AIDABench: AI Data Analytics Benchmark

2026-01-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, extended

Summary

AIDABench is a new, comprehensive benchmark designed to evaluate AI systems on complex, end-to-end data analytics tasks involving diverse document types like spreadsheets, databases, and financial reports. It features over 600 analytical tasks across three core dimensions: question answering (QA), data visualization, and file generation. The benchmark's tasks are challenging, with medium and hard tasks accounting for over 70% of the dataset, often requiring 13 or more reasoning steps. An evaluation of 11 state-of-the-art proprietary and open-source models, including claude-sonnet-4-5 and gemini-3-pro-preview, revealed that the best-performing model achieved only 59.43 pass@1, indicating significant challenges for current AI systems in real-world data analytics. AIDABench provides a principled reference for enterprise procurement, tool selection, and model optimization, and is publicly available on GitHub.

Key takeaway

For Machine Learning Engineers and AI Scientists evaluating or developing AI-driven document analysis agents, AIDABench provides a critical, realistic benchmark. Your focus should be on improving model performance, especially in file generation and handling semantic ambiguities, as these areas show significant headroom for improvement. Consider integrating robust error handling for numerical calculations and non-English text rendering in visualizations, as these are common failure modes.

Key insights

AIDABench offers a rigorous, end-to-end evaluation for AI in complex data analytics, revealing current model limitations.

Principles

• End-to-end evaluation is critical for real-world AI performance.
• Heterogeneous data types and multi-step reasoning define real-world complexity.
• Model capacity correlates with performance on complex tasks.

Method

AIDABench uses a "plan–execute–verify" loop with a tool-call interface for Python code execution in a stateless sandbox. Dedicated evaluators for QA, visualization, and file generation assess accuracy and readability.

In practice

• Use AIDABench for robust AI model selection.
• Prioritize models strong in File Generation for complex workflows.
• Provide auxiliary spreadsheet summaries to improve model performance.

Topics

• AIDABench
• AI Data Analytics
• Document Understanding
• LLM Evaluation
• File Generation

Code references

• MichaelYang-lyx/AIDABench

Best for: AI Scientist, Research Scientist, Machine Learning Engineer, AI Engineer, Data Scientist, AI Researcher

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.