The Cross-Architecture Reconstruction Test: Artificial Intelligence, Physical Representation, and the Structure of Natural Law

Physics has repeatedly changed its instruments, mathematical languages, and theories, but mature physical knowledge has largely been produced within a single broad cognitive lineage: human scientific cognition. This creates a persistent identification problem. When a successful physical theory contains a particular mathematical or structural feature, it is difficult to determine whether that feature is imposed primarily by the physical domain or partly by the architecture of the knower. This paper proposes the Cross-Architecture Reconstruction Test as an experimental framework for addressing that problem. The core idea is to hold the physical target and evidential constraints approximately fixed, vary the cognitive architecture, and compare independently reconstructed theories. The framework distinguishes three outcomes: structural convergence, representational divergence with structural equivalence, and provisionally irreducible structural divergence. It develops criteria for reconstruction independence, theory equivalence, cross-architecture invariance, and the joint evidential role of historical and cross-architecture invariance. It also introduces reverse reconstruction as an orthogonal diagnostic: artificial architectures with different resource profiles may sometimes recover scientifically relevant lower-order structure that mature human abstraction has compressed or suppressed. A staged experimental program is proposed, beginning with synthetic worlds and known physical systems before extending to representation-blinded quantum reconstruction and open fundamental physics. The broader claim is methodological rather than metaphysical. Artificial intelligence need not be treated as a privileged knower or a transparent route to ontology. Its distinctive value is that it may allow the architecture of the knower itself to become an experimentally variable parameter. This creates a form of experimental comparative epistemology: structures that remain stable across sufficiently independent and heterogeneous reconstructions gain evidence of architecture robustness, while systematic divergence reveals where scientific representation may depend on cognitive architecture. Instead of asking only whether a human theory corresponds to reality, the test asks what remains when the knower changes. Keywords: Cross-Architecture Reconstruction Test; artificial intelligence; scientific representation; cognitive architecture; cross-architecture invariance; theoretical equivalence; scientific realism; structural realism; natural law; reverse reconstruction; experimental epistemology; philosophy of physics