arXiv:2605.27904v1 Announce Type: cross Abstract: 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 distrac
The proliferation of context-dependent AI applications makes robust evaluation of agentic foresight crucial, particularly as current benchmarks often provide context rather than requiring discovery.
This development addresses a critical gap in AI agent evaluation, enabling the assessment of an agent's ability to autonomously identify and filter relevant information, which is central to building effective autonomous systems.
The introduction of Dr-CiK shifts the focus of AI agent benchmarking from merely processing provided context to actively discovering and discerning it from noisy, heterogeneous data sources.
- · AI researchers
- · Autonomous system developers
- · AI evaluation platforms
- · AI models without robust information retrieval capabilities
- · Benchmarks that pre-select context
Improved foresight capabilities in AI agents become a new differentiator in their performance metrics.
The development of more sophisticated AI components specialized in context discovery and relevance filtering accelerates.
Autonomous agents gain increased reliability in real-world, uncertain environments, expanding their deployment across complex domains.
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Read at arXiv cs.LG