• Computer Science > Artificial Intelligence [Submitted on 24 Feb 2026] Title:KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning 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 and provides a practical framework for real-world time series intelligence, which is in urgent demand. • References & Citations export BibTeX citation Loading…

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Researchers have introduced KairosVL, a model that extends time‑series analysis by integrating semantic context into the reasoning process. The paper defines a new “Semantic‑Conditional Time Series Reasoning” task, moving beyond purely numerical modeling to include contextual understanding. KairosVL employs a two‑round reinforcement‑learning framework: the first round trains the model on fundamental temporal primitives, while the second focuses on semantic‑conditioned reasoning. Experiments on synthetic and real‑world datasets show that the approach not only matches but often surpasses existing methods, preserves intrinsic reasoning abilities, and improves generalization to unseen scenarios. The work highlights the practical potential of combining temporal modeling with semantic reasoning for real‑world time‑series intelligence.

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