Learning Composable Forecasting Operators via Block Infilling

Long-horizon forecasting is usually formulated either as a fixed mapping to a target window or as a recursive process that advances through its own predictions. We ask whether forecasting can instead be organized around a reusable local prediction problem. We introduce Augure, which learns a shared masked block-infilling operator and composes it across future chunks. The same operator is used at every chunk, predicts each chunk jointly, and can be applied additional times at inference without changing its parameters or output head. Across standard probabilistic forecasting benchmarks, this inductive bias is competitive with direct, recurrent, autoregressive, and diffusion-based alternatives. Controlled ablations show that the local training problem matters: masked infilling, exposure to varying prefix lengths, and the absence of explicit rollout-depth specialization each affect the quality of the resulting operator. A checkpoint trained at $H=96$ can be reused up to $H=720$, with little native-relative cost on some datasets and larger degradation on others. These results suggest that masked block infilling provides a useful compositional inductive bias while also exposing a central limitation of repeated reuse:performance can degrade as model-generated prefixes accumulate.