cs.AI updates on arXiv.org 07月15日
DeepResearch$^{\text{Eco}}$: A Recursive Agentic Workflow for Complex Scientific Question Answering in Ecology
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介绍一种基于LLM的智能科学合成系统DeepResearch$^{ ext{Eco}},支持对原始研究问题的递归、深度和广度探索,提升文献检索的多样性和细致性,应用于生态研究问题,效果显著。

arXiv:2507.10522v1 Announce Type: new Abstract: 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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科学合成系统 LLM技术 生态研究
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