Aidabench Eval

aidabench-eval

AIDABench: AI Data Analytics Benchmark — Yang et al. (2026) (arXiv:2603.15636, 2026)

What this evaluates

Evaluates LLM-driven document analysis agents on end-to-end data analytics workflows, including question answering, data visualization, and file generation. It probes multi-step numerical reasoning, cross-data consistency, and long-horizon planning on heterogeneous real-world documents.

Datasets

• AIDABench — total 600; splits: Easy (-1), Medium (-1), Hard (-1); repo https://github.com/MichaelYang-lyx/AIDABench

Metrics

• Pass@3 (primary) — range: percent
  • Pass@k measures the probability that at least one of k generated attempts is correct. For Pass@3, the model is allowed up to 3 attempts per task, and the metric reports the percentage of tasks where at least one attempt passes the evaluation criteria.

Input / output format

Input: Heterogeneous real-world documents (spreadsheets, databases, financial reports) with associated analytical questions or tasks requiring multi-step reasoning.

Output: Model-generated outputs including answers, data visualizations, or generated files, evaluated for correctness against expert-verified ground truth.

Scoring recipe

def pass_at_k(predictions, k=3):
    correct = [bool(p) for p in predictions]
    return 1.0 if any(correct) else 0.0

score = (sum(pass_at_k(task_preds) for task_preds in all_tasks) / total_tasks) * 100

Common pitfalls

• Difficulty is defined strictly by operation-chain length (number of key expert steps), not by task type or domain.
• Pass@3 allows up to 3 attempts per task; reporting Pass@1 will significantly underestimate model capability.
• Tasks span three distinct dimensions (QA, visualization, file generation) but are aggregated in the main table, masking dimension-specific performance.

Evidence (verbatim from paper)

We stratify task difficulty by operation-chain length, defined as the number of key expert operations (Steps). Low complexity ( ≤ 6 ) accounts for 29.5% of tasks, medium (7-12) for 49.4%, and high ( ≥ 13 ) for 21.1% . Table 4 reports pass@3 performance on each split.

Citation

@misc{yang2026aidabench,
  title={AIDABench: AI Data Analytics Benchmark},
  author={Yang et al. (2026)},
  year={2026},
  note={arXiv:2603.15636}
}

• arXiv: 2603.15636