Beyond Representation: Towards a Foundational Science

Modern science relies on representational models that organize empirical observations through constructs such as electrons, energy levels, spacetime, wavefunction and causal mechanisms. While these models achieved extraordinary predictive success, they also led to an implicit inversion in which these unobservable theoretical constructs are treated as explanatory ground and observable spectral regularities as derivative. Recent large-scale analyses of atomic spectra reveal that diverse physical measurements exhibit unexpectedly low-dimensional and relational organization recoverable directly through frequency-domain relations, without introducing hidden entities or theoretical assumptions. These results show that classification, compression, generalization, and prediction emerge naturally from intrinsic spectral structure prior to representational interpretation. The paper argues that representation reorganizes empirical structure rather than generates it and becomes methodologically secondary once observable relations exhibit predictive closure. The persistent demand for additional mechanistic explanation arises not from deficiencies in empirical structure, but from the expectation that physical understanding must ultimately be translated into narratives of localized objects, linear causality, and particle interactions. Once representational constructs acquire explanatory authority beyond their empirical origin, they tend to stabilize and reify, expanding the theoretical apparatus to accommodate inconsistencies. This motivates the concept of representational debt, whereby additional abstract layers accumulate beyond empirical necessity, increasing descriptive complexity and delaying the discovery of simpler organizing principles. More broadly, the analysis motivates Foundational Science, a discipline concerned not with ontological entities or local causal mechanisms, but with invariant relations, scaling laws, and structural constraints. The Spectral Framework is presented as an empirical realization of this methodological perspective, illustrating how scientific explanation may be re-grounded in simple organizational principles already present in measurements.