Towards Verifiable Agentic Data Science: Solving Irregular TSQA Via Tool-Grounded Reasoning

Researchers introduced IRTS-ToolBench, a benchmark of 1,700 questions across 10 task types and 13 domains, to evaluate large language models and AI agents on irregular time series question answering. The benchmark addresses the gap in existing TSQA benchmarks that assume regularly sampled inputs, providing standardized inputs and a reproducible evaluation protocol for LLM-based irregular time series analysis.

arXiv:2606.15107v1 Announce Type: new Abstract: Time series data in real-world deployments is overwhelmingly irregular. Observations are asynchronous, missing values are informative rather than random, and sampling frequencies vary across sensors and operational windows. However, existing Time Series Question Answering TSQA benchmarks mostly assume regularly sampled inputs, leaving a fundamental gap in understanding how large language models LLMs and AI agents perform under irregular conditions. To bridge this gap, we introduce IRTS-ToolBench, a benchmark of 1,700 questions spanning 10 task types across 13 domains. IRTS-ToolBench is designed to be used independently by any researcher working on LLM-based irregular time series analysis, providing standardized inputs and a reproducible evaluation protocol. Code can be found in https://github.com/SanhornC/IRTS-ToolBench.