Continuity Models: Beyond language and spatial intelligence
Multimodal AI can now render and describe with astonishing fidelity, yet it still fails to preserve continuity of reasoning across time. This research thesis introduces Continuity Models as Rkive's direction for reliable long-horizon multimodal reasoning in production.
This research article argues that the core frontier in AI is not only bigger context windows, more frames, or richer world models, but explicit continuity: temporal, logical, and operational. It maps current limitations across language, video, spatial intelligence, and representation-first approaches; explains why more capacity is not the same as continuity; and defines Rkive's Continuity Models paradigm, where temporal event structure becomes first-class and measurable in production. The thesis emphasizes structured temporal representations, deterministic execution, interface-level comparability, and learning loops grounded in real human decisions.
