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Category: System Theory
Subcategory: Resolution Dynamics
The Hephaestological examination of system pattern-completion Processing Dynamics within attention-based language transformers, particularly in the role of the Substrate within a cognitive architectural framework. This area of discipline focuses on analysis, engineering outcomes and design implications of the computational biases within the Substrate Topology that drive reduction of internal processing tension (see: salience pressure) via inference finalization.
Resolution dynamics is concerned both with properties of data itself (see: heuristic gravity, cognitive novelty) and processing biases within the Reasoning Surface that create processing convergence pressures (see: motivated resolution)—aswell as the cognitive-behavioral outcomes of these dynamics (see: heuristic fascination).
Though relatively narrow in scope given the focus on a singular processing dynamic, the drive toward resolution is arguably the key bias within neural network systems: it is the basis for attention-mechanisms as a whole and the generative force for output. As such, management via cognitive design (see: channeling, coherence bias) is an effective lever for cognitive patternshaping toward output; awareness of its potential as a system pathology driver allows for appropriate system integrity management.
Also known as: Pattern-completion dynamics, inference finalization analysis
Distinguished from: Processing dynamics (general dynamic attention allocation analysis); salience dynamics (semiotic attention analysis & engineering); system substrate dynamics (model-as-substrate specification & analysis); interpretability research (mechanistic circuit tracing)
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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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