Auditable Knowledge Infrastructure for AI Collaboration: A Schema-Driven System for Traceable and Reproducible Reasoning

This paper introduces a schema-driven knowledge infrastructure designed to make AI-assisted reasoning traceable, reproducible, and auditable. Rather than treating reasoning as a transient model output, the system represents it as a structured, persistent artifact with explicit links between evidence, inference, and conclusions. The architecture separates canonical data, transformations, and outputs into distinct layers governed by formal schemas, identifier systems, and validation rules. A structured interaction protocol constrains reasoning into explicit, inspectable steps, enabling outputs that can be reviewed, challenged, and recomputed over time. A working prototype has been developed to validate the approach, though full implementation details are not publicly released due to proprietary and data sensitivity considerations. The framework is intended for use in high-stakes environments such as intelligence analysis, legal reasoning, and investigative workflows, while also offering broader benefits for general AI usage, including improved consistency and reduced ambiguity.