Long Context's Softmax Denominator Problem: Attention Dilution and Sparse Fixes
CShorten30 · x · 2026-10-02
A Weaviate podcast clip breaks down why long context windows hit a math problem in the softmax denominator: it sums over every token while the numerator for the relevant token stays fixed, flattening attention scores until the right document barely stands out. Two mitigations are discussed: BlockSearch uses length-aware softmax scaling and drops low-scoring documents before attention runs, while REFRAG lets the decoder read pre-computed chunk embeddings and expand only the chunks a lightweight policy deems necessary. The episode also covers the state of sparse attention and synergies with vector databases.
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