Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
Ning, Kanghui, Pan, Zijie, Jiang, Yushan, Schneider, Anderson, Nevmyvaka, Yuriy, Song, Dongjin
–arXiv.org Artificial Intelligence
Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding, structured multi-step reasoning, and faithful evaluation frameworks. The other advances system-level reasoning, moving beyond language-only explanations by incorporating multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. Together, these directions outline a flexible and extensible framework for advancing time series reasoning, aiming to deliver interpretable and trustworthy temporal intelligence across diverse domains.
arXiv.org Artificial Intelligence
Oct-21-2025
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- North America > United States
- Connecticut > Tolland County > Storrs (0.15)
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