When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence

This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system can no longer reliably perceive, reason, and act? We formulate this as a theory- and model-agnostic challenge in which systems must infer reliable actions for increasingly distant future states across a growing observation--reality gap. We operationalize this challenge through the effective Speed of Coherent Cognition (S C eff), defined as the highest tested Speed of Reality (S R) through which a system continuously satisfies a predefined behavioural reliability criterion from baseline. As a proof of concept, reinforcement-learning agents are trained at the baseline S R and evaluated, without retraining, as S R is increased by up to 50 times while computational resources remain fixed. We compare Transformer and gated-MLP baselines with Co 4, an architecture inspired by cellular mechanisms implicated in conscious processing and awake thought. Across the tested scenarios, Co 4 achieves substantially higher S C eff than the baselines, motivating S C eff as a new benchmark and the scaling of coherent cognition as a principle for reliable AI.