GITCO: Gated Inference-Time Context Optimization in TSFMs

Patch-based Time Series Foundation Models (TSFMs) suffer from \textit{context poisoning:} structurally anomalous patches capture disproportionate attention and silently degrade zero-shot forecast quality. We propose improving TSFM accuracy at inference time by optimizing the input context rather than modifying model weights. We present \textbf{GITCO} (Gated Inference-Time Context Optimization), a lightweight three-component framework: \textit{Gate, Router,} and \textit{Critic} that selectively identifies and suppresses harmful patches without any parameter updates. Evaluated on TimesFM 2.5 across 53 GIFT-Eval datasets under K-fold cross-validation, GITCO achieves an average +1.95\% MASE reduction on TimesFM 2.5 while capturing 89.9\% of the improvement upper bound. We introduce \textit{context sensitivity profiles} as a new characterizable property of TSFMs: the mapping from time series meta-features to expected accuracy improvement under inference-time context intervention, shaped jointly by model architecture and the statistical structure of the data.