DeepSurvey提升论文综述深度与引用可靠性，避免误导性结论。
DeepSurvey: Enhancing Analytical Depth and Citation Reliability in Automated Survey Generation
- 从全文提取结构化要点，通过聚类和对比分析构建跨论文关系。
- 引用准确率提升12.3%，内容深度得分8.644，优于最强基线。
- 适合需要高质量自动化综述的研究者，尤其关注可信引用与深度分析。
随着科学文献快速增长，自动化综述生成已成为人工智能科学家和人类研究者的重要能力。然而，现有系统因依赖摘要和孤立处理论文，导致分析深度有限；同时，由于检索不精准和事后验证，引用不可靠，生成的综述浅显且可能误导研究者。我们提出DeepSurvey，一种智能体系统，同时解决上述问题。为增强深度，DeepSurvey从全文提取结构化关键点，通过聚类与对比分析建模跨论文关系，并结合代码仓库分析还原实现细节。为提升可靠性，它采用引文图扩展与混合过滤实现主题聚焦检索，强制执行证据约束的引用分配，并部署多粒度智能体精炼以验证引用与主张的一致性。实验表明，DeepSurvey在内容评分（8.644/10）和引用质量上均达最优，相比最强基线引用召回率提升12.3%、精确率提升9.3%；在跨领域泛化能力上表现更稳健（非计算机领域下降幅度从0.22降至0.69），领域专家评价其整体质量达83.3%，内容深度达100%。
原文摘要 · Abstract (English)
As scientific literature grows rapidly, automated survey generation has become a key capability for AI scientists and human researchers. However, existing systems suffer from limited analytical depth due to reliance on abstracts and isolated paper processing, and unreliable citations from imprecise retrieval and post-hoc grounding, producing superficial surveys and may mislead researchers. We present DeepSurvey, an agentic system that addresses both. To enhance depth, DeepSurvey extracts structured keynotes from full-text papers, models cross-paper relationships through clustering and comparative analysis, and integrates code-repository analysis to recover implementation-level details. To fortify reliability, it combines citation-graph expansion with hybrid filtering for topic-focussed retrieval, enforces evidence-constrained citation assignment, and deploys multi-granularity agentic refinement to validate citation-claim alignment. Experiments show that DeepSurvey achieves the highest content score (8.644/10) and citation quality (12.3% and 9.3% recall and precision gains over the strongest baseline), generalizes more robustly across domains (0.14 vs 0.22 to 0.69 CS-to-non-CS drop), and is preferred over human-written surveys by domain experts (83.3% overall quality, 100% content depth).
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