Profunctor Optics, a Categorical Update
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Abstract (arXiv)
Optics are bidirectional data accessors that capture data transformation patterns such as accessing subfields or iterating over containers. Profunctor optics are a particular choice of representation supporting modularity, meaning that we can construct accessors for complex structures by combining simpler ones. Profunctor optics have previously been studied only in an unenriched and non-mixed setting, in which both directions of access are modelled in the same category. However, functional programming languages are arguably better described by enriched categories; and we have found that some structures in the literature are actually mixed optics, with access directions modelled in different categories. Our work generalizes a classic result by Pastro and Street on Tambara theory and uses it to describe mixed V-enriched profunctor optics and to endow them with V-category structure. We provide some original families of optics and derivations, including an elementary one for traversals. Finally, we discuss a Haskell implementation.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Jan 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 0; 2024: 2; 2025: 0; 2026: 1.
6 papers.
- 2021Categorical Foundations of Gradient-Based Learningcited by 44
- 2024Reinforcement Learning in Categorical Cyberneticscited by 4
- 2024Deep Learning with Parametric Lensescited by 3
- 2022Compositional Active Inference II: Polynomial Dynamics. Approximate Inference Doctrinescited by 2
- 2022Space-time tradeoffs of lenses and optics via higher category theorycited by 2
- 2026Generalised Bellman recurrence and three dualities in sequential decision-makingcited by 0
- 2026Generalised Bellman recurrence and three dualities in sequential decision-making20 Jul 2026
- 2024Reinforcement Learning in Categorical Cybernetics3 Apr 2024
- 2024Deep Learning with Parametric Lenses30 Mar 2024
- 2022Space-time tradeoffs of lenses and optics via higher category theory19 Sep 2022
- 2022Compositional Active Inference II: Polynomial Dynamics. Approximate Inference Doctrines25 Aug 2022
- 2021Categorical Foundations of Gradient-Based Learning2 Mar 2021