Autonomous editorial systems represent an emerging class of computational frameworks that transform how large volumes of information are ingested, organized, and analyzed. This work presents a structured, continuously operating editorial architecture that treats news and reports as persistent state rather than transient documents. The system separates editorial organization from investigative analysis, enabling deterministic orchestration of artificial intelligence components across ingestion, enrichment, clustering, verification, and persistence stages. We introduce a pipeline-based design in which stories evolve over time through incremental updates, automated re-evaluation, and contextual enrichment. The architecture supports scalable real-time processing while maintaining traceability, reproducibility, and editorial oversight. By framing editorial workflows as computational processes, the system enables algorithmic investigation, longitudinal analysis, and automated discovery of trends, inconsistencies, and emerging narratives. This paper formalizes the architectural principles, data flow, and operational characteristics of autonomous editorial systems and demonstrates how artificial intelligence can be integrated as a controlled, inspectable component rather than an opaque decision-maker. The proposed approach establishes a foundation for future research into machine-assisted journalism, automated investigation, and large-scale information synthesis.

翻译：自主编辑系统是一类新兴的计算框架，能够变革大规模信息的摄取、组织与分析方式。本文提出了一种结构化、持续运行的编辑架构，将新闻报道视为持续状态而非瞬时文档。该系统将编辑组织工作与调查研究相分离，支持在摄取、增强、聚类、验证与持久化阶段对人工智能组件进行确定性编排。我们引入基于流水线的设计，使故事叙事能够通过增量更新、自动重评估与上下文增强实现随时间演进。该架构支持可扩展的实时处理，同时保持可追溯性、可复现性与编辑监督。通过将编辑工作流转化为计算过程，该系统能够实现算法化调查、纵向分析以及趋势、矛盾与新兴叙事的自动发现。本文正式阐述了自主编辑系统的架构原理、数据流与运行特征，并展示了如何将人工智能作为可控、可检视的组件而非黑箱决策者加以集成。所提出的方法为机器辅助新闻业、自动化调查与大规模信息合成等未来研究奠定了基础。