SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning

TL;DR

This work proposes 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.

Abstract

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.

Figures (8)

• Figure 1: (a) The newly released SenTSR-Bench benchmark, collected from real-world machine monitoring environments, with multi-stage diagnostic questions. (b) Performance of the proposed framework on SenTSR-Bench, surpassing both stand-alone time-series specialists (TSLM) and general reasoning models (GRLM). (c) Case study illustrating why knowledge injection helps: the specialist captures key time-series patterns but fails to connect them to the correct root cause; the general reasoner shows strong reasoning but overlooks domain-specific critical failure patterns; our method injects the in-domain knowledge from fine-tuned specialist into the reasoner’s reasoning trace, aligning the trace with domain knowledge and producing the correct diagnosis.
• Figure 2: Overview of the proposed paradigm. (a) Knowledge injection: given a reasoning question and its time-series, a time–series LM (TSLM) produces grounded analysis snippets that are injected into the reasoning trace of a general frozen reasoning LM (GRLM) to answer diagnostic queries without weight updates. (b) Thinking transfer via RL: We train the TSLM using reinforcement learning with verifiable rewards (RLVR) with an explicit thinking structure to elicit analysis-first thinking traces without human supervision; at inference, these traces are transferred via injection into the reasoning LM to strengthen temporal grounding for diagnosis.
• Figure 3: SenTSR-Bench Construction pipeline.
• Figure 4: Comparison of baseline (zero-shot) reasoning, knowledge prompting, and knowledge injection. (a) SenTSR-Bench Benchmark with Qwen-VL-3B (RL) as the TSLM. (b) TSEvol and TS&Language Benchmarks with Qwen-VL-3B (RL) as the TSLM. (c) TSEvol and TS&Language Benchmarks with ChatTS-14B as the TSLM. Across all settings, the injection-based method consistently outperforms others.
• Figure 5: (a) Performance comparison between (i) the standalone TSLM, (ii) knowledge injection where the GRLM receives only the TSLM textual summary (Injection w/o TS), and (iii) full knowledge injection where the GRLM receives both the raw time series and the injected summary (Injection w/ TS). (b) Comparison of overall diagnostic accuracy versus inference latency for different methods.