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 introduce Embedding-Aware Feature Discovery (EAFD), a unified framework that bridges this gap by coupling pretrained event-sequence embeddings with a self-reflective LLM-driven feature generation agent. EAFD iteratively discovers, evaluates, and refines features directly from raw event sequences using two complementary criteria: \emph{alignment}, which explains information already encoded in embeddings, and \emph{complementarity}, which identifies predictive signals missing from them. Across both open-source and industrial transaction benchmarks, EAFD consistently outperforms embedding-only and feature-based baselines, achieving relative gains of up to $+5.8\%$ over state-of-the-art pretrained embeddings, resulting in new state-of-the-art performance across event-sequence datasets.

翻译：工业金融系统运行于交易、用户行为和系统日志等时序事件序列之上。尽管近期研究强调表示学习与大语言模型，但由于可解释性、有限监督下的鲁棒性以及严格的延迟约束，生产系统仍严重依赖人工构建的统计特征。这导致学习得到的嵌入表示与基于特征的流程之间存在持续脱节。本文提出嵌入感知特征发现（EAFD），这是一个通过将预训练的事件序列嵌入与自反思的LLM驱动特征生成智能体相耦合来弥合这一鸿沟的统一框架。EAFD利用两个互补准则直接从原始事件序列中迭代地发现、评估和优化特征：\emph{对齐性}——解释嵌入中已编码的信息，以及\emph{互补性}——识别嵌入中缺失的预测信号。在开源与工业交易基准测试中，EAFD始终优于纯嵌入方法与基于特征的基线，相比最先进的预训练嵌入实现了高达$+5.8\%$的相对性能提升，从而在事件序列数据集上创造了新的最优性能记录。