新闻
Long Text to Predictive Features: LLM-Guided Blockwise Feature Engineering via Executable Program Search
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- Researchers developed LLM-BlockFE, a framework that converts long text into executable feature programs offline, eliminating runtime LLM calls for risk-control predictions.
- 为何重要
- Matters for engineers building production risk systems that need to extract features from unstructured text without incurring per-request LLM inference costs.
- 注意
- Results shown on two public and two private datasets; real-world deployment details and generalization to non-financial domains remain unclear from this summary.
- llm
- language model
这条新闻背后的模式
- Process Reward Models & Verifier-Guided Search
- Eval-Driven Development (Agent CI)
- Agentic Context Engineering (Evolving Playbook)
每个模式都讲清楚技术如何运作、何时值得投入，以及在哪里会失效。
The Agent Architect
每周一个模式、一个权衡、一个生产事故案例。为构建智能体系统的人准备的每周简报。
每周一封邮件,一键退订。您的地址仅用于发送简报。