Dr-CiK: A Benchmark testing Deep Research for Context-Aided Forecasting

Time series forecasting in real-world settings often depends not only on historical observations, but also on external context that must be actively discovered from noisy, heterogeneous information sources. Yet existing context-aided forecasting benchmarks typically assume that the supporting context is already provided, leaving open whether agents can identify it on their own. Therefore, we introduce Dr-CiK, a benchmark for evaluating whether agents can retrieve forecasting-relevant supporting context from a document corpus, filter out distractors, distill the retrieved context into forecast-useful evidence, and generate forecasts supported by that evidence. Through context ablations and evaluations of state-of-the-art deep research and forecasting methods paired together, we show that high-quality context substantially improves forecasting performance in Dr-CiK. However, existing DR methods recover limited ground-truth supporting evidence, are frequently misled by distractors, and can cause forecasters to perform worse with retrieved context than without context. Our results motivate research on agents that search for the right context to predict the future.