arXiv:2610.20764v1 Announce Type: new Abstract: 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 ($S_C^{\mathrm{eff}}$), 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 $S_C^{\mathrm{eff}}$ than the baselines, motivating $S_C^{\mathrm{eff}}$ as a new benchmark and the scaling of coherent cognition as a principle for reliable AI.
TurboMamba: Faster Inference, Faster Training, and Better Prediction with Lexical Memory