Abstract
In this study, we propose a computational framework termed Quantum Neuro-Semantics (QNS) to model and map latent space trajectories within transformer-based neural language models generating micro-fictions grounded in counterfactual physics. By perturbing base physical constants within prompt conditions—such as altered Planck constants, variable gravitational coupling, or localized retrocausality—we track the geometric topology and curvature of semantic vectors across deep network layers. Using high-dimensional manifold learning and differential geometry metrics, we isolate semantic geodesics that correspond to logical coherence within physically impossible narrative worlds. Our findings demonstrate that counterfactual physical constraints induce structured topological phase transitions in the model's intermediate representations, resulting in distinct cluster bifurcations that correlate with narrative resolution. Furthermore, we reveal that transformer architectures possess latent sub-manifolds capable of maintaining self-consistent speculative physical laws without collapsing into semantic entropy. This work bridges neural representational geometry, narrative dynamics, and speculative physics, providing new mathematical and empirical tools for evaluating high-dimensional creative synthesis in artificial intelligence systems.
