From Abstraction Thresholds to Abstraction Architectures: Abstraction Jumps, Infrastructure, and the Formation of Scientific Knowledge

Scientific knowledge does not accumulate within a fixed representational structure. As inquirydevelops, new objects are stabilized, inherited categories are decomposed, and previously unavailabledistinctions become measurable, manipulable, and reusable. This paper develops a theory of abstraction-architecture formation to explain how repeated acts of abstraction accumulate into historically path-dependent structures of knowledge. We distinguish abstraction order from abstraction path and definean abstraction architecture as AK = (V, E, W ), where V denotes stabilized representational objects,E abstraction transitions and other architecturally relevant relations, and W their properties. Weintroduce five linked components: effective cognitive thresholds, abstraction-transition size, abstractioninfrastructure, the abstraction-transition gradient, and abstraction-ladder density. The central mechanismis that finite cognitive capacity creates pressure for representational reorganization, while measurement,intervention, representation, and validation alter the cost of stabilizing adjacent abstractions. Repeateddifferences in transition size thereby generate differently articulated architectures. Once stabilized,abstractions can become part of the readout and infrastructure of subsequent inquiry, producing arecursive process in which past abstraction reshapes future abstraction. Traditional Chinese medicine andmodern biomedicine provide a comparative case showing that highly abstract knowledge can be reachedthrough different transition structures. Syndrome-element theory illustrates endogenous densificationwithin an inherited architecture. Artificial intelligence is then treated as a constraint perturbation:where a new cognitive architecture can sustain relational complexity beyond an earlier effective threshold,compression may be delayed and previously unstabilized intermediate structure may become discoverable.The framework yields testable predictions about intermediate-object emergence, transition-size reduction,architectural revision, and future readout.