Dr-CiK：面向预见驱动型智能体的测试平台
Dr-CiK: A Benchmark testing Deep Research for Context-Aided Forecasting
AI总结：
针对现有上下文辅助预测基准假设上下文已提供的问题，提出Dr-CiK基准，评估智能体从文档语料库中检索、过滤、提炼预测相关上下文并生成预测的能力，实验表明高质量上下文显著提升预测性能，但现有深度研究智能体恢复证据不足5%、易受干扰误导。
摘要：
现实环境中的时间序列预测通常不仅依赖于历史观测，还依赖于必须从嘈杂、异构的信息源中主动发现的外部上下文。然而，现有的上下文辅助预测基准通常假设支持性上下文已经提供，未考虑智能体是否能自行识别。因此，我们引入Dr-CiK，一个用于评估智能体是否能够从文档语料库中检索预测相关的支持性上下文、过滤干扰项、将检索到的上下文提炼为对预测有用的证据，并生成由该证据支持的预测的基准。通过上下文消融实验以及对最先进的深度研究和预测方法的联合评估，我们表明高质量上下文显著提高了Dr-CiK中的预测性能。然而，大多数现有的深度研究智能体仅能恢复一小部分真实支持证据（通常<5%），经常被干扰项误导（>80%的干扰项引用），并且可能导致预测器在使用检索到的上下文时比不使用上下文时表现更差。我们的结果激励了对预见驱动型智能体的研究，这些智能体能够搜索正确的上下文以预测未来。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
