一句话:证明基于无类型域模型的 NbE 也适用于非累积宇宙层级,并给出可靠、完备且机器化的基础。
归一化求值(NbE)基于无类型域模型,可将项归一化为 βη 范式,且便于机器化验证;但已有工作仅针对累积宇宙层级,其正确性证明依赖累积性。本文研究无类型 NbE 能否适用于 Agda、Lean 等证明助手广泛使用的非累积宇宙层级,并给出肯定回答。为忠实反映非累积性导致的类型唯一性,作者在 Martin-Löf 类型理论中显式标注宇宙层级,语义侧也显式管理层级,使语义比累积情形更复杂。作者证明该 NbE 算法可靠且完备;并表明显式层级标注在逻辑上冗余,去掉标注后 NbE 仍适用。最终为面向非累积性的 NbE 提供机器化基础。
Normalization by evaluation (NbE) based on an untyped domain model is a convenient and powerful way to normalize terms to their β η normal forms. It enables a concise technical setup and simplicity for mechanization. Nevertheless, to date, untyped NbE has only been studied for cumulative universe hierarchies, and its correctness proof critically relies on the cumulativity of the system. Therefore, we are faced with the question: whether untyped NbE applies to a non-cumulative universe hierarchy? Because such a universe hierarchy is also widely used by proof assistants like Agda and Lean, this question is of practical significance. Our work answers this question positively. One important property typically induced from non-cumulativity is uniqueness : every term has a unique type. To faithfully reflect the uniqueness property, we work with a Martin-Löf type theory with explicit universe levels ascribed in the syntactic judgments. On the semantic side, universe levels are also explicitly managed, which leads to more complex semantics compared with a cumulative universe hierarchy. We prove that the NbE algorithm is sound and complete, and confirm that NbE does work with non-cumulativity. Moreover, to better align with common practice, we also show that the explicit annotations of universe levels, though technically useful, are logically redundant: NbE remains applicable without these annotations. As such, we provide a mechanized foundation with NbE for non-cumulativity.
@article{paperbot3048,
title = {Normalization by Evaluation for Non-cumulativity},
author = {Shengyi Jiang and Jason Z. S. Hu and Bruno C. d. S. Oliveira},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {ICFP},
year = {2025},
doi = {10.1145/3747508}
}