1) Can self-attention be reframed as a dynamic instance-specific feature selection mechanism, and how might this perspective inform the development of more interpretable or efficient Transformer models?

Self-attention can be seen as dynamic feature selection, where each token selects relevant tokens based on context-specific attention weights. This view emphasizes attention as choosing key features (tokens) for each position, adapting to the input. For interpretability, attention weights can highlight critical token interactions, aiding visualization and model explanation. For efficiency, focusing on high-weight tokens enables sparse attention, reducing O(n²) complexity to O(nk). This perspective inspires interpretable designs (e.g., binary attention weights) and efficient models (e.g., adaptive token selection), optimizing Transformers for long sequences and transparent decision-making.

2) Is it feasible to integrate multi-hop affinity propagation (as in Inf-FS) into attention mechanisms to capture deeper token interactions within a single layer, and what are the trade-offs?

Integrating multi-hop affinity propagation into self-attention is feasible by modeling token interactions as a graph and propagating affinities over multiple hops. This captures deeper dependencies in a single layer, potentially reducing model depth. Benefits include richer contextual representations and improved long-range dependency modeling. However, computational complexity increases (e.g., O(kn²) for k hops), and memory usage grows due to intermediate states. Over-propagation may dilute local context, impacting tasks needing direct relationships. Careful design, like sparse graphs or diffusion-based propagation, is needed to balance efficiency, representation quality, and training stability for practical implementation. for more information email: [email protected]

Yes, self-attention can be viewed as a dynamic feature selection mechanism. Self-attention computes attention weights that determine which input features (tokens/positions) are most relevant for each output position, effectively selecting and weighting different parts of the input sequence dynamically based on context. Unlike static feature selection that pre-determines relevant features, self-attention adapts its selection criteria for each query, allowing the model to focus on different input elements depending on what needs to be computed. The attention weights act as learnable, context-dependent gates that amplify important features while suppressing irrelevant ones, making it a sophisticated form of dynamic feature selection that operates at each layer and attention head independently.