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[2602.20494] KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
Summary
The paper introduces KairosVL, a novel framework that enhances time series analysis by integrating semantic reasoning, achieving competitive performance in various tasks.
Why It Matters
As time series data becomes increasingly complex, traditional numerical models fall short. This research highlights the importance of semantic understanding in time series analysis, addressing a critical gap in the field and offering a practical solution for real-world applications.
Key Takeaways
- KairosVL combines semantic reasoning with time series analysis for improved decision-making.
- The framework utilizes a two-round reinforcement learning approach to enhance reasoning capabilities.
- Extensive experiments show significant performance boosts and better generalization to unseen scenarios.
Computer Science > Artificial Intelligence arXiv:2602.20494 (cs) [Submitted on 24 Feb 2026] Title:KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning Authors:Haotian Si, Changhua Pei, Xiao He, Zeyan Li, Zhe Xie, Zexin Wang, Jiyao Hu, Zhaoyang Yu, Tieying Zhang, Dan Pei, Jianhui Li, Gaogang Xie View a PDF of the paper titled KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning, by Haotian Si and 11 other authors View PDF HTML (experimental) Abstract:Driven by the increasingly complex and decision-oriented demands of time series analysis, we introduce the Semantic-Conditional Time Series Reasoning task, which extends conventional time series analysis beyond purely numerical modeling to incorporate contextual and semantic understanding. To further enhance the mode's reasoning capabilities on complex time series problems, we propose a two-round reinforcement learning framework: the first round strengthens the mode's perception of fundamental temporal primitives, while the second focuses on semantic-conditioned reasoning. The resulting model, KairosVL, achieves competitive performance across both synthetic and real-world tasks. Extensive experiments and ablation studies demonstrate that our framework not only boosts performance but also preserves intrinsic reasoning ability and significantly improves generalization to unseen scenarios. To summarize, our work highlights the potential of combining semantic reasoning with temporal modeling an...
