DR$^{3}$-Eval: Towards Realistic and Reproducible Deep Research Evaluation
Deep Research Agents (DRAs) aim to solve complex, long-horizon research tasks involving planning, retrieval, multimodal understanding, and report generation, yet their evaluation remains challenging due to dynamic web environments and ambiguous task definitions. We propose DR$^{3}$-Eval, a realistic and reproducible benchmark for evaluating deep research agents on multimodal, multi-file report generation. DR$^{3}$-Eval is constructed from authentic user-provided materials and paired with a per-t
Record details
Published: 16 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (16 April 2026), “DR$^{3}$-Eval: Towards Realistic and Reproducible Deep Research Evaluation,” evidence record 5793, https://ethics.ai/record/5793 (originally published by arXiv).
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