From Parameter Space to Structure Space: A Knowledge Formalization Compilation Framework for LLMs Based on Daopology—GlyphCode—GlyphCode Reasoner

Large language models (LLMs) face a fundamental contradiction in knowledge acquisition and transfer: knowledge is stored implicitly in distributed parameters, making it difficult for humans to directly read, verify, or transfer it across models. This leads to repeated and costly post-training for each model iteration. This paper proposes a theoretical framework that compiles LLM knowledge into explicit structural entities that are both human-readable and machine-reasonable. The framework integrates three core components: Daopology, which provides a structured coordinate system for knowledge (79 minimal expressive parameters); GlyphCode, which offers a universal symbolic encoding system for structural entities; and the GlyphCode Reasoner, which serves as a neuro-symbolic inference engine. We further design a fully automated knowledge extraction and verification pipeline based on multi-LLM interaction and validation, replacing traditional manual knowledge engineering.In particular, we reposition existing knowledge graphs as dictionaries and indexes, and vector databases as semantic indexing and retrieval layers. We propose a “two-end alignment, bidirectional compilation” engineering architecture in which Daopology serves as the structural core (substance), while knowledge graphs, vector databases, and ontology systems serve as infrastructure (function). Through forward compilation (structure → graph/vector) and backward compilation (graph/vector → structure), bidirectional mapping is achieved. The system actively discovers knowledge that is completely absent in graphs but may exist in LLMs, extracts it through multi-LLM cross-validation into Daopology structures, and thereby enriches and perfects the knowledge base.Quantitative analysis based on existing empirical data shows that the startup cost of this framework can be controlled at the level of several thousand USD. It achieves approximately 48% information retention on comprehension-intensive tasks and up to 94.2% in domain knowledge injection scenarios. Compared with the original LLM paradigm of repeated training and inference, computational cost is reduced by 1–2 orders of magnitude, and development time is shortened by 60%–90%. The core conclusion is: LLMs provide experience, Daopology provides coordinates, GlyphCode provides encoding, the GlyphCode Reasoner provides inference, and graphs and vector stores provide indexing—five components working together to transform knowledge from “unreadable parameters” into “auditable, transferable, augmentable, and growable” structures.