DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911442576343040 |
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| author | Zhong, Joey Zhang, Hao Southern, Clare Yang, Jeremy Wang, Thomas Jung, Kate Zhang, Shu Yarats, Denis Ho, Johnny Ma, Jerry |
| author_facet | Zhong, Joey Zhang, Hao Southern, Clare Yang, Jeremy Wang, Thomas Jung, Kate Zhang, Shu Yarats, Denis Ho, Johnny Ma, Jerry |
| contents | We present DRACO (Deep Research Accuracy, Completeness, and Objectivity), a benchmark of complex deep research tasks. These tasks, which span 10 domains and draw on information sources from 40 countries, originate from anonymized real-world usage patterns within a large-scale deep research system. Tasks are sampled from a de-identified dataset of Perplexity Deep Research requests, then filtered and augmented to ensure that the tasks are anonymized, open-ended and complex, objectively evaluable, and representative of the broad scope of real-world deep research use cases. Outputs are graded against task-specific rubrics along four dimensions: factual accuracy (accuracy), breadth and depth of analysis (including completeness), presentation quality (including objectivity), and citation quality. DRACO is publicly available at https://hf.co/datasets/perplexity-ai/draco. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_11685 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity Zhong, Joey Zhang, Hao Southern, Clare Yang, Jeremy Wang, Thomas Jung, Kate Zhang, Shu Yarats, Denis Ho, Johnny Ma, Jerry Machine Learning Artificial Intelligence We present DRACO (Deep Research Accuracy, Completeness, and Objectivity), a benchmark of complex deep research tasks. These tasks, which span 10 domains and draw on information sources from 40 countries, originate from anonymized real-world usage patterns within a large-scale deep research system. Tasks are sampled from a de-identified dataset of Perplexity Deep Research requests, then filtered and augmented to ensure that the tasks are anonymized, open-ended and complex, objectively evaluable, and representative of the broad scope of real-world deep research use cases. Outputs are graded against task-specific rubrics along four dimensions: factual accuracy (accuracy), breadth and depth of analysis (including completeness), presentation quality (including objectivity), and citation quality. DRACO is publicly available at https://hf.co/datasets/perplexity-ai/draco. |
| title | DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.11685 |