Sparse Attention as MIPS (Maximum Inner Product Search)
Source: https://x.com/teortaxestex Author: teortaxestex Date: 2025-2026
Summary
Insight: sparse attention is equivalent to approximate maximum inner product search (MIPS). Leading techniques like Hierarchical Navigable Small World (HNSW) graphs are already well-studied for MIPS. Suggests importing MIPS research into sparse attention design.
Key Claims
- Sparse attention = approximate MIPS (Maximum Inner Product Search)
- MIPS is a well-researched field with mature techniques (HNSW, LSH, etc.)
- Existing sparse attention research reinvents what MIPS already solved
- Suggested approach: apply HNSW and related techniques to attention directly
Concepts
- Scaling & Compute — efficient attention as scaling enabler
- Context Engineering — sparse attention enables longer effective context