Naturality for higher-dimensional path types

We define a naturality construction for the operations of weak ω-categories, as a meta-operation in a dependent type theory. Our construction has a geometrical motivation as a local tensor product with a directed interval, and behaves logically as a globular analogue of Reynolds parametricity. Our construction operates as a "power tool" to support construction of terms with geometrical structure, and we use it to define composition operations for cylinders and cones in ω-categories. The machinery can generate terms of high complexity, and we have implemented our construction in a proof assistant, which verifies that the generated terms have the correct type. All our results can be exported to homotopy type theory, allowing the explicit computation of complex path type inhabitants.

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Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1109/lics65433.2025.00028
Citations
1
Primary Topic
Computational Geometry and Mesh Generation
Type
article
Field-Weighted Citation Impact
3.54
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article

Naturality for higher-dimensional path types

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1 citations
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article

Naturality for higher-dimensional path types

Jamie Vicary, Thibaut Benjamin, Chiara Sarti, Ioannis Markakis, Wilfred Offord
article en
1 citations

Abstract

We define a naturality construction for the operations of weak ω-categories, as a meta-operation in a dependent type theory. Our construction has a geometrical motivation as a local tensor product with a directed interval, and behaves logically as a globular analogue of Reynolds parametricity. Our construction operates as a "power tool" to support construction of terms with geometrical structure, and we use it to define composition operations for cylinders and cones in ω-categories. The machinery can generate terms of high complexity, and we have implemented our construction in a proof assistant, which verifies that the generated terms have the correct type. All our results can be exported to homotopy type theory, allowing the explicit computation of complex path type inhabitants.

University of Cambridge (GB)
Openalex Percentile: Top 6%
Computational Geometry and Mesh Generation
3.54
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