DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity
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Abstract (arXiv)
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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 12 Feb 2026.
Builds on
24 of 57 references.
References · 24
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- 2025BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agentscited by 616
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- 2025xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluationscited by 118
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- 2026DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agentscited by 44
12 more
- 2024Towards Evaluating and Building Versatile Large Language Models for Medicinecited by 38
- 2025LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wildcited by 35
- 2025ReportBench: Evaluating Deep Research Agents via Academic Survey Taskscited by 34
- 2026DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reportscited by 33
- 2025DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesiscited by 31
- 2025ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquirycited by 30
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- 2025ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklistscited by 22
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- 2025FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysiscited by 13
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Citing papers per year: 2026: 11.
11 papers.
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- 2026Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agentscited by 3
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- 2026FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverablescited by 1
- 2026WANDR: A Benchmark for Wide and Deep Researchcited by 1
- 2026A Survey on Rubric-Guided Reinforcement Learning for Language Modelscited by 1
- 2026Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedbackcited by 0
- 2026WinSyn: An Automated Pipeline for Realistic Enterprise Question-Answering Evaluationcited by 0
- 2026WinSyn: An Automated Pipeline for Realistic Enterprise Question-Answering Evaluation10 Sep 2026
- 2026A Survey on Rubric-Guided Reinforcement Learning for Language Models27 Aug 2026
- 2026WANDR: A Benchmark for Wide and Deep Research14 Aug 2026
- 2026FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables4 Aug 2026
- 2026FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality14 Jul 2026
- 2026Agentic Routing: The Harness-Native Data Flywheel13 Jul 2026
- 2026Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback8 Jun 2026
- 2026From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape7 Jun 2026
- 2026Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents3 Apr 2026
- 2026Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias3 Apr 2026
- 2026Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence16 Feb 2026