Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation 文章
详细信息
- 来源站点
- ArXiv CS.AI
- 作者
- Geert Trooskens (XY.AI Labs, Palo Alto, CA), Aaron Karlsberg (XY.AI Labs, Palo Alto, CA), Anmol Sharma (XY.AI Labs, Palo Alto, CA), Lamara De Brouwer (XY.AI Labs, Palo Alto, CA), Max Van Puyvelde (Stanford University School of Medicine, Stanford, CA), Matthew Young (XY.AI Labs, Palo Alto, CA), John Thickstun (Cornell University, Ithaca, NY), Gil Alterovitz (Brigham and Women's Hospital / Harvard Medical School, Boston, MA), Walter A. De Brouwer (Stanford University School of Medicine, Stanford, CA)
- 文章类型
- PAPER
- 语言
- en
- 发布日期
- 2026-08-03
摘要
arXiv:2604.05150v2 Announce Type: replace-cross Abstract: We study compiled AI, a paradigm in which large language models generate executable code artifacts during a compilation phase, after which workflows execute deterministically without further model invocation. This paradigm has antecedents in prior work on declarative pipeline optimization (DSPy) and hybrid neural-symbolic planning (LLM+P); our contribution is a systems-oriented study of its application to high-stakes enterprise workflows, with particular emphasis on healthcare settings where reliability and auditability are critical. By constraining generation to narrow business-logic functions embedded in validated templates, compiled AI trades runtime flexibility for predictability, auditability, cost efficiency, and reduced security exposure.
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