CAN-QA creates 33,128 QA pairs from CAN traffic logs in 10 categories to test LLMs, which capture patterns but struggle with temporal reasoning and multi-condition inference.
Time-mqa: Time series multi-task question answering with context enhancement
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TimeSeriesExamAgent combines templates and LLM agents to generate scalable time series reasoning benchmarks, demonstrating that current LLMs have limited performance on both abstract and domain-specific tasks.
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
citing papers explorer
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CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
CAN-QA creates 33,128 QA pairs from CAN traffic logs in 10 categories to test LLMs, which capture patterns but struggle with temporal reasoning and multi-condition inference.
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TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
TimeSeriesExamAgent combines templates and LLM agents to generate scalable time series reasoning benchmarks, demonstrating that current LLMs have limited performance on both abstract and domain-specific tasks.
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TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
TS-Agent is an agentic framework that uses LLMs only for evidence-based reasoning while delegating extraction to raw time series tools, matching or exceeding baselines on four benchmarks with largest gains on reasoning tasks.
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Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback
Time-RA reformulates time series anomaly detection as a reasoning-intensive generative task and provides the RATs40K multimodal benchmark to evaluate and improve LLM-based diagnosis.
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From Time Series Analysis to Question Answering: A Survey in the LLM Era
A survey proposing a taxonomy of Injective, Bridging, and Internal Alignment paradigms to evolve TSA into user-driven Time Series Question Answering with LLMs.
- TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis