Dataset Card for AIDABench

Links

• Paper (arXiv)
• GitHub Repository

Dataset Summary

AIDABench is a benchmark for evaluating AI systems on end-to-end data analytics over real-world documents. It contains 600+ diverse analytical tasks grounded in realistic scenarios and spans heterogeneous data sources such as spreadsheets, databases, financial reports, and operational records. Tasks are designed to be challenging, often requiring multi-step reasoning and tool use to complete reliably.

Figure 1: Overview of the AIDABench evaluation framework.

Supported Tasks and Evaluation Targets

AIDABench focuses on practical document analytics workflows where a model/agent must read files, reason over structured data, and produce a final deliverable.

Task Categories

The dataset is organized around three primary capability dimensions:

• File Generation (43.3%)
  Data wrangling and transformation tasks such as filtering, normalization, deduplication, joins, and cross-sheet linkage, with outputs as generated files (e.g., spreadsheets).

• Question Answering (QA) (37.5%)
  Analytical queries such as aggregation, averages, ranking, comparisons, and trend analysis, with outputs as final answers.

• Data Visualization (19.2%)
  Chart creation/adaptation tasks (e.g., bar/line/pie) including style requirements and presentation constraints, with outputs as figures or chart files.

Figure 2: Example evaluation scenarios for QA, Data Visualization, and File Generation.

Task Complexity

Tasks are stratified by the number of expert-level reasoning steps required:

• Easy (29.5%): ≤ 6 steps
• Medium (49.4%): 7–12 steps
• Hard (21.1%): ≥ 13 steps
• Cross-file Reasoning: 27.4% of tasks require reasoning over multiple input files (up to 14 files).

Data Formats

Most inputs are tabular files (xlsx/csv dominate), complemented by DOCX and PDF formats to support mixed-type document processing.

Evaluation Framework

All models are evaluated under a unified tool-augmented protocol: the model receives task instructions and associated files, and can execute arbitrary Python code within a sandboxed environment to complete the task.

To align with task categories, AIDABench uses three dedicated LLM-based evaluators:

1. QA Evaluator
   A binary judge that determines whether the produced answer matches the reference (under the benchmark’s scoring rules).

2. Visualization Evaluator
   Scores both correctness and readability of generated visualizations.

3. Spreadsheet File Evaluator
   Verifies generated spreadsheet outputs with a coarse-to-fine strategy, combining structural checks with sampled content validation and task-specific verification.

Figure 3: The design of the three types of evaluators in AIDABench.

Baseline Performance

Results indicate that complex, tool-augmented document analytics remains challenging: the best-performing baseline model (Claude-Sonnet-4.5) achieves 59.43 pass@1 on AIDABench (see the paper for full settings, model list, and breakdowns).

Intended Uses

AIDABench is intended for:

• Evaluating agents or tool-using LLM systems on realistic document analytics tasks
• Benchmarking end-to-end capabilities across QA, file generation, and visualization
• Diagnosing failure modes in multi-step, multi-file reasoning over business-like data

Limitations

• The benchmark is designed for tool-augmented settings; purely text-only inference may underperform due to the need for code execution and file manipulation.
• Automated evaluation relies on LLM judges, which introduces additional compute cost and (small) scoring variance depending on settings.

Citation

If you use this dataset, please cite the original paper:

@article{yang2026aidabench,
  title={AIDABENCH: AI DATA ANALYTICS BENCHMARK},
  author={Yang, Yibo and Lei, Fei and Sun, Yixuan and others},
  journal={arXiv preprint},
  year={2026}
}

---