The agentic system with teacher-bandit planning and distillation to a student model reduces latency by 23% while achieving 89% plan replication accuracy and 15x faster inference on NYC Taxi and IMDB datasets.
Liu et al., ”Query optimization for vector databases in AI applica- tions,” inProc
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics
The agentic system with teacher-bandit planning and distillation to a student model reduces latency by 23% while achieving 89% plan replication accuracy and 15x faster inference on NYC Taxi and IMDB datasets.