KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning

2026-02-24

AI Summary

This work proposes a novel task termed semantic-conditioned time series reasoning to address the limitations of traditional time series analysis methods, which often lack contextual semantic understanding and thus struggle to support complex decision-making. To tackle this challenge, the authors introduce the first unified framework that jointly integrates semantic comprehension with time series modeling. The approach employs a two-stage reinforcement learning strategy: first enhancing the model’s capacity to capture temporal structures through a temporal primitive perception mechanism, and then performing reasoning under semantic guidance. Experimental results demonstrate that the proposed framework significantly improves both generalization performance and reasoning stability across synthetic and real-world tasks, thereby validating the effectiveness of deeply integrating semantic and temporal information.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Time-Series/Data Streams
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• time series reasoning
• semantic understanding
• contextual reasoning
• decision-oriented analysis
• temporal modeling

Innovation

• Semantic-Conditional Reasoning
• Time Series Analysis
• Reinforcement Learning Framework
• Temporal Primitives
• Multimodal Reasoning