Publishing OS: A Deterministic Protocol for Epistemic Integrity and Multi-Target Knowledge Compilation in the Generative AI Era

The proliferation of Large Language Models (LLMs) has reduced the marginal cost of automated text generation to near zero, yet it has simultaneously induced a systemic collapse in long-term epistemic consistency across multi-volume published works. Modern publishing pipelines lack formal, machine-verifiable boundaries between structural semantics and stylistic presentation, leaving long-form knowledge architectures vulnerable to structural drift, hallucinated dependencies, and ideological self-contradiction. To address this crisis, we introduce Publishing OS (v1.0.0), an open, deterministic protocol that models long-form intellectual output as an Epistemic Directed Acyclic Graph (DAG). Operating under a strict separation between Fence (immutable semantic topology) and Paint (media-dependent execution layers), Publishing OS comprises three core architectural layers: (1) an Epistemic Substrate Layer enforcing a Minimum Viable Schema (MVS) of seven presentation-free fields; (2) a Safety Kernel Layer that executes automated cycle-detection algorithms and tag-cluster invariant checks against an archival Data Moat to prevent cross-volume epistemic collisions; and (3) a Multi-Compile Engine that deterministically renders verified Master JSON files into four parallel formats (Academic LaTeX/TikZ, General EPUB3/Markdown, Visual UI React components, and Media slide decks) without semantic loss. We demonstrate that this protocol transforms static publication archives into executable, machine-verifiable knowledge assets, providing a scalable architectural foundation for autonomous publishing ecosystems and multi-agent knowledge synthesis.