What Makes Cross-Variable Mixing Effective? Variable-Specific Value Interfaces for Multivariate Time-Series Forecasting

Recent multivariate time-series forecasting methods increasingly exploit cross-variable dependencies through mixing mechanisms that determine which variables communicate and how strongly. However, variables often differ in periodicity, phase, lag, and predictive role. Selecting a useful source therefore does not determine how its information should be expressed for different targets, yet existing mixers typically aggregate values in a shared latent space. We revisit cross-variable aggregation through variable-specific value interfaces. Our FrameMix operator equips variables with learnable orthogonal value interfaces for expressing outgoing information and interpreting aggregated information. Composing the source and receiver interfaces yields a distinct value transformation for every ordered variable pair without directly learning pairwise operators. We incorporate FrameMix into Frameformer, a patch-based architecture that mixes variables at each patch position before modeling temporal dependencies within each variable. Experiments show that variable-specific interfaces improve multiple routing mechanisms and remain effective with both learned and fixed frames, indicating that their gains cannot be explained by additional trainable capacity alone. More expressive pair-specific alternatives require substantially more parameters but provide no reliable accuracy gains. Frameformer achieves the lowest error on six of eight benchmarks at horizon 96 and is most effective on strongly coupled multivariate data, while results at horizons 192 to 720 on six benchmarks are mixed.