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∞-Categorical Models of Linear Logic

  • Laboratoire d'Informatique (LIX)

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The notion of categorical model of linear logic is now well studied and established around the notion of linear-non-linear adjunction, which encompasses the earlier notions of Seely categories, Lafont categories and linear categories. These categorical structures have counterparts in the realm of ∞-categories, which can thus be thought of as weak forms of models of linear logic. The goal of this article is to formally introduce them and study their relationships. We show that ∞-linear-non-linear adjunctions still play the role of a unifying notion of model in this setting. Moreover, we provide a sufficient condition for a symmetric monoidal ∞-category to be Lafont. Finally, we illustrate our constructions by providing models: we construct linear-non-linear adjunctions that generalize well-known models in relations (and variants based on profunctors or spans), domains and vector spaces. In particular, we introduce a model based on spectra, a homotopical variant of abelian groups.

Original languageEnglish
Title of host publication10th International Conference on Formal Structures for Computation and Deduction, FSCD 2025
EditorsMaribel Fernandez
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959773744
DOIs
Publication statusPublished - 7 Jul 2025
Event10th International Conference on Formal Structures for Computation and Deduction, FSCD 2025 - Birmingham, United Kingdom
Duration: 14 Jul 202520 Jul 2025

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
Volume337
ISSN (Print)1868-8969

Conference

Conference10th International Conference on Formal Structures for Computation and Deduction, FSCD 2025
Country/TerritoryUnited Kingdom
CityBirmingham
Period14/07/2520/07/25

Keywords

  • linear logic
  • linear-non-linear adjunction
  • spectrum
  • ∞-category

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