Despite the prevalent assumption of uniform variable importance in long-term time series forecasting models, real-world applications often exhibit causal relationships and varying data acquisition costs. Specifically, cost‐effective exogenous data (e.g., local weather) can unilaterally influence dynamics of endogenous variables, like canopy or lake surface temperature. Exploiting these links enables more efficient forecasts when exogenous inputs are readily available. Transformer-based models capture long-range dependencies but incur high computation and suffer from permutation invariance. Patch-based variants improve efficiency yet can miss local temporal patterns. To efficiently exploit informative signals across both the temporal dimension and exogenous variables, this study proposes XLinear, a lightweight time series forecasting model built upon Multi-Layer Perceptrons (MLPs). XLinear uses MLPs and sigmoid activation to extract both temporal patterns and variate-wise dependencies, and employs a global token mapped from an endogenous variable as a pivotal hub for interacting with exogenous variables. Its prediction head then integrates these signals to forecast the endogenous series. It is evaluated on seven widely adopted benchmarks and five datasets with exogenous variables. Compared with state-of-the-art models, XLinear delivers superior accuracy and efficiency for both multivariate forecasts and univariate forecasts influenced by exogenous inputs.