KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning
Haotian Si, Changhua Pei, Xiao He, Zeyan Li, Zhe Xie, Zexin Wang, Jiyao Hu, Zhaoyang Yu, Tieying Zhang, Dan Pei, Jianhui Li, Gaogang Xie

TL;DR
KairosVL introduces a novel framework that combines semantic reasoning with temporal modeling in time series analysis, enhancing understanding and generalization in complex, decision-oriented tasks.
Contribution
The paper presents a two-round reinforcement learning framework for semantic-conditioned time series reasoning, advancing beyond traditional numerical models.
Findings
Achieves competitive performance on synthetic and real-world tasks.
Enhances reasoning ability and generalization to unseen scenarios.
Demonstrates the effectiveness of semantic integration in time series analysis.
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.…
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Taxonomy
TopicsTime Series Analysis and Forecasting · Machine Learning in Healthcare · Neural Networks and Reservoir Computing
