Volatility control converts risk estimates into portfolio exposure, yet existing approaches often rely on a fixed volatility estimator or a pre-defined control rule that may not adapt to changing market conditions. We propose VolRouter, a modular framework that formulates volatility control as state-conditioned routing over estimator-controller pairs. VolRouter first summarizes market conditions into a control-relevant state profile and then performs routing through three stages: state inference, switch review, and pair selection. The Router can be implemented using rule-based, learnable, or LLM-based decision modules, while portfolio actions remain generated by predefined control policies. We evaluate VolRouter across S&P 500, Multi-Asset, Bitcoin, and USDT volatility-control settings. VolRouter achieves the highest Sharpe ratio in three of four settings. On S&P 500, it improves Sharpe from 0.952 for RV + Naive Scaling to 1.222 while reducing maximum drawdown from 15.10% to 12.58% and daily CVaR from 1.76% to 1.32%. On Multi-Asset, it improves Sharpe from 1.498 to 1.540 and reduces CVaR from 1.56% to 1.18%. Bitcoin shows similar improvements in risk-adjusted performance, while USDT provides a boundary case where simpler state-aware selectors remain competitive. Ablation and sensitivity analyses show that the improvement comes from relative policy evaluation and selective persistent switching rather than simply expanding the policy library. These results suggest that volatility control can be viewed as a policy-selection problem when risk management requirements vary across market states.
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.