Generative Reality: A Model of Reality as Sequential Token Generation with Conscious Readahead

Existing models of consciousness and reality fail to account for a specific phenomenological category: direct access to concrete future event sequences. This paper proposes Generative Reality (GR), which models reality as a sequential generative process structurally analogous to Large Language Model inference. Each event is probabilistically conditioned on the full preceding context, and individual consciousness functions as a read-only process capable of sampling the most probable continuation several tokens ahead. The model integrates with block universe theory, Strømme's universal consciousness field, quantum mechanics, string theory, Bohm's implicate order, Integrated Information Theory, Orch-OR, and predictive processing, while remaining agnostic to the nature of the generative source. GR is explicitly incompatible with "thought creates reality" frameworks. The paper presents axioms, structural mapping to LLM mechanics, explanatory scope, testable predictions, and acknowledged limitations.