How Far Are We from Genuinely Useful Deep Research Agents?
Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering benchmarks, while research on generating comprehensive reports remains overlooked. Worse, current benchmarks for report synthesis suffer from task complexity and subjective metrics -- this fails to reflect user demands and limits the practical utility of generated reports. To address these gaps, we present Fine-grained DEepResearch bench (FINDER), an enhanced benchmark consisting of 100 human-curated research tasks with 419 structured checklist items that standardize report structure, analytical depth, and factual grounding. Based on approximately 1,000 reports produced by mainstream DRAs, we further propose Deep rEsearch Failure Taxonomy (DEFT), the first failure taxonomy for deep research agents. DEFT contains 14 fine-grained failure modes across reasoning, retrieval, and generation, and is built upon grounded theory with human-LLM co-annotating and inter-annotator reliability validation. Our experimental findings reveal that current DRAs struggle not with task comprehension but with evidence integration, verification, and reasoning-resilient planning.
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Dingling Zhang
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He Zhu
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Jincheng Ren
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2025-12-02T08:39:42.538Z
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JinCheng Ren
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Kangqi Song
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Xinran Zhou
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Boyu Feng
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2025-12-29T14:25:31.555Z
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Boyu Feng
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Shudong Liu
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2025-12-03T09:18:24.389Z
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Shudong Liu
Sudanl
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Jiabin Luo
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Weihao Xie
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2026-02-12T13:59:09.618Z
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xieweihao
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Zhaohui Wang
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Tianrui Qin
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King Zhu
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Yuqing Wang
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Qianben Chen
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2026-03-23T11:02:47.710Z
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Chen
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Yuchen Eleanor Jiang
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Wei Wang
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Jiaheng Liu
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Wangchunshu Zhou
How Far Are We from Genuinely Useful Deep Research Agents?
Deep Research Agents (DRAs) aim to automatically produce analyst-level reports through iterative information retrieval and synthesis. However, most existing DRAs were validated on question-answering benchmarks, while research on generating comprehensive reports remains overlooked. Worse, current benchmarks for report synthesis suffer from task complexity and subjective metrics -- this fails to reflect user demands and limits the practical utility of generated reports. To address these gaps, we present Fine-grained DEepResearch bench (FINDER), an enhanced benchmark consisting of 100 human-curated research tasks with 419 structured checklist items that standardize report structure, analytical depth, and factual grounding. Based on approximately 1,000 reports produced by mainstream DRAs, we further propose Deep rEsearch Failure Taxonomy (DEFT), the first failure taxonomy for deep research agents. DEFT contains 14 fine-grained failure modes across reasoning, retrieval, and generation, and is built upon grounded theory with human-LLM co-annotating and inter-annotator reliability validation. Our experimental findings reveal that current DRAs struggle not with task comprehension but with evidence integration, verification, and reasoning-resilient planning.
FINDER is a benchmark for deep research agents with standardized human-curated tasks and DEFT is a failure taxonomy revealing that DRAs struggle with evidence integration, verification, and reasoning-resilient planning.
Deep Research Agents Fine-grained DEepResearch bench structured checklist items Deep rEsearch Failure Taxonomy reasoning retrieval generation failure modes reasoning-resilient planning