Salient Causal Events: Toward Sparse Physical Intelligence and Causal Attention

Current artificial intelligence systems rely predominantly on dense continuous processing,requiring large computational resources to model environments, language, and multimodal in-teraction.This paper proposes the Salient Causal Event (SCE) hypothesis: intelligent systems mayoperate more efficiently when computational resources are allocated around sparse causallyrelevant transitions rather than continuous full-state reconstruction.An SCE is defined conceptually as a transition that significantly alters the future trajectoryof a system. Instead of continuously modeling all variables, an intelligent agent may focuson events such as collisions, velocity inversions, semantic negations, abrupt contact changes, orirreversible state transitions. Under this view, physical reality itself acts as a natural mechanismof information compression.The paper introduces a dual-process architectural hypothesis in which low-cost monitor-ing layers detect candidate transitions using inexpensive proxies, while deeper computationalresources are activated only during salient causal moments.The framework is discussed in relation to event-based sensing, Event Segmentation Theory,Active Inference, sparse Transformer architectures, and multimodal grounding.The paper additionally proposes the concept of Semantic Salient Causal Events (S-SCEs),extending sparse causal processing into language models. Tokens associated with semanticinversions or large predictive shifts may function analogously to physical causal discontinuities.Finally, several empirical predictions are proposed, including measurable attention redistri-bution around semantic transitions in pretrained Transformers and improved efficiency in sparseevent-triggered multimodal systems.This work is presented as a speculative architectural hypothesis and conceptual positionpaper intended to motivate future experimental research in efficient embodied and multimodalintelligence.