SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
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 LLMs (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 SenTSR-Bench, a multivariate time-series-based diagnostic reasoning benchmark collected from real-world industrial operations. Across SenTSR-Bench 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.
699d21974e37ec6dfa1bc612
Zelin He
699d21974e37ec6dfa1bc613
Boran Han
699d21974e37ec6dfa1bc614
Xiyuan Zhang
699d21974e37ec6dfa1bc615
Shuai Zhang
699d21974e37ec6dfa1bc616
Haotian Lin
699d21974e37ec6dfa1bc617
Qi Zhu
699d21974e37ec6dfa1bc618
Haoyang Fang
699d21974e37ec6dfa1bc619
Danielle C. Maddix
699d21974e37ec6dfa1bc61a
Abdul Fatir Ansari
699d21974e37ec6dfa1bc61b
Akash Chandrayan
699d21974e37ec6dfa1bc61c
Abhinav Pradhan
699d21974e37ec6dfa1bc61d
Bernie Wang
699d21974e37ec6dfa1bc61e
Matthew Reimherr
SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
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 LLMs (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 SenTSR-Bench, a multivariate time-series-based diagnostic reasoning benchmark collected from real-world industrial operations. Across SenTSR-Bench 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.
A hybrid knowledge-injection framework combines general reasoning large language models with time-series LLMs through reinforcement learning-based verifiable rewards to enhance time-series diagnostic reasoning performance.
large language models time-series LLMs knowledge injection reinforcement learning verifiable rewards diagnostic reasoning multivariate time-series benchmark