Semantically Separating Nominal Wyvern for Usability and Decidability
July 05, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yu Xiang Zhu, Amos Robinson, Sophia Roshal, Timothy Mou, Julian Mackay, Jonathan Aldrich, Alex Potanin
arXiv ID
2507.03867
Category
cs.PL: Programming Languages
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The Dependent Object Types (DOT) calculus incorporates concepts from functional languages (e.g. modules) with traditional object-oriented features (e.g. objects, subtyping) to achieve greater expressivity (e.g. F-bounded polymorphism). However, this merger of paradigms comes at the cost of subtype decidability. Recent work on bringing decidability to DOT has either sacrificed expressiveness or ease of use. The unrestricted construction of recursive types and type bounds has made subtype decidability a much harder problem than in traditional object-oriented programming. Recognizing this, our paper introduces Nominal Wyvern, a DOT-like dependent type system that takes an alternative approach: instead of having a uniform structural syntax like DOT, Nominal Wyvern is designed around a "semantic separation" between the nominal declaration of recursive types on the one hand, and the structural refinement of those types when they are used on the other. This design naturally guides the user to avoid writing undecidably recursive structural types. From a technical standpoint, this separation also makes guaranteeing decidability possible by allowing for an intuitive adaptation of material/shape separation, a technique for achieving subtype decidability by separating types responsible for subtyping constraints from types that represent concrete data. The result is a type system with syntax and structure familiar to OOP users that achieves decidability without compromising the expressiveness of F-bounded polymorphism and module systems as they are used in practice.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted