Tactic introduces a novel approach to sparse attention that dynamically adapts to varying importance across different attention heads, layers, and contexts.

• Eliminates fixed token budgets by using clustering and distribution fitting to determine optimal sparsity patterns
• Provides a calibration-free solution that automatically adjusts to content importance
• Achieves better performance while reducing the computational load of KV caches
• Maintains high accuracy for long-context applications without manual tuning

This engineering breakthrough enables more efficient deployment of long-context LLMs in practical applications, reducing computational costs while preserving model quality for complex tasks.

Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs