From Mathematical Superintelligence to Physical Discovery

Recent advances in artificial intelligence suggest that coordinated populations of capablereasoning agents may solve mathematical problems of extraordinary complexity. Suchachievements raise a distinct question: Can comparable systems advance fundamental physicsby identifying the physical mechanisms responsible for nature’s observed behavior, rather thanmerely extending mathematical descriptions already accepted by scientific convention?This paper proposes a human-directed, observation-first AI research architecture forinvestigating Space-Phase (SP3) theory. SP3 proposes that physical space is a participatingmedium interacting with energy through nested configurations, conditioning, memory, andrelaxation. It seeks common explanations for matter formation, wave behavior, magnetism,gravity, galaxy dynamics, and cosmological development.A practical obstacle is the imbalance between established scientific literature, extensivelyrepresented in language-model training, and less widely circulated alternative research. Theauthor reports 1,098 SP3 papers deposited on Zenodo as of November 12, 2026. Publicavailability does not establish their inclusion in major models’ training data. This paperproposes retrieval-augmented reasoning, version-controlled mechanism registries, independentobservation auditing, and adversarial comparison with established physics. Occam’s razorSP3 Space-Phase Theory • AI-Assisted Physical Discoveryshould favor mechanisms that explain reliable observations, preserve quantitative successes,predict new outcomes, and require fewer independent assumptions. The Ship Neutron thoughtexperiment and NGC 628 study are proposed as complementary initial investigations.