TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.
arXiv preprint arXiv:2509.24378 , year=
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cs.AI 2years
2026 2verdicts
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The authors create VisAnomBench with VLM-generated anomaly explanations and fine-tune VisAnomReasoner, reporting precision and F1 gains of at least 21 and 23 points on the new benchmark plus cross-benchmark improvements.
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TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
TSQAgent uses three collaborative LLM agents with analytical tools to identify relevant quality dimensions and enable quantitative comparisons for time series data, improving on standard LLM methods and leading to better downstream data selection.
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Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
The authors create VisAnomBench with VLM-generated anomaly explanations and fine-tune VisAnomReasoner, reporting precision and F1 gains of at least 21 and 23 points on the new benchmark plus cross-benchmark improvements.