CCR 2.0: High-level Reasoning for Conditional Refinements
July 06, 2025 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Youngju Song, Minki Cho
arXiv ID
2507.04298
Category
cs.PL: Programming Languages
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
In recent years, great progress has been made in the field of formal verification for low-level systems. Many of them are based on one of two popular approaches: refinement or unary separation logic. These two approaches are very different in nature and offer complementary benefits in compositionality. Recently, to fuse these benefits into a single unified mechanism, a new approach called Conditional Contextual Refinement (CCR 1.0 for short) was proposed. In this paper, we advance CCR 1.0 and provide novel and intuitive reasoning principles, resulting in CCR 2.0. Achieving this goal was challenging due to non-trivial counterexamples which necessitated elegant changes to the model of CCR 1.0. On top of CCR 2.0, we show how to fuse the benefits of refinement, unary separation logic, and also relational separation logic. Our results are formalized in Rocq.
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