AGENTIC-SYSTEMSJune 6, 20261 min read8 views

GITCO: Gated Inference-Time Context Optimization in TSFMs

Researchers have introduced GITCO, a lightweight framework that optimizes input context for Time Series Foundation Models (TSFMs) at inference time. By selectively suppressing harmful data patches without any parameter updates, GITCO significantly improves zero-shot forecasting accuracy.

Computer Science > Artificial Intelligence

Title:GITCO: Gated Inference-Time Context Optimization in TSFMs

View PDF HTML (experimental)Abstract:Patch-based Time Series Foundation Models (TSFMs) suffer from 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 GITCO (Gated Inference-Time Context Optimization), a lightweight three-component framework: Gate, Router, and 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 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.

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Source: arXiv cs.AI Recent

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