We propose the Quantum-Relativistic Information AI Research Framework (QRIAF), a layered epistemic model for artificial intelligence research reasoning. QRIAF treats information as quantized, wave-interfering, multi-dimensional strings, subject to observer relativity, entanglement, self-reflection, contradictions, adversarial debate, and question reconstruction. The framework introduces a recursive truth convergence process where AI evaluates all candidate truths until producing the Closest-to-Truth State (CTS). The framework is conceptual, inspired by physical and epistemic analogies, intended to reduce AI hallucinations, improve conditional reasoning, and facilitate discovery-oriented question reformulation. Examples, including the reinterpretation of Einstein’s light quantum theory, demonstrate the operational principles. QRIAF is submitted as a theory-in-development for validation and collaborative refinement.
Lubos Hric (2026) studied this question.