现有的研究基础设施基本上是以文档为中心的,，提供论文之间的引用链接，但缺乏方法论演变的明确表示。特别是,，它没有捕获解释研究方法如何以及为何出现, 适应, 并相互构建的结构化关系。随着人工智能驱动的研究代理作为科学知识的新消费者类别的兴起,，这种限制变得越来越重要,，因为此类代理无法从非结构化文本可靠地重建方法演化拓扑。我们引入了 Intern-Atlas, 一个方法论演化图，它自动识别方法级实体,，推断方法论, 之间的沿袭关系，并捕获驱动连续创新之间转换的瓶颈。由涵盖 AI 会议, 期刊, 和 arXiv 预印本, 的 1,030,314 篇论文构建而成，生成的图表包含 9,410,201 个语义类型边,，每个边都基于逐字来源证据,，形成方法论开发的可查询因果网络。为了操作这个结构,，我们进一步提出了一种自引导时间树搜索算法，用于构建进化链，跟踪方法随时间的进展。我们根据专家策划的真实进化链评估结果图的质量，并观察到强一致性。此外, 我们证明 Intern-Atlas 可以实现创意评估和自动化创意生成中的下游应用。我们将方法论演化图定位为新兴自动化科学发现的基础数据层。
Existing research infrastructure is fundamentally document-centric, providing citation links between papers but lacking explicit representations of methodological evolution. In particular, it does not capture the structured relationships that explain how and why research methods emerge, adapt, and build upon one another. With the rise of AI-driven research agents as a new class of consumers of scientific knowledge, this limitation becomes increasingly consequential, as such agents cannot reliably reconstruct method evolution topologies from unstructured text. We introduce Intern-Atlas, a methodological evolution graph that automatically identifies method-level entities, infers lineage relationships among methodologies, and captures the bottlenecks that drive transitions between successive innovations. Built from 1,030,314 papers spanning AI conferences, journals, and arXiv preprints, the resulting graph comprises 9,410,201 semantically typed edges, each grounded in verbatim source evidence, forming a queryable causal network of methodological development. To operationalize this structure, we further propose a self-guided temporal tree search algorithm for constructing evolution chains that trace the progression of methods over time. We evaluate the quality of the resulting graph against expert-curated ground-truth evolution chains and observe strong alignment. In addition, we demonstrate that Intern-Atlas enables downstream applications in idea evaluation and automated idea generation. We position methodological evolution graphs as a foundational data layer for the emerging automated scientific discovery.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)