Beyond Correspondence: Generative Reality, Cognitive Protocols, and Knowledge Beyond Human Understanding

Debates over scientific realism are commonly organized around a relation between theory and reality. Scientific realists argue that the empirical and explanatory success of mature theories supports some form of commitment to their entities, structures, or approximate truth. Constructive empiricists restrict the epistemic aim of science to empirical adequacy. Structural realists shift commitment from theoretical entities to structures that survive theory change. Despite their differences, these positions typically begin with an already constituted scientific representation and then ask what epistemic or ontological relation it bears to reality. This paper argues that a logically prior problem has not received comparable attention: how does an independently existing and dynamically generated reality become epistemically readable to a finite cognitive system in the first place? Building on the ontology--generation--readout architecture of Generative Ontology and Closure Dynamics (GOCD), we propose a framework we call \emph{readout epistemology}. We distinguish an ontological layer $\mathcal O$, a generative layer $\mathcal G$, and a readout protocol $\Pi_i$ associated with cognitive system $i$. Epistemically accessible representation is written schematically as\[R_i=\Pi_i[\mathcal G(\mathcal O)].\]The readout protocol is minimally decomposed into\[\Pi_i=\Pi(B_i,\Gamma_i,\Theta_i),\]where $B_i$ denotes effective bandwidth, $\Gamma_i$ gating, and $\Theta_i$ resolution or salience thresholds. The framework preserves ontological independence while rejecting representational independence. Reality may exist and generate structures independently of a knower, while the form in which those structures become epistemically accessible remains protocol-dependent. Correspondence is therefore not abandoned but relocated: it becomes a downstream relation to be evaluated only after representation has been generated through a readout architecture. Artificial intelligence gives this framework new empirical significance. AI systems can operate under effective memory, search, parallelization, and verification regimes radically different from those available to individual humans. Contemporary work on computational opacity has already shown that machine-generated proofs may support mathematical knowledge even when neither the generating process nor the complete proof is human-surveyable. We argue that the deeper consequence is not opacity itself but \emph{readout divergence}: different cognitive protocols may access different regions of the space of scientifically viable representations. This leads to three main contributions. First, we formulate a generative--readout architecture for scientific representation. Second, we distinguish detection, verification, and comprehension thresholds, and correspondingly distinguish generative regularities, predictive laws, formal laws, and intelligible laws. Third, we introduce the concept of a \emph{cross-readout invariant}: a structure independently recovered across sufficiently different cognitive protocols. Such invariance may provide a new source of realist evidence, complementary to the historical invariance emphasized by structural realism. The resulting view has a broader implication. Scientific reliability need not remain coextensive with human intelligibility. A generative structure may be real, scientifically exploitable, formally verifiable, and predictively powerful without admitting any presently available human-scale compression. AI may therefore change not merely the speed at which science advances, but the boundary of what can enter science as knowledge at all.