One Model, Three Views: Input-Condition Integration for Probabilistic Time Series Foundation Models

Recent time series foundation models (TSFMs) can condition on known future covariates, but this conditioning helps some targets and samples while hurting others, and which ones cannot be told in advance. By varying only its inputs, a single frozen TSFM yields three views of the same forecast: from the target's history alone (target-only), with past covariates added (multivariate-history), and with known future covariate values added (known-future). We show that replaying how forecasts change across these views on completed historical events reveals whether richer conditioning was beneficial, harmful, or insufficient. Based on this finding, we propose input-condition integration, which corrects the known-future forecast using the other two views as references rather than as candidates to select or average. The correction is learned from the observed input history alone and applied only when it proves reliable there, leaving the TSFM unchanged. Across three TSFM backbones, input-condition integration improves probabilistic accuracy over the known-future forecast on real-world benchmarks and better recovers covariate-driven responses in controlled synthetic tasks. These results suggest that the value of additional inputs lies not only in the information they supply, but in how the model responds to them.