# DRACO: a Cross-Domain Benchmark for Deep Research Accuracy, Completeness, and Objectivity

arXiv: https://arxiv.org/abs/2602.11685 · HTML: https://mlsanity.com/paper/2602.11685/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 24 of 24)

- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents](https://mlsanity.com/paper/2504.12516/) (2025)
- [Humanity's Last Exam](https://mlsanity.com/paper/2501.14249/) (2025)
- [DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents](https://mlsanity.com/paper/2506.11763/) (2025)
- [Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation](https://mlsanity.com/paper/2409.12941/) (2024)
- [Deep Research Agents: A Systematic Examination And Roadmap](https://mlsanity.com/paper/2506.18096/) (2025)
- [xbench: Tracking Agents Productivity Scaling with Profession-Aligned Real-World Evaluations](https://mlsanity.com/paper/2506.13651/) (2025)
- [ResearchRubrics: A Benchmark of Prompts and Rubrics For Evaluating Deep Research Agents](https://mlsanity.com/paper/2511.07685/) (2025)
- [Deep Research: A Survey of Autonomous Research Agents](https://mlsanity.com/paper/2508.12752/) (2025)
- [Finance Agent Benchmark: Benchmarking LLMs on Real-world Financial Research Tasks](https://mlsanity.com/paper/2508.00828/) (2025)
- [LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://mlsanity.com/paper/2412.17259/) (2024)
- [DeepSearchQA: Bridging the Comprehensiveness Gap for Deep Research Agents](https://mlsanity.com/paper/2601.20975/) (2026)
- [Towards Evaluating and Building Versatile Large Language Models for Medicine](https://mlsanity.com/paper/2408.12547/) (2024)
- [ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks](https://mlsanity.com/paper/2508.15804/) (2025)
- [LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild](https://mlsanity.com/paper/2510.14240/) (2025)
- [DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis](https://mlsanity.com/paper/2508.20033/) (2025)
- [DeepResearch Bench II: Diagnosing Deep Research Agents via Rubrics from Expert Reports](https://mlsanity.com/paper/2601.08536/) (2026)
- [ResearcherBench: Evaluating Deep AI Research Systems on the Frontiers of Scientific Inquiry](https://mlsanity.com/paper/2507.16280/) (2025)
- [MedBrowseComp: Benchmarking Medical Deep Research and Computer Use](https://mlsanity.com/paper/2505.14963/) (2025)
- [ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklists](https://mlsanity.com/paper/2506.01241/) (2025)
- [DRBench: A Realistic Benchmark for Enterprise Deep Research](https://mlsanity.com/paper/2510.00172/) (2025)
- [MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents](https://mlsanity.com/paper/2601.12346/) (2026)
- [FinDeepResearch: Evaluating Deep Research Agents in Rigorous Financial Analysis](https://mlsanity.com/paper/2510.13936/) (2025)
- [DeepResearchEval: An Automated Framework for Deep Research Task Construction and Agentic Evaluation](https://mlsanity.com/paper/2601.09688/) (2026)

## Built on by (most cited first, 14 of 14)

- [Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents](https://mlsanity.com/paper/2604.03173/) (2026)
- [FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality](https://mlsanity.com/paper/2607.12252/) (2026)
- [From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape](https://mlsanity.com/paper/2606.08625/) (2026)
- [Agentic Routing: The Harness-Native Data Flywheel](https://mlsanity.com/paper/2607.11399/) (2026)
- [A Survey on Rubric-Guided Reinforcement Learning for Language Models](https://mlsanity.com/paper/2608.27505/) (2026)
- [Dr-CiK: A Benchmark testing Deep Research for Context-Aided Forecasting](https://mlsanity.com/paper/2605.27904/) (2026)
- [Hunt Globally: Wide Search AI Agents for Drug Asset Scouting in Investing, Business Development, and Competitive Intelligence](https://mlsanity.com/paper/2602.15019/) (2026)
- [Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias](https://mlsanity.com/paper/2604.02923/) (2026)
- [FinProBench: Evaluating Financial AI Agents with Role-Grounded Rubrics Derived from Professional Deliverables](https://mlsanity.com/paper/2608.04077/) (2026)
- [WANDR: A Benchmark for Wide and Deep Research](https://mlsanity.com/paper/2608.14747/) (2026)
- [Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback](https://mlsanity.com/paper/2606.09748/) (2026)
- [DRFLOW: A Deep Research Benchmark for Personalized Workflow Prediction](https://mlsanity.com/paper/2606.18191/) (2026)
- [WinSyn: An Automated Pipeline for Realistic Enterprise Question-Answering Evaluation](https://mlsanity.com/paper/2609.12171/) (2026)
- [LifeSearch: Benchmarking Deep Research Agents for Real-World Life and Productivity](https://mlsanity.com/paper/2610.18092/) (2026)

## 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.