PortfolioRouter: Consequence-Aware Multi-Task Routing for Portfolio Agents

Portfolio agents can recommend materially different allocations for the same trading date, but standard best-expert classification does not weight errors by their financial cost. The cost of a routing error depends on the return forgone by choosing one proposal over another. We introduce PortfolioRouter, a consequence-aware portfolio router that learns when agent choice matters and evaluates the value of switching between proposals. Its multi-task routing module uses market-state representations and current portfolio proposals to jointly learn a general selector, a critical-date detector, a specialist trained for critical-date selection, and an auxiliary prediction of volatility-normalized relative asset returns. A default-relative value model then uses realized historical feedback to estimate whether changing the default is worthwhile. Its objective penalizes harmful replacements, and estimates across history lengths are combined conservatively. Selective deployment uses a gate calibrated on the preceding quarter to decide whether to add a switch. In U.S. and China backtests covering 2025 through June 2026, with same-close execution and zero transaction costs, PortfolioRouter achieves mean returns of 34.86% and 45.92%. These are 8.69 and 13.59 percentage points higher than the same models’ general paths. Retaining only the switches on retrospectively identified critical dates still adds 3.99 and 11.10 points. Code and artifacts are avaliable in https://anonymous.4open.science/r/PortfolioRouter.