Predict recall progression curves per query before searching, then dynamically adjust search depth based on predictions—this eliminates expensive parameter tuning and achieves target recall with 80% fewer data accesses.
QASP predicts how recall improves as vector search explores more data, then uses that prediction to set search depth for any target recall level. Instead of tuning fixed parameters per dataset or running multiple searches, it learns once upfront and adapts per query, reducing computational cost while maintaining consistent quality across different queries.