Rewriting the Space of the Possible Recursive Creativity as Object–Space Coevolution. A Cumulative Theory of Second-Order Design and AI Retraining within a General Theory of Technology

Creativity is ordinarily assessed through the novelty and value of an output. This article develops a cumulative theoretical extension in which recursive creativity is defined as a causally traceable process of object–space coevolution: a produced object can become evidence that changes the design space governing subsequent generation, evaluation, or reproduction, while the reconfigured space changes the conditions under which subsequent objects can be produced. A creative system is represented as Sₜ = ⟨Vₜ, Eₜ, Rₜ⟩ (variation, evaluation, reproduction), with transition Sₜ₊₁ = Φₜ(Sₜ, Oₜ, Xₜ). The central recursive relation is therefore Oₜ → ΔSₜ → Sₜ₊₁ → Oₜ₊₁, rather than mere repetition of an output-generation cycle. The framework distinguishes degree-0 activity within a substantively fixed space, degree-1 transformation of V, E, or R, and degree-2 transformation of the rule Φ by which the space itself is transformed, under safeguards against self-validation, functional drift, and unbounded meta-regression. Five proposed diagnostics—effective diversity or coverage (H), external evaluative independence (εE), reproductive resistance (μR), correctability (K), and maturity (M)—separate recursive transformation from performance gain, parameter drift, or degeneration. The theory is operationalized for artificial-intelligence retraining through a provenance-based Recursive Creativity Record (RCR), which identifies when outputs become evidence that changes training procedures, evaluation criteria, or reproduction conditions. Within the General Theory of Technology, recursive creativity is formulated as Tᵢ → Cr → Tⱼ, a mechanism of technogenesis. Eight propositions with falsification conditions are retained, and a four-part research program is derived for formal dynamics, AI security and governance, political technology, and empirical testing. The article makes no empirical claim: its contribution is a disciplined, auditable architecture for identif