MARS: Multi-resolution Adaptive Routing for Sequential Recommendation

Scaling sequential recommendation to long user histories requires compressing diverse behavioral evidence into memories that can be scored efficiently against many candidates. We first provide evidence that real user histories exhibit multi-scale semantic structure: short-lived intent, medium-term interests, and long-term preferences can coexist in the same sequence. However, existing summarization-based models mainly optimize compact history compression and do not explicitly organize cached user memory across temporal scales, which can lead to \textit{temporal aliasing} where distinct behavioral signals become entangled before prediction. We propose MARS, a multi-resolution user memory that writes the full history into recurrent state tracks initialized with different half-life priors. A sparse routing reader then materializes compact seed memories by selecting the relevant temporal resolutions for each seed, preserving fixed-size candidate scoring. Experiments across recommendation datasets show that MARS outperforms strong recommendation baselines, with gains most pronounced for users with long histories.