KairosVL: Orchestrating Time Series and Semantics for Unified Reasoning

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. 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.