让预训练嵌入自动发现可解释的事件特征，提升金融系统预测性能。
Embedding-Aware Feature Discovery: Bridging Latent Representations and Interpretable Features in Event Sequences
- 用大模型从原始事件流中迭代生成特征，结合对齐与互补性评估
- 在工业级交易数据上最高提升5.8%准确率，超越纯嵌入与手工特征方法
- 适合需要可解释性与高精度的金融风控、日志分析场景
工业金融系统依赖时间序列事件（如交易、用户操作、系统日志）运行。尽管近年研究聚焦表示学习与大语言模型，生产系统仍主要使用手工统计特征，因其具备可解释性、弱监督下的鲁棒性及严格的延迟约束。这导致学习到的嵌入与特征驱动流程之间存在持续脱节。本文提出嵌入感知特征发现框架（EAFD），通过将预训练事件序列嵌入与自省式大模型驱动的特征生成代理相结合，弥合这一差距。EAFD利用两个互补标准——对齐性（解释嵌入中已编码的信息）与互补性（识别嵌入中缺失的预测信号）——从原始事件序列中迭代发现、评估并优化特征。在开源与工业级交易基准上，EAFD持续优于仅用嵌入或仅用手工特征的基线，相对最先进的预训练嵌入最高提升5.8%，在多个事件序列数据集上达到新最优性能。
原文摘要 · Abstract (English)
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.
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