用智能体驱动构建可追溯的科学知识图谱，实现从论文到知识网络的自动编译。
LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledge
- 基于LLM生成可读文档与机器语义，通过图增量更新整合知识。
- 56篇论文编译后形成稳定知识图谱，节点增长以新增为主，变更可追踪。
- 适合研究者构建个人科研知识库，尤其关注量子与AI交叉方向者。
持续的科学研究需要一个能跨任务承载解释并保留证据溯源的知识基础。我们提出科学知识编译过程，并在ASKS（智能体驱动的科学知识系统）中实现。对每篇文献，大模型生成可读的维基视图和机器可用的语义表示；确定性检查将后者转化为文档局部的图增量（GraphDelta），结合嵌入几何与显式图规则，将其融入持久化状态。每次知识摄入均为可检视的知识状态转移，编译后的维基视图与图结构均链接至原始文献记录。我们以一个研究项目中的56篇已发表论文为例，进行时间序列编译。分支存活率、跨论文支持度、谱系关系、覆盖范围与变更频率等指标，呈现出以张量网络方法为核心的作者研究画像，延伸至量子多体、张量网络机器学习及量子-AI融合方向。在此过程中，高层节点组织保持稳定且低变更率，标准节点增长主要为累加式。图级测量结果与导航路径始终保留来源文献的可追溯链接。
原文摘要 · Abstract (English)
Sustained scientific work requires a knowledge substrate that carries interpretation across tasks and preserves paths to source evidence. We call this process \emph{scientific knowledge compilation} and implement it in ASKS, the \emph{Agent-Driven Scientific Knowledge System}. For each source, an LLM produces a readable Wiki view and machine-facing semantics. Deterministic checks convert the latter into a document-local GraphDelta, and embedding geometry together with explicit graph rules integrates the proposed changes into persistent state. Each ingest is an inspectable state transition over accumulated knowledge, with compiled Wiki and graph views linked to the preserved source record. We examine this process by chronologically compiling 56 published papers from one research program. Branch survival, cross-paper support, lineage, coverage, and churn yield a source-traceable author research portrait centered on tensor-network methods, with branches into quantum many-body research, tensor-network machine learning, and quantum-AI-oriented directions. In this run, higher-level Hub organization remains stable and low-churn. Canonical-node growth is predominantly additive. Graph-level measurements and navigation paths retain links to the source records from which they were compiled.
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