A multi-layer ELT data quality framework using LLM semantic tests detected all 16 injected anomalies versus 7 in a manual baseline, with full cross-store agreement in 106 seconds.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
IEFF enables retrain-free feature efficiency rollouts in ranking systems by elastically controlling feature coverage at serving time, achieving 5x faster rollouts, zero retraining GPU cost, and 50-55% less performance degradation than abrupt feature removal.
citing papers explorer
-
A Multi-Layer Testing Framework for Automated Data Quality Assurance in Cloud-Native ELT Pipelines
A multi-layer ELT data quality framework using LLM semantic tests detected all 16 injected anomalies versus 7 in a manual baseline, with full cross-store agreement in 106 seconds.
-
Intelligent Elastic Feature Fading: Enabling Model Retrain-Free Feature Efficiency Rollouts at Scale
IEFF enables retrain-free feature efficiency rollouts in ranking systems by elastically controlling feature coverage at serving time, achieving 5x faster rollouts, zero retraining GPU cost, and 50-55% less performance degradation than abrupt feature removal.