Reliability-Conditioned Multi-Modal Retrieval for Time-Series Analysis with In-Context Learning

Time-series analysis increasingly combines numerical trajectories with textual context, yet the two modalities provide complementary and unevenly reliable evidence. Existing language-model pipelines improve temporal representation or retrieve demonstrations through lexical, semantic, or temporal similarity, but they usually apply one retrieval rule to every query or require downstream model feedback to learn selection. We introduce Reliability-Conditioned Query Adaptive Similarity for Hybrid retrieval (RC-QASH), a multi-modal In-Context Learning (ICL) approach that treats text/time-series fusion as a conservative query level reliability decision. Using only nested out-of-fold (OOF) records from the demonstration bank, its gate summarizes view-specific class distributions, confidence, disagreement, and ranking overlap, then either retains the Hybrid anchor or shifts the modality balance. On Finance and Weather, the full pipeline achieves the best cross-domain point estimates in the DeepSeek baseline comparison, with equal-domain mean Accuracy of 57.75% and PRIMARY of 49.19%, where PRIMARY averages balanced accuracy and macro-F1. Relative to Time-Series Top-3, the strongest reported comparator on these aggregate metrics, these scores correspond to improvements of 6.20% and 6.52%, respectively. Shared banks, prompt rendering, and fixed decoding settings isolate retrieval changes. Matched DeepSeek and Llama comparisons evaluate the controller on changed prompts. The gate concentrates its changes on queries where static Hybrid retrieval is weaker, and these comparisons show positive mean gains within the selected subsets. These results show that retrieval geometry can support selective multi-modal adaptation without downstream-output supervision.