Breaking AI's Context Ceiling: SubQ's 12M-Token Efficiency Breakthrough

Enterprise AI has struggled with a fundamental constraint: traditional attention scales quadratically with context, making it prohibitively expensive to reason over complete artifacts like codebases or documents. SubQ 1.1 Small's Subquadratic Sparse Attention solves this by delivering near-perfect accuracy at 12M tokens while using 64.5x less compute than dense attention. This efficiency breakthrough eliminates costly workarounds and enables enterprises to perform complex reasoning tasks at scale, fundamentally changing how organizations can leverage AI for document and code analysis.