ASKS系统用LLM编译56篇论文，把科学知识变成可追溯的图谱，比传统方法更系统化。
ASKS系统使用大语言模型处理56篇论文，生成可读的Wiki视图和机器可读语义。系统通过确定性检查将语义转换为文档级GraphDelta，利用嵌入几何和显式图规则整合到持久状态。编译过程生成了以张量网络方法为中心的研究图谱，包含量子多体研究、张量网络机器学习和量子AI方向。每个编译步骤都可追溯源文献，保持与原始记录的链接。
LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledge
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.