Full-Fidelity Semantic Aggregation: Navigating Datasets

Full-fidelity semantic aggregation presents significant advantages over utilizing publicly facing contemporary LLMs hosted by other organizations (e.g., cost, fewer required resources, interoperability, tail analysis, et cetera). Full-fidelity models can be trained with low-end/legacy hardware, become functional with virtually any dataset, and can be used in cross-dataset analysis. The model of truth approach that is generally made available to the general public seems to target a general audience by means of prescriptive heuristics, which involve issues related to dogma. It also lacks a more holistic familiarity through breadth in data. The approach outlined here exposes what the equivalent of neural network weights, or as it is labeled herein, conduciveness. It allows additional flexibility in assessments of heavily opinionated subjects. It allows analysis of divergence in variance and intensity. This method also opens a wide scope of innovation to occur in the academic world. The intent here is to provide a brief evaluation to prove the viability of the method.