The field is moving towards exploring innovative approaches to improve the performance and expressiveness of AI models, particularly in areas such as compositional generalization and semantic memory. Quantum models are being investigated for their potential to improve training efficiency and overcome the limitations of classical models. Additionally, wave-based approaches are being proposed as an alternative to traditional vector-based methods, allowing for more robust and expressive representations of knowledge and semantic similarity. Noteworthy papers include: Compositional Concept Generalization with Variational Quantum Circuits, which demonstrates the potential of quantum models in compositional generalization tasks. Wave-Based Semantic Memory with Resonance-Based Retrieval, which introduces a novel framework for modeling knowledge as wave patterns and retrieves it through resonance-based interference. Back to Ear: Perceptually Driven High Fidelity Music Reconstruction, which proposes a new VAE training paradigm that prioritizes auditory perceptual aspects, resulting in improved phase accuracy and stereophonic spatial representation. Exploring How Audio Effects Alter Emotion with Foundation Models, which leverages foundation models to analyze the impact of audio effects on emotion, offering insights into the complex relationships between sound design techniques and affective perception.