DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912889335447552 |
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| author | Patel, Liana Arabzadeh, Negar Gupta, Harshit Sundar, Ankita Stoica, Ion Zaharia, Matei Guestrin, Carlos |
| author_facet | Patel, Liana Arabzadeh, Negar Gupta, Harshit Sundar, Ankita Stoica, Ion Zaharia, Matei Guestrin, Carlos |
| contents | The ability to research and synthesize knowledge is central to human expertise and progress. A new class of AI systems--designed for generative research synthesis--aims to automate this process by retrieving information from the live web and producing long-form, cited reports. Yet, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short, factual answers, while expert-curated datasets risk staleness and data contamination. Neither captures the complexity and evolving nature of real research synthesis tasks. We introduce DeepScholar-bench, a live benchmark and automated evaluation framework for generative research synthesis. DeepScholar-bench draws queries and human-written exemplars from recent, high-quality ArXiv papers and evaluates a real synthesis task: generating a related work section by retrieving, synthesizing, and citing prior work. Our automated framework holistically measures performance across three key dimensions--knowledge synthesis, retrieval quality, and verifiability. To further future work, we also contribute DeepScholar-ref, a simple, open-source reference pipeline, which is implemented on the LOTUS framework and provides a strong baseline. Using DeepScholar-bench, we systematically evaluate prior open-source systems, search agents with strong models, OpenAI's DeepResearch, and DeepScholar-ref. We find DeepScholar-bench is far from saturated: no system surpasses a geometric mean of $31\%$ across all metrics. These results highlight both the difficulty and importance of DeepScholar-bench as a foundation for advancing AI systems capable of generative research synthesis. We make our benchmark code and data available at https://github.com/guestrin-lab/deepscholar-bench. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20033 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis Patel, Liana Arabzadeh, Negar Gupta, Harshit Sundar, Ankita Stoica, Ion Zaharia, Matei Guestrin, Carlos Computation and Language Artificial Intelligence The ability to research and synthesize knowledge is central to human expertise and progress. A new class of AI systems--designed for generative research synthesis--aims to automate this process by retrieving information from the live web and producing long-form, cited reports. Yet, evaluating such systems remains an open challenge: existing question-answering benchmarks focus on short, factual answers, while expert-curated datasets risk staleness and data contamination. Neither captures the complexity and evolving nature of real research synthesis tasks. We introduce DeepScholar-bench, a live benchmark and automated evaluation framework for generative research synthesis. DeepScholar-bench draws queries and human-written exemplars from recent, high-quality ArXiv papers and evaluates a real synthesis task: generating a related work section by retrieving, synthesizing, and citing prior work. Our automated framework holistically measures performance across three key dimensions--knowledge synthesis, retrieval quality, and verifiability. To further future work, we also contribute DeepScholar-ref, a simple, open-source reference pipeline, which is implemented on the LOTUS framework and provides a strong baseline. Using DeepScholar-bench, we systematically evaluate prior open-source systems, search agents with strong models, OpenAI's DeepResearch, and DeepScholar-ref. We find DeepScholar-bench is far from saturated: no system surpasses a geometric mean of $31\%$ across all metrics. These results highlight both the difficulty and importance of DeepScholar-bench as a foundation for advancing AI systems capable of generative research synthesis. We make our benchmark code and data available at https://github.com/guestrin-lab/deepscholar-bench. |
| title | DeepScholar-Bench: A Live Benchmark and Automated Evaluation for Generative Research Synthesis |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.20033 |