金融时序预测别急着堆参数，试试把市场状态信息只用来做路由。RG-ResMoE在1027只美股上比MLP更准更稳，软路由还改善了VaR校准。
本文提出RG-ResMoE架构，用于1027只美国股票的5天已实现波动率预测。该模型将制度信息仅用于专家路由，而非直接参与预测。在容量匹配实验中，RG-ResMoE在预测准确性和训练稳定性上优于标准MLP，并在日本股票面板上复现了类似增益。将制度变量直接拼接到输入会降低性能，而仅作为路由门控输入则改善预测精度和VaR校准。软路由一致优于硬路由。
Regime-Gated Residual Mixture-of-Experts for Cross-Sectional Volatility Forecasting
Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1,027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning, and random seeds are matched across architectures. We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting. The base predictor models volatility from stock features, while a gating network uses regime state variables to route residual corrections. RG-ResMoE consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study. Similar gains are observed on an independent Japanese panel. The integration pathway is decisive: appending the same regime variables directly to the forecasting input degrades both predictive performance and training stability, whereas restricting them to the routing gate improves accuracy and Value-at-Risk calibration. Hard routing consistently underperforms soft routing. The results suggest that, in compact neural volatility forecasting models, the primary value of mixture-of-experts models lies less in increasing model capacity than in controlling how nonstationary regime information influences prediction.