The Challenge of Irregular Time-Series Question Answering

Standard LLM-based data analysis often fails when tasked with irregular Time-Series Question Answering (TSQA). Unlike structured tabular data, irregular time series contain non-uniform intervals, missing values, and complex temporal dependencies that require more than simple pattern matching. The authors argue that current agentic approaches rely too heavily on the model's internal reasoning, which is prone to hallucination and logical errors when performing multi-step mathematical or statistical operations.

Tool-Grounded Reasoning as a Verification Framework

To address these limitations, the paper introduces a framework for "verifiable agentic data science." The core insight is to decouple the agent's high-level planning from the low-level execution of data operations. By grounding the agent's reasoning in a set of specialized, verifiable tools, the system ensures that every step of the data processing pipeline—from data cleaning and interpolation to statistical aggregation—is traceable and mathematically sound.

Instead of asking an LLM to "calculate the trend," the agent is forced to decompose the request into a series of explicit tool calls (e.g., resample_data, compute_moving_average, perform_regression). Each tool output serves as a verifiable checkpoint. If a step fails or produces an illogical result, the agent can backtrack or adjust its strategy, effectively creating a self-correcting loop that significantly reduces the error rate compared to monolithic generation.

Improving Reliability in Agentic Pipelines

This approach shifts the burden of accuracy from the model's weights to the tool-use protocol. By enforcing a strict schema for tool inputs and outputs, the framework allows for:

• Auditability: Every transformation applied to the time-series data is logged and reproducible.
• Error Isolation: Failures in data processing are localized to specific tool executions, making it easier to debug complex queries.
• Constraint Satisfaction: The agent operates within a defined sandbox of statistical operations, preventing the model from inventing non-existent data points or applying inappropriate analytical methods to irregular temporal data.