Post-Document Science: From Static Narratives to Intelligent Objects

Scientific publishing is currently constrained by an unstructured narrative bottleneck paradigm, which increasingly diverges from the scale, complexity, and computational nature of modern research. Despite rapid advancements in data generation and analysis, scientific knowledge is predominantly disseminated as static narrative artifacts, thereby limiting reproducibility, machine accessibility, and cumulative integration. This study explores how scientific communication can be restructured to facilitate scalable validation and reliable knowledge accumulation. We propose the Object-Oriented Scientific Information paradigm, wherein scientific contributions are represented as executable, machine-interpretable objects that integrate structured data, reproducible methodologies, and formally encoded semantic claims. To operationalize this paradigm, we delineate the architecture of an Autonomous Knowledge Engine, a modular neuro-symbolic system that combines domain-specialized Mixture-of-Experts routing, formal verification of claims, and an information-theoretic filter based on marginal information gain. This architecture enables continuous validation, redundancy control, and the integration of scientific contributions within an active knowledge graph. The analysis demonstrates that Object-Oriented Scientific Information (OOSI) and Autonomous Knowledge Engine (AKE) fundamentally differ from existing document-based, executable, and semantic publishing models by shifting epistemic control from narrative evaluation to computational verification. We conclude that transitioning toward a computable scientific record is essential for sustaining reliable and self-correcting science in the context of accelerating knowledge production.