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

Sanhorn Chen, Xiaoyang Chen, Boyu Liu, Roy Zhao

arXiv:2606.15107v1cs.AI

TL;DR

Existing TSQA benchmarks mostly assume regular sampling, leaving LLM and agent performance on irregular time series under-evaluated. IRTS-ToolBench provides a benchmark of irregular univariate TSQA with standardized accuracy and tool-use evaluation. Its results indicate non-trivial current-model reasoning, alongside difficulty with higher-level temporal reasoning and reliable agentic tool use.

  • Problem

    Existing TSQA benchmarks mostly use regularly sampled inputs, leaving a fundamental gap in evaluating LLMs and AI agents under irregular conditions.

    Source: §1 · p. 1

  • Method

    IRTS-ToolBench combines semantically grounded irregular-series construction, 1,700 questions across 10 task types and 13 domains, and a 30-tool library with standardized evaluation.

    Source: §1 · p. 1, §4 · p. 3, §6 · p. 4

  • Results

    Current LLMs show non-trivial reasoning over irregular time series, especially with contextual information and tools, but struggle with higher-level temporal reasoning and need strong scaffolding for reliable agentic tool use.

    Source: §6 · p. 5

  • Takeaways & Limitations

    The benchmark provides standardized evaluation of answer correctness and tool-grounded reasoning behavior on irregular univariate time-series questions.

    Source: §6 · p. 4

    The LLM-based generation pipeline may be sensitive to prompting, consensus quality, and transformation choices.

    Source: §6 · p. 5

Abstract

from arXiv · show

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.

1 INTRODUCTION

Existing TSQA benchmarks and agentic frameworks evaluate reasoning on regular time series, although real-world sampling is often irregular. IRTS-ToolBench addresses this evaluation gap with semantically grounded irregularization, broad task coverage, and tools for evaluating LLMs and agents.

1 INTRODUCTION

  • Existing TSQA benchmarks and agentic frameworks assume regular sampling, despite real-world examples such as clinically triggered ICU measurements and anomaly-triggered industrial sensor readings.The paper frames the absence of a rigorous irregular-time-series benchmark as a gap in evaluation infrastructure.Source: §1 · p. 1
  • Random dropout and sparse-mask resampling produce statistically irregular data without domain semantics, while real-world irregularity spans nine cause-driven types across three categories.The authors report no prior semantically grounded regular-to-irregular transformation pipeline to their knowledge.Source: §1 · p. 1
  • IRTS-ToolBench provides 1,700 questions across 10 task types and 13 domains, plus a 30-tool library for evaluating LLMs and AI agents on irregular univariate TSQA.The library includes 7 irregularity operators and 23 analytical tools.Source: §1 · p. 1

2 RELATED WORK

Prior TSQA benchmarks, irregularity methods, and agentic time-series frameworks provide foundations for IRTS-ToolBench, but the cited benchmarks assume regular inputs and prior transformation work does not use LLM-guided irregularization.

TSQA Benchmarks

  • TSAQA, Time-MQA, and ITFormer contribute task taxonomies, contextualized multi-task QA, and cross-modal alignment, respectively, but all assume clean, regularly sampled inputs.TSAQA's multi-LLM consensus question generation inspired the benchmark's question-generation pipeline.Source: §2 · p. 2

Irregular Time Series: Taxonomy and Transformation Methods

  • TIME-IMM supplies a nine-type irregularity taxonomy used as the pipeline's decision space, while Du et al. provide missingness-function implementations for its execution layer.The cited related work does not use LLMs to guide irregularization.Source: §2 · p. 2

Agentic Time Series Frameworks

  • TimeART establishes a tool-augmented agentic evaluation template that IRTS-ToolBench extends to irregular time series; TS-Agent and TimeSeriesScientist address other time-series agent workflows.TS-Agent focuses on statistical insight gathering, while TimeSeriesScientist uses role-based agents for forecasting workflows.Source: §2 · p. 2

3 BENCHMARK DESIGN

IRTS-ToolBench combines ten tasks across three reasoning categories with a multi-stage construction pipeline and a two-layer tool library, creating benchmark samples and tools for irregular univariate TSQA.

3.1 TASK DOMAIN, SCOPE, AND DESCRIPTIONS

  • The benchmark covers 10 tasks in standard, irregularity-specific, and regularity–irregularity interface reasoning, forming a progression from basic irregular sampling to cross-representation reasoning.It draws from two source datasets spanning 13 domains; samples are univariate and use multiple-choice or true/false formats.Source: §3.1 · p. 2

3.2 BENCHMARK CONSTRUCTION PIPELINE

  • The irregularization pipeline enriches sample context, selects a taxonomy-based irregularity type and transformation plan, then generates validated numerical parameters for execution.Execution produces irregular series with realistic missingness patterns and may jitter timestamps.Source: §3.2 · p. 3
  • Task-specific question generation uses GPT-5.1 to generate or construct questions and answers, then three independent LLMs assess clarity and answerability.The questions are conditioned on transformed series and their anomaly annotations.Source: §3.2 · p. 3
  • For each finalized sample, three LLMs propose required tool sequences; majority voting selects tools, with a union fallback when no tool receives majority support.The resulting golden tool set specifies the minimum tools required for a correct answer.Source: §3.2 · p. 3

3.2.1 TOOL LIBRARY

  • The 30-tool library supports evaluation of LLMs and agents through irregularity operators and 23 advanced analytical tools, including summary statistics and trend and seasonality detection.The library is organized into irregularity operators and advanced analytical tools.Source: §3.2.1 · p. 3

4 EVALUATION PROTOCOL

The protocol scores model answers against ground truth and reports overall and per-task accuracy, with tool-match rates reported separately when tool evaluation is enabled.

4 EVALUATION PROTOCOL

  • The protocol parses model outputs against ground truth in multiple-choice or true/false format and defines three primary metrics for standardized reporting.The listed reporting requirements include overall accuracy, task-level accuracy across ten tasks, and tool-match rates when applicable.Source: §4 · pp. 3–4
  • Overall accuracy uses all evaluated examples, while task-level accuracy is computed over examples belonging to each task.For both multiple-choice and true/false questions, an output must exactly match the ground-truth answer to receive credit.Source: §4 · p. 3
  • When tool evaluation is enabled, each task reports exact-match, partial-match, and complete-mismatch rates by comparing called tools with the proposed golden tool set.The three rates are mutually exclusive and sum to 1 within each task; they are not used to compute answer accuracy.Source: §4 · pp. 3–4
  • Answers receive binary credit for exact ground-truth matches; overall accuracy averages scores across samples, while task-level accuracy averages within each task.Tool-match rates are aggregated separately by task type as indicators of tool-use behavior.Source: §4 · p. 4

5 BENCHMARK VALIDATION

The validation combines automated review of generated questions and tool sets, human review, and zero-shot model evaluation. Results show strong overall model performance in some settings, alongside persistent difficulty on higher-level temporal tasks.

Task Quality Verification

  • GPT-5.1, Claude Sonnet 4.5, and Gemini 2.5 Flash evaluate generated questions and propose tools, with majority support used to construct the golden tool set.Most questions are retained when all evaluators meet a threshold; Anomaly Detection requires scores of at least 0.85.Source: §5 · p. 4

Human Peer Review and Human Baseline

  • 80% and 78%: the two human reviewers’ overall accuracies; Regularity Recovery and Missingness Reasoning have higher uncertainty and lower agreement than other tasks.Reviewers independently assessed a stratified random 2% sample for clarity and ambiguity.Source: §5 · p. 4

Evaluation Results

  • 78.59: Qwen3.6-27B outperforms the other three models in all settings, while Claude-Opus-4.7 achieves 74–77% overall accuracy.Tool calling improves several numerical-analysis tasks, but temporal relationship reasoning and regular-vs-irregular discrimination remain less consistent across models.Source: §5 · p. 4

6 CONCLUSION

IRTS-ToolBench is a 1,700-question benchmark for irregular univariate time-series QA, spanning 10 task types and 13 domains. The authors report that tools and contextual information accompany non-trivial LLM reasoning, while higher-level temporal reasoning remains difficult and reliable agentic tool use requires strong scaffolding.

6 CONCLUSION

  • IRTS-ToolBench provides 1,700 questions across 10 task types and 13 domains for evaluating irregular univariate time-series QA.It combines semantically grounded construction, task-level diagnostics, and a 30-tool library with golden tool sets for evaluating answer correctness and tool-grounded reasoning.Source: §6 · p. 4
  • Current LLMs show non-trivial irregular-time-series reasoning, especially with context and tools, but still struggle with higher-level temporal reasoning and need strong scaffolding for reliable agentic tool use.The generation pipeline may inherit sensitivity to prompting, consensus quality, and transformation choices; future work proposes broader validation and more naturally irregular source data.Source: §6 · p. 5

A TASK DESIGN

Table 1 summarizes tasks and identifies TF as true or false and MC as multiple choices.

A TASK DESIGN

  • Table 1 summarizes tasks; TF means true or false, and MC means multiple choices.Source: Table 1 · p. 7

A.1 TASK DESCRIPTIONS

The task descriptions organize irregular time-series QA into ten tasks across standard reasoning, irregularity-specific reasoning, and regularity–irregularity interface reasoning. They specify each task’s input and target, with challenges including sparse observations, missingness patterns, and non-uniform spacing.

A.1.1 STANDARD REASONING

  • Anomaly Detection asks whether a specified anomaly exists, where it occurs, or how many occur, requiring timestamp-aware reasoning over sparse observations.Source: §A.1.1 · p. 7
  • Classification asks models to select the series’ domain, adapting reasoning to non-uniform spacing or using resampling tools.Source: §A.1.1 · p. 7
  • Regular vs. Irregular Discrimination asks models to identify sampling regularity, including near-regular sequences with mild jitter that require interval-statistic checks.Source: §A.1.1 · p. 8
  • Regularity Recovery asks models to choose a plausible regular reconstruction, matching the original grid, interpolation or imputation strategy, and signal.Source: §A.1.1 · p. 8

A.2 IRREGULARITY-SPECIFIC REASONING

  • Characterization asks models to assess trend, seasonality, and stationarity, using interpolation or tools designed for irregular sequences.Source: §A.2 · p. 8
  • Irregularity Cause Attribution asks models to identify which of nine categories best explains missingness by combining domain knowledge with structural pattern analysis.Source: §A.2 · p. 8
  • Temporal Relationship asks models to choose the correct next chronological patch from candidates drawn from other sequences as distractors.Source: §A.2 · p. 8

A.2.1 REGULARITY–IRREGULARITY INTERFACE REASONING

  • Missingness Reasoning asks whether a particular gap is informative, with ground truth derived from irregularity type and transformation domain context.Source: §A.2.1 · p. 8

B BENCHMARK CONSTRUCTION PIPELINE

IRTS-ToolBench is built from source datasets through a three-layer process that transforms regular series, generates questions, and constructs golden tool sets. Its transformation pipeline uses contextual information to select and parameterize irregularity mechanisms.

B BENCHMARK CONSTRUCTION PIPELINE

B.1 IRREGULAR TIME SERIES TRANSFORMATION

  • The transformation pipeline enriches each series’ context, selects a taxonomy-based irregularity type and plan, then generates validated numerical parameters for execution.Execution applies the parameters to produce irregular series with realistic missingness and optionally jittered timestamps.Source: §B.1 · p. 9

B.2 QUESTION GENERATION PIPELINE

Questions are generated for each task type from transformed irregular series and anomaly annotations, then checked for clarity and answerability. A separate consensus protocol defines the minimum tools required to answer each finalized sample.

B.2 QUESTION GENERATION PIPELINE

  • GPT-5.1 generates task-specific metadata and either questions and answers or deterministic task outputs; three LLMs then assess clarity and answerability.The reviewers are GPT-5.1, Claude Sonnet 4.5, and Gemini 2.5 Flash.Source: §B.2 · p. 9
  • Questions are conditioned on transformed irregular series and their anomaly annotations, with generation tailored to each task type.Source: §B.2 · p. 9

B.3 GOLDEN TOOL SET CONSTRUCTION

  • Three LLMs independently propose required tool sequences, and majority voting retains tools proposed by at least two; a union fallback applies when none wins majority support.The resulting golden tool set specifies the minimum tools needed to answer each finalized sample.Source: §B.3 · p. 10

C EVALUATION RESULTS

Across IRTS-ToolBench, Qwen3.6-27B leads overall, while tool-calling gains vary by model and task, with larger gaps on deeper irregularity reasoning. The benchmark’s tool library supports both tool-augmented LLMs and agentic tool-use, and examples span three reasoning levels.

C EVALUATION RESULTS

  • Qwen3.6-27B reaches 99.60 from 96.80 on anomaly detection and 100.00 on classification; DeepSeek-V4-Flash rises 31.33 to 98.67 on ISE and 64.67 to 89.33 on RR with tools.Deeper irregular-pattern tasks show larger performance gaps, and Claude-Opus-4.7 generally performs strongly on cause attribution and missingness reasoning.Source: C EVALUATION RESULTS · p. 11
  • Qwen3.6-27B scores 78.59 and outperforms the other three models in all settings; Claude-Opus-4.7 records 74–77 percent overall accuracy.Source: Table 2 · p. 11, C EVALUATION RESULTS · p. 11

D TOOL LIBRARY

  • The 30-tool library has irregularity operators and 23 advanced analytical tools, supporting evaluation through tool-augmented prompting or agentic tool-use frameworks.Analytical tools include summary statistics and trend and seasonality detection.Source: D TOOL LIBRARY · p. 11

E EXAMPLES

  • Three examples span regular-vs-irregular discrimination, irregularity cause attribution, and missingness reasoning, using true/false or multiple-choice questions.The examples ask whether a series is irregular, which collection mechanism best explains an irregularity, or why an observation is missing.Source: E EXAMPLES · pp. 12–15
  • The cause-attribution and missingness examples use wearable-sensor streams; one marks a single absent sample, while another lists duty cycling, packet loss, buffering, off-body periods, or app pauses.Source: E EXAMPLES · pp. 14–15

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