The field of machine learning is witnessing a significant shift towards more interpretable and spectral learning approaches. Researchers are moving away from traditional neural networks and exploring alternative frameworks that provide more transparency and efficiency. One of the key directions is the use of Kolmogorov-Arnold Networks (KANs), which have been shown to outperform traditional models in various tasks, including stock prediction, image classification, and natural language processing. Another area of focus is spectral learning, which operates entirely in the wavelet domain and eliminates the need for traditional neural layers. This approach has been demonstrated to be more efficient and effective in tasks such as denoising and token classification. The use of Hilbert spaces and operator-based machine intelligence is also gaining traction, providing a more rigorous mathematical formulation of learning tasks and highlighting the advantages of spectral learning and symbolic reasoning. Noteworthy papers in this area include: KASPER, which introduces a novel framework for stock prediction and explainable regimes, achieving state-of-the-art results on real-world financial time series. Wavelet Logic Machines, which presents a fully spectral learning framework that eliminates traditional neural layers and achieves competitive performance on synthetic 3D denoising and natural language tasks. Scientific Machine Learning with Kolmogorov-Arnold Networks, which reviews recent progress in KAN-based models and highlights their advantages in capturing complex dynamics and learning more effectively.