Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences

ArXi:2603.15713v1 Announce Type: cross Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes representation learning and large language models, production systems continue to rely heavily on handcrafted statistical features due to their interpretability, robustness under limited supervision, and strict latency constraints. This creates a persistent disconnect between learned embeddings and feature-based pipelines. We.