diagnostic tool for analyzing and monitoring high-performance BFT consensus.
Comments9 pages, 4 figures, published as a full paper at 2026 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)
DOI：10.1109/ICBC67748.2026.11575517
