LSA enables practical ultra-long context (1M+ tokens) by replacing expensive full attention with hierarchical cluster scoring, achieving 22.7x speedup in prefill and 4.6x in decode while preserving model accuracy.
This paper introduces Latent Sparse Attention (LSA), a method that makes attention efficient at ultra-long context lengths (1M+ tokens) by hierarchically clustering keys in a learned latent space. Instead of comparing queries to every key, queries score clusters of keys, reducing computation from quadratic to subquadratic while maintaining accuracy.