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MMAU: Massive Multitask Agent Understanding
Holistic benchmark evaluating agents across five domains with 20 tasks and 3K+ prompts. The example preserves its published-era model roster.
In 30 seconds
- What
- Benchmark testing agents across five domains with 3000+ prompts measuring understanding, reasoning, planning, problem-solving, and self-correction.
- When to use
- Comparing agent performance across diverse problem types or tracking whether model improvements translate to real capability gains across domains.
- Watch out
- High setup and compute cost; results reflect benchmark design choices, not necessarily real-world performance on your specific tasks.
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MMAU: Massive Multitask Agent Understanding: Overview
Holistic benchmark evaluating agents across five domains with 20 tasks and 3K+ prompts. The example preserves its published-era model roster.
- Five core domains: Tool-use, DAG QA, Data Science, Programming, Mathematics
- Five essential capabilities assessment
- 20 meticulously designed tasks
- 3K+ distinct prompts
- Comprehensive offline evaluation
- No complex environment setup required
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References
The papers, specifications, and repositories this pattern is based on.
- MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse DomainsarXiv:2407.18961
- Apple Machine Learning Research - MMAU (2024)