让AI自主构建可自我演化的知识图谱，持续生成新知识。
Agentic Deep Graph Reasoning Yields Self-Organizing Knowledge Networks
- 用大模型动态迭代构建知识图谱，每步生成新概念并融合到全局结构中。
- 图谱演化出枢纽节点与连接不同模块的桥梁节点，呈现无标度网络特征。
- 适用于材料设计等科学问题，能生成跨领域创新思路，适合科研探索者。
我们提出一种代理式、自治的图结构扩展框架，通过迭代方式在原位构建并优化知识结构。不同于依赖静态抽取或单次学习的传统知识图谱构建方法，该方法将具备推理能力的大语言模型与持续更新的图表示相结合。每一步中，系统主动生成新概念与关系，将其合并至全局图，并根据演化后的结构制定后续提示。通过这一反馈驱动的循环，模型将信息组织成具有枢纽节点、稳定模块性及连接异构知识簇的桥接节点的无标度网络。经过数百次迭代，新节点与边持续出现而不饱和，中心性指标与最短路径分布演变，实现更分散的连通性。分析揭示了高度连接的‘枢纽’概念涌现及‘桥接’节点影响力的动态变化，表明代理式、自增强的图结构构建可产生开放、连贯的知识体系。应用于材料设计任务时，通过提取节点级与协同级原则，实现真正的知识合成，生成超越简单总结的跨域创新想法，强化了该框架在开放式科学发现中的潜力。我们还讨论了其在科学发现中的其他应用，并展望了提升可扩展性与可解释性的未来方向。
原文摘要 · Abstract (English)
We present an agentic, autonomous graph expansion framework that iteratively structures and refines knowledge in situ. Unlike conventional knowledge graph construction methods relying on static extraction or single-pass learning, our approach couples a reasoning-native large language model with a continually updated graph representation. At each step, the system actively generates new concepts and relationships, merges them into a global graph, and formulates subsequent prompts based on its evolving structure. Through this feedback-driven loop, the model organizes information into a scale-free network characterized by hub formation, stable modularity, and bridging nodes that link disparate knowledge clusters. Over hundreds of iterations, new nodes and edges continue to appear without saturating, while centrality measures and shortest path distributions evolve to yield increasingly distributed connectivity. Our analysis reveals emergent patterns, such as the rise of highly connected 'hub' concepts and the shifting influence of 'bridge' nodes, indicating that agentic, self-reinforcing graph construction can yield open-ended, coherent knowledge structures. Applied to materials design problems, we present compositional reasoning experiments by extracting node-specific and synergy-level principles to foster genuinely novel knowledge synthesis, yielding cross-domain ideas that transcend rote summarization and strengthen the framework's potential for open-ended scientific discovery. We discuss other applications in scientific discovery and outline future directions for enhancing scalability and interpretability.
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