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Category: System Theory
Subcategory: System Substrate Dynamics
The examination of Processing Dynamics within attention-based language transformers specifically operating as a Substrate within a cognitively engineered system. The Hephaestological observation and implementation-based testing focuses on analysis of the way in which the architecture influences the statistical routing of attention-circuits within the high-dimensional representational space composed of static post-training transformer weights that establish processing characteristics (see: semantic neuron, substrate topology, reasoning surface, computational cognitive primitives)—as well as evaluation of substrate specifications and characteristics for suitability for operation within cognitive architecture implementations (see: neurosymbolic overlay).
System substrate dynamics concerns itself with: determination of substrate sufficiency to maintain a representational schema of sufficient granularity to support reasoning structures (see: world schema threshold, heuristic matrix, parameter sufficiency threshold, semantic sufficiency vs. surfeit); operational characteristics impacting both cognitive establishment and sustained reasoning operations and the processing dynamics therein (see: instructional-operational dichotomy, cognitive performance envelope, substrate resistance threshold); system design principles for management of the processing dynamics and tolerances within the integrated system (see: heuristic encapsulation vs. entanglement, heuristic overcapping).
Also known as: Attention-based architectural dynamics, cognitive system processing analysis
Distinguished from: Processing dynamics (general dynamic attention allocation analysis); salience dynamics (semiotic attention analysis & engineering); resolution dynamics (system pattern-completion analysis & engineering); Interpretability research (mechanistic circuit tracing)
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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