Attention-based architectures transformed artificial intelligence by solving contextual token coordination at scale. Transformers demonstrated that global similarity scoring within a single forward pass could unlock unprecedented capability across language, vision, and multimodal systems.
But attention does not resolve a deeper architectural constraint.
As AI systems expand into robotics, continual learning, embodied interaction, and long-horizon planning, they must coordinate persistent, semi-autonomous processes with internal state. Token coordination is not process coordination. Stateless forward passes are not structural memory. External orchestration is not native architecture.
After Attention presents a structural argument: the next frontier lies in coordination between persistent modules operating under resource constraints.
The book introduces Resonant Modular Systems (RMS), a coordination-layer framework grounded in attractor dynamics, phase alignment, predictive surprise, and energy-regularized sparse coupling.
This is a forward architectural synthesis-precise enough to implement, structured enough to test, and constrained enough to refute.
For researchers, engineers, and technical strategists evaluating what comes after attention-based dominance.
