DataClaw: A Process-Oriented Agent Benchmark for Exploratory Real-World Data Analysis
📰 ArXiv cs.AI
Learn how to evaluate autonomous data analysis agents with DataClaw, a process-oriented benchmark for exploratory real-world data analysis
Action Steps
- Build a dataset using real-world data to test the agent's exploratory analysis capabilities
- Configure the DataClaw benchmark to evaluate the agent's performance
- Run the benchmark to assess the agent's ability to perform exploratory analysis
- Test the agent's reasoning process using the evaluation metrics provided by DataClaw
- Apply the insights gained from the benchmark to improve the agent's performance
Who Needs to Know This
Data scientists and machine learning engineers can use DataClaw to test and improve the performance of their autonomous data analysis agents, while researchers can utilize it to evaluate the reasoning process of these agents
Key Insight
💡 DataClaw provides a comprehensive evaluation of autonomous data analysis agents, focusing on their ability to perform exploratory analysis in underexplored data environments
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📊 Evaluate autonomous data analysis agents with DataClaw, a process-oriented benchmark for exploratory real-world data analysis 🚀
Key Takeaways
Learn how to evaluate autonomous data analysis agents with DataClaw, a process-oriented benchmark for exploratory real-world data analysis
Full Article
Title: DataClaw: A Process-Oriented Agent Benchmark for Exploratory Real-World Data Analysis
Abstract:
arXiv:2605.02503v1 Announce Type: new Abstract: Evaluating autonomous data analysis agents requires testing their ability to perform exploratory analysis in underexplored data environments. However, many existing benchmarks emphasize final answer accuracy in prior-guided data settings and provide limited support for reasoning process evaluation. We introduce DataClaw, a process-oriented benchmark for exploratory real-world data analysis. DataClaw contains approximately 2.06 million real-world re
Abstract:
arXiv:2605.02503v1 Announce Type: new Abstract: Evaluating autonomous data analysis agents requires testing their ability to perform exploratory analysis in underexplored data environments. However, many existing benchmarks emphasize final answer accuracy in prior-guided data settings and provide limited support for reasoning process evaluation. We introduce DataClaw, a process-oriented benchmark for exploratory real-world data analysis. DataClaw contains approximately 2.06 million real-world re
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