SOURCE-LINKED INTELLIGENCE
Agentic ML Exploration (A-MLE) for Ads Ranking
Modern industrial ads ranking stacks are increasingly bottlenecked not by model capacity or training compute, but by the throughput of human ML iteration - the cycles of research, implementation, training, debugging, evaluation, and launch required to surface a single statistically significant improvement. A typical ranking stack contains numerous differentiated models with heterogeneous data, architectures, and infrastructure constraints, and each cycle takes days to weeks of senior engineer attention per model. As a result, techniques that have proven effective on one model diffuse into othe
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-08T04:48:03.000Z
First collected: 2026-09-20T20:22:01.598Z. This is not the publication date.