# Subquadratic — How SSA Makes Long Context Practical

- Company: Subquadratic (subq.ai)
- Announced: 2026-05-05
- Category: not stated
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://subq.ai/how-ssa-makes-long-context-practical
- Record: https://forck.live/items/23945-how-ssa-makes-long-context-practical
- Subject: SubQ

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. …

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