CLAIR: Consistent LLM Adaptation via Index-free Reprogramming

Large language models (LLMs) have been adapted to time-series forecasting through patch-based reprogramming and task-specific conditioning. However, aligning contextualized patch representations requires choosing which patches to compare, while representation agreement alone does not ensure forecast agreement. We propose CLAIR, an asymmetric teacher-guided framework for Consistent LLM Adaptation via Index-free Reprogramming that couples patch-level transport alignment (PTA) with forecast-level consistency (FC). A clean-view exponential moving average (EMA) teacher provides a shared reference for a student processing a mildly perturbed view of the same input. PTA uses entropically regularized optimal transport to establish soft correspondences between post-backbone student and teacher patch representations, while FC encourages agreement between their predictions. Together with structure-aware input conditioning, these objectives complement the supervised forecasting loss without reordering the patch states used for prediction. Experiments across long-term, short-term, and few-shot forecasting demonstrate competitive performance against representative LLM-based and non-LLM forecasters, with mean relative reductions in horizon-averaged MSE of 3.1% and 3.8% across six benchmarks under the 10% and 5% few-shot settings, respectively, relative to the best evaluated LLM baseline on each dataset. Controlled comparisons on ETTh1 and ETTm1 show that PTA and index-wise alignment perform comparably without FC. When both are combined with FC, PTA reduces horizon-averaged MSE by 2.1% and 2.6%, respectively, relative to index-wise alignment.