How SSA Makes Long Context Practical

Updated with recent third-party validated benchmarks from source Note: In this paper we share third-party verified benchmarks.

• A comprehensive model card is coming soon!
• SubQ is built around SSA, Subquadratic Sparse Attention, a linearly scaling attention mechanism designed for long-context retrieval, reasoning, and software engineering workloads.
• The core claim is simple: the hard problems enterprise AI needs to solve are long-context problems.
• Codebase, contracts, enterprise corpora, databases, spreadsheets, research corpora, and long-running agent sessions rarely fail because the answer is absent.
• They fail because the relevant evidence is distributed across a large body of context, referenced indirectly, and meaningful only when multiple pieces are held in view at once.
• Dense attention made modern language models possible, but it also made long context expensive.
• Every token compares against every other token, so attention grows quadratically with sequence length.
• SSA changes that scaling behavior.
• …