Time-series Diagnostic Reasoning with Knowledge Injection

Time‐series diagnostic reasoning is essential for many applications, yet existing solutions face a persistent gap: general reasoning large language models (GRLMs) possess strong reasoning skills but lack the domain-specific knowledge to understand complex time-series patterns. Conversely, fine-tuned time-series large language models (TSLMs) understand these patterns but lack the capacity to generalize reasoning for more complicated questions. To bridge this gap, we propose a hybrid knowledge-injection framework that injects TSLM-generated insights directly into GRLM's reasoning trace, thereby achieving strong time-series reasoning with in-domain knowledge. As collecting data for knowledge injection fine-tuning is costly, we further leverage a reinforcement learning-based approach with verifiable rewards (RLVR) to elicit knowledge-rich traces without human supervision, then transfer such an in-domain thinking trace into GRLM for efficient knowledge injection. We further release SensorTSR, a multivariate time-series-based diagnostic reasoning benchmark collected from real-world industrial operations. Across SensorTSR and other public datasets, our method consistently surpasses TSLMs by 9.1\%–26.1\% and GRLMs by 7.9\%–22.4\%, delivering robust, context-aware time-series diagnostic insights.