DataClaw Benchmark Tests AI Agents on Real-World Data Analysis
DataClaw has been unveiled by researchers as a benchmark focused on assessing autonomous data analysis agents in real-world exploratory contexts. In contrast to traditional benchmarks that prioritize the accuracy of final answers in guided data settings, DataClaw centers on evaluating reasoning processes. It encompasses around 2.06 million authentic records spanning enterprise, industry, and policy sectors, while retaining inherent data noise. The benchmark features 492 tasks across various domains, inspired by think-tank consulting situations, with annotations marking intermediate milestones for evaluating processes. These annotations track an agent's progress and identify reasoning failures. Tests involving eight sophisticated LLMs revealed that current agents are not yet dependable, with seven models scoring below 50% success. This benchmark seeks to fill the void in assessing agents' capabilities for exploratory analysis in less-studied data environments.
Key facts
- DataClaw is a process-oriented benchmark for exploratory real-world data analysis.
- It contains approximately 2.06 million real-world records.
- Data covers enterprise, industry, and policy domains.
- Native data noise is preserved.
- Includes 492 cross-domain tasks from think-tank consulting scenarios.
- Each task is annotated with intermediate milestones for process-level evaluation.
- Experiments with eight advanced LLMs showed seven models below 50% success.
- Current agents remain far from reliable in this setting.
Entities
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