According to @godofprompt, recent research challenges the view that attention mechanisms are essential in AI models. The paper cited (arxiv.org/abs/2512.19428) demonstrates that what is fundamentally required is not attention itself, but a sufficiently expressive geometric evolution mechanism for hidden representations. This signals the beginning of a new era in AI architecture design, where researchers are encouraged to explore geometric alternatives to traditional attention, potentially leading to more efficient and innovative neural network architectures. As reported by @godofprompt, this development opens significant opportunities for advancing AI models beyond current attention-based methods.