Intelligence as Navigation on Informational Terrain

Contemporary artificial intelligence systems largely define intelligence in terms of probabilistic generation. From Bayesian inference to large-scale transformer architectures, uncertainty is modeled as a distribution over possible outcomes, and intelligence is expressed as the ability to compress uncertainty into coherent prediction. These systems are extraordinarily powerful within mapped representational manifolds, where interpolation and statistical generalization are sufficient. However, probabilistic generation embeds a structural assumption: that unknown states are, in principle, interpolable within an existing manifold. Even under low confidence, the system produces continuation. Informational absence is transformed into weighted possibility. This paper proposes a complementary structural definition: intelligence as admissible navigation across unknown informational terrain. Under the terrain model, informational reality is represented as a topology of verified presence states and structured absence. Presence and absence are categorically distinct. Movement is governed not by likelihood maximization but by admissibility constraints that preserve structural continuity. Unknown regions are not collapsed into prediction; they are preserved until resolved through lawful traversal. A central construct of the framework is the vantage state: an intermediate verified state that increases admissible adjacency. Rather than sampling broadly across possibility space, the system strategically restructures its local topology, expanding the set of lawful transitions. Exploration becomes directional rather than diffuse. Computation scales with admissible locality rather than global manifold size. This navigational architecture reframes intelligence from output generation to disciplined movement through uncertainty. It synthesizes insights from probabilistic modeling, heuristic search, epistemology, and AI interpretability into a unified structural model. A graph-theoretic formalization of informational terrain and admissible traversal is provided. The framework is not adversarial to generative AI. Instead, it positions generative systems as effective mechanisms within verified regions, while proposing navigational constraints as structural regulators at epistemic boundaries. Implications extend to artificial general intelligence, safety-critical systems, epistemic transparency, governance design, and computational efficiency. By making absence explicit and traversal constrained, navigational intelligence introduces a disciplined architecture for structured expansion into the unknown.