Self-improving data synthesis enables scalable adaptation of embedding models

AI agents require specialised retrieval across domains and tasks, but adapting embedding models is constrained by costly manual training-data design. Here we introduce SYNTRA, a self-improving agentic framework that turns a few retrieval examples into reusable synthesis instructions. Given an unlabe