Projection-Aware Inference: Restoring Query Semantics to Machine Learning Models

Databases support explicit projection of a sample space prior to evaluation, allowing logic to be restricted dynamically at query time. In contrast, most machine learning systems collapse restriction, representation, and inference during training, exposing only a fixed inference interface at runtime. This paper introduces a projection-aware inference model in which restriction is a first-class, parameterized operation orthogonal to learning. By separating projection from inference semantics, models regain query-like behavior without retraining, enabling precision analysis, anomaly detection, and individualized reasoning that are difficult or impossible under traditional population-trained paradigms. ChatGPT contriibuted details and documentation