Evans' Law: A Predictive Threshold for Long-Context Accuracy Collapse in Large Language Models

Language models exhibit consistent performance decay as input and output lengths increase. This paper presents Evans' Law, defining the relationship between context length and accuracy degradation. Initial experimental validation confirms the phenomenon exists and provides empirical data to refine the mathematical formulation. Evans' Law: The likelihood of errors rises super-linearly with prompt and output length until accuracy falls below 50%, following a power-law relationship determined by model capacity and task complexity