Beacon Architecture: A New Approach to Attention in AI
research#transformer📝 Blog|Analyzed: Mar 12, 2026 07:15•
Published: Mar 12, 2026 07:08
•1 min read
•Qiita AIAnalysis
GhostDrift Research's Beacon architecture offers a fresh perspective on attention mechanisms in AI, moving from a 'mix-first' approach to a 'protect-then-select' design. The publicly released demo on GitHub provides a valuable resource for understanding this innovative approach. It's an exciting development in how we design and understand attention within neural networks!
Key Takeaways
- •Beacon reframes attention as a 'protect-then-select' process, unlike the common 'mix-first' approach.
- •The architecture consists of three layers: Transformer-style attention, MG-OS barrier, and GD-Attention selection.
- •The GitHub demo allows for direct comparison and understanding of the 'protect-then-select' structure.
Reference / Citation
View Original"Beacon is a proposal to reframe attention as a mechanism that protects necessary candidates conditionally and then selects them (protect-then-select)."