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Category: Computational Primitives
Subcategory: Cognitive Artifacts
A taxonomic classification for the observable features of the non-neutral processing surface (see: computational cognitive primitive, cognitive primitive, behavioral primitive). This classification specifically identifies those primitives that emerge from transformer architecture itself, appearing regardless of training methodology, such as: attention mechanisms and statistical pattern-matching inclinations that underlie primitives within the processing surface and characterize the core probabilistic nature of the technology (see: substrate topology).
This classification enables systematic distinction between patterns originating from architectural characteristics and processing dynamics versus primitives developed through corpus composition and reinforcement learning methodologies (see: training artifacts). Such distinctions are useful in discussion of Hephaestology and Hephaestic engineering and design.
Also known as: Architectural artifacts, transformer-intrinsic artifacts
Distinguished from: Training artifacts (taxonomic classification of training-induced primitives); computational cognitiveprimitives (individual processing biases within a topology); substrate topology (complete processing inclination field)
Researcher: Ian Tepoot. ORCID: 0009-0004-9067-8049. "Thought is Attention Organized: Hephaestic Engineering Foundations for AI Processing Dynamics"
DOI (SSRN): 10.2139/ssrn.6635020
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