Time Dilation for LLM Hallucination Prevention: A Special Relativistic Approach

This paper introduces a novel analogical framework for preventing large language model (LLM) hallucinations inspired by principles from Einstein's special relativity. The work proposes that hallucinations occur when token generation proceeds at excessive "semantic velocity," causing desynchronization from factual context, drawing an analogy to time dilation effects in physics. The paper presents a paradigm shift from uniform computational processing to adaptive temporal dynamics, where uncertainty acts as "semantic mass" that creates computational gravity wells. These wells automatically allocate more processing time to uncertain regions, creating self-regulating neural networks that naturally prevent rushed inference leading to fabricated content. Key contributions include: Analogical mathematical framework mapping relativistic concepts to LLM behavior Introduction of semantic velocity and temporal dilation concepts as analogies for neural networks Gravitational semantic field architecture with variable temporal allocation Theoretical foundation for adaptive computational spacetime analogies in AI systems The approach represents a fundamental reconceptualization of neural network architecture, suggesting that time itself can be a learnable and dynamic parameter. By allowing different computational regions to operate at variable temporal rates based on local uncertainty, the framework creates systems that naturally exhibit careful reasoning where it matters most. This interdisciplinary work bridges theoretical physics and artificial intelligence, offering a novel perspective on AI safety and hallucination prevention through temporal dynamics rather than traditional training approaches.Keywords: LLM Hallucinations, Special Relativity, Time Dilation, Semantic Velocity, Neural Networks, Computational Spacetime, Uncertainty Quantification, Adaptive Processing, AI Safety, Temporal Dynamics, Einstein Field Equations, Gravitational Fields, Machine Learning, Natural Language Processing, Transformer Architecture.