Training-Free Continual Learning for Multimodal Forecasting via Prompt Evolution

Adapting a forecasting system to a changing world classically means retraining. We ask whether the task of continually learning regimes for time series forecasting can be represented by a piece of text: a guidance block, evolved by outcome-driven reflection and used in a frozen LLM forecaster's prompt. On a refreshed multimodal benchmark (weekly search indices, daily currencies, daily commodities, each window paired with web-retrieved news), we run GEPA-style prompt evolution under strict contamination control to prevent the data leakage common in time series benchmarks: the forecaster's knowledge cutoff predates every evaluated window. A $\sim$900-word evolved guidance carried by Llama-3.3-70B beats Time-Series Foundation Models (TSFMs) on held-out data (GM MASE 0.776 vs.\ 0.898 for TabPFN-TS-3), transfers to more recent models without measurable loss, and costs dollars rather than GPU-days to update. We characterize the loop: out-of-loop gains concentrate in the first reflection, in-loop improvements overstate out-of-loop gains $2$--$3\times$ unless an untouched monitor arbitrates, and error-only objectives silently suppress the text modality. Extensions compile the guidance into executable code with a TSFM as a callable tool, recovering most of the gain deterministically, and ablations show the advantage widens at short horizons while removing the news text does not hurt the guided forecaster. We argue that continual prompt evolution is a practical, training-free alternative to retraining for multimodal forecasting.