KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
#KairosVL #Time Series Analysis #Semantic Reasoning #Reinforcement Learning #AI Research #arXiv #Temporal Modeling #Generalization
📌 Key Takeaways
- Researchers introduced KairosVL model for enhanced time series analysis
- Proposed Semantic-Conditional Time Series Reasoning task incorporating semantics
- Two-round reinforcement learning framework improves temporal and semantic reasoning
- Achieves competitive performance with better generalization to unseen scenarios
📖 Full Retelling
Researchers led by Haotian Si and 11 collaborators introduced KairosVL, a novel artificial intelligence model for time series analysis, on the arXiv preprint server on February 24, 2026, aiming to enhance conventional time series analysis by incorporating contextual and semantic understanding beyond purely numerical modeling. The research team proposed the Semantic-Conditional Time Series Reasoning task, which extends traditional time series analysis by incorporating contextual and semantic understanding, addressing the increasingly complex and decision-oriented demands of modern data analysis. To strengthen the model's reasoning capabilities, they developed a sophisticated two-round reinforcement learning framework where the first round focuses on strengthening the model's perception of fundamental temporal primitives, while the second round concentrates on semantic-conditioned reasoning. Extensive experiments and ablation studies demonstrated that KairosVL achieves competitive performance across both synthetic and real-world tasks while preserving intrinsic reasoning ability and significantly improving generalization to unseen scenarios. The researchers highlight that their work provides a practical framework for real-world time series intelligence, which is in urgent demand across various industries and applications.
🏷️ Themes
Artificial Intelligence, Time Series Analysis, Semantic Reasoning
📚 Related People & Topics
Reinforcement learning
Field of machine learning
In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...
Time series
Sequence of data points over time
In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time. Thus it is a sequence of discrete-time data.
Entity Intersection Graph
Connections for Reinforcement learning:
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Large language model
8 shared
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Artificial intelligence
6 shared
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Machine learning
4 shared
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Science Publishing Group
2 shared
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Reasoning model
2 shared
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.20494 [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 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20494 [cs.AI] (or arXiv:2602.20494v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.20494 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Haotian Si [ view email ] [v1] Tue, 24 Feb 2026 02:50:38 UTC (3,004 KB) Full-text links: Access Paper: View a PDF of the paper titled Kairo...
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