用递归智能体系统自动回答复杂生态学问题，整合文献效率提升21倍。
DeepResearch$^{\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology
- 构建递归式智能体工作流，支持深度与广度可控的文献探索。
- 在49个生态学问题上，每千字集成文献量提升14.9倍，最高达21倍。
- 参数可调、推理透明，适合需要高精度综述的研究者使用。
我们提出DeepResearch$^\text{Eco}$，一种基于大模型的智能体系统，用于自动化科学合成，支持对原创性科研问题的递归式、深度与广度可控的文献探索，提升检索多样性与细微性。不同于传统检索增强生成流程，DeepResearch实现用户可控的合成过程，具备透明推理与参数驱动配置能力，可在保持分析严谨性的同时，高效整合领域特定证据。应用于49个生态学研究问题，其源文献整合量最高提升21倍，每千字集成来源数量增加14.9倍。高参数设置可达到专家级分析深度与上下文多样性。源代码见：https://github.com/sciknoworg/deep-research。
原文摘要 · Abstract (English)
We introduce DeepResearch$^{\text{Eco}}$, a novel agentic LLM-based system for automated scientific synthesis that supports recursive, depth- and breadth-controlled exploration of original research questions -- enhancing search diversity and nuance in the retrieval of relevant scientific literature. Unlike conventional retrieval-augmented generation pipelines, DeepResearch enables user-controllable synthesis with transparent reasoning and parameter-driven configurability, facilitating high-throughput integration of domain-specific evidence while maintaining analytical rigor. Applied to 49 ecological research questions, DeepResearch achieves up to a 21-fold increase in source integration and a 14.9-fold rise in sources integrated per 1,000 words. High-parameter settings yield expert-level analytical depth and contextual diversity. Source code available at: https://github.com/sciknoworg/deep-research.
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