LVLMs' Attention and FFN Roles Decoupled via Information Theory
A recent paper published on arXiv (2605.05668) introduces a cohesive framework rooted in information theory and geometry for examining the internal components of large vision-language models (LVLMs). This framework uncovers a functional separation: attention layers function as operators that preserve subspaces, concentrating on reconfiguration, whereas feed-forward networks (FFNs) act as operators that expand subspaces, facilitating semantic advancement. Experimental results indicate that substituting learned attention weights leads to a decline in performance, underscoring the importance of attention. This research tackles the absence of a theoretical foundation in previous attribution techniques, providing valuable insights for optimizing architectures.
Key facts
- Paper arXiv:2605.05668
- Proposes unified framework based on information theory and geometry
- Attention acts as subspace-preserving operator for reconfiguration
- FFNs act as subspace-expanding operators for semantic innovation
- Replacing learned attention weights degrades performance
- Decoder backbone is residual-connection Transformer
- Prior statistical approaches lacked unified theoretical basis
- Framework quantifies geometric and entropic nature of residual updates
Entities
Institutions
- arXiv