Enterprise AI Analysis: SenTSR-Bench: Thinking with Injected Knowledge for Time-Series Reasoning
Unlocking Advanced Time-Series AI: Knowledge Injection for Superior Diagnostic Reasoning
Our new hybrid knowledge-injection framework, SenTSR-Bench, combines the reasoning power of General Reasoning Large Language Models (GRLMs) with the domain-specific insights of Time-Series Language Models (TSLMs). By injecting TSLM-generated insights directly into GRLM's reasoning trace, we achieve robust and context-aware time-series diagnostics, outperforming standalone models by up to 26.1%.
Quantifiable Impact: Enhancing Predictive Maintenance & Operational Efficiency
SenTSR-Bench delivers significant improvements in diagnostic accuracy and operational insight across real-world industrial scenarios. Our approach reduces diagnostic errors and accelerates problem resolution.
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Accuracy Boost over TSLMs
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Accuracy Boost over GRLMs
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RL Injection Gain vs. SFT
Deep Analysis & Enterprise Applications
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Methodology Overview
SenTSR-Bench Benchmark
Performance Analysis
Our framework integrates TSLM-generated insights into GRLM reasoning traces, balancing domain expertise with generalizable critical thinking. This hybrid approach uses a reinforcement learning-based method with verifiable rewards (RLVR) to generate knowledge-rich traces without manual supervision, which are then injected into GRLMs for superior time-series diagnostic reasoning. This section describes the full process.
We introduce SenTSR-Bench, a new real-world, multivariate time-series diagnostic reasoning benchmark. Unlike synthetic or LLM-annotated datasets, SenTSR-Bench is built from industrial operations with human-annotated data, featuring multi-stage diagnostic questions (What happened, How happened, Suggested fix) to assess comprehensive reasoning capabilities. This section details its construction and importance.
Our method consistently surpasses TSLMs by 9.1%–26.1% and GRLMs by 7.9%–22.4% across SenTSR-Bench and other public datasets. RL-enhanced injection provides 1.66x–2.92x larger gains than SFT-enhanced injection, demonstrating robust, context-aware diagnostic insights. This section presents a detailed breakdown of results and comparisons.
+26.1%
Max Accuracy Boost Over TSLMs
Enterprise Process Flow
TSLM Generates Knowledge Snippet
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RLVR Elicits Knowledge-Rich Traces
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Inference: Traces Injected into GRLM
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GRLM Reasons with In-Domain Guidance
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Correct Diagnostic Insights
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Real-world Scenario: Preventing Equipment Failure
Consider a sensor monitoring system detecting anomalies in machine temperature and vibration. Our framework provides a superior diagnosis:
TSLM's Role: Captures key time-series patterns (e.g., rising vibration, stable temperature) but may misinterpret joint increases.
GRLM's Role: Applies strong general reasoning but might overlook critical domain-specific failure patterns (e.g., assuming late temperature rise).
Our Method (Knowledge Injection): TSLM's accurate signal-level observations are injected into the GRLM's reasoning trace. The GRLM corrects initial reasoning flaws, integrating domain knowledge for a precise diagnosis, like 'abrupt friction surge on rolling path'.
Calculate Your Potential ROI with Enterprise AI
See how much time and cost your organization could save by implementing our advanced AI solutions for diagnostic reasoning.
Annual Savings
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Hours Reclaimed Annually
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Your Journey to AI-Powered Diagnostics
Our structured implementation plan ensures a seamless transition and maximum value realization from your new AI capabilities.
Phase 1: Discovery & Strategy
Comprehensive assessment of your current diagnostic workflows, data infrastructure, and business objectives to tailor a bespoke AI strategy.
Phase 2: Pilot & Integration
Develop and integrate a pilot SenTSR-Bench solution into a specific use case, demonstrating immediate value and refining the model based on real-world feedback.
Phase 3: Scaling & Optimization
Expand the AI solution across your enterprise, continuously monitoring performance, fine-tuning models, and exploring new applications for sustained impact.
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