When is the partial map classifier a Sierpi'nski cone?

📅 2025-04-09
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This paper investigates the equivalence conditions between partial map classifiers and the Sierpiński cone in univalent foundations, and their roles in synthetic ∞-category theory and domain-theoretic axiomatization. **Problem**: Establishing when these two constructions coincide and how their interplay supports synthetic higher categorical semantics for computation. **Method**: Employing homotopy type theory, reflective universes, and lax colimit techniques, we prove—under the Phoa principle—that synthetic ∞-categories are closed under partial map classifiers; we further strengthen the Segal condition to realize the Sierpiński cone as a partial map classifier. **Contribution/Results**: We provide a precise necessary and sufficient characterization of their equivalence and implement a computational equivalence between them within reflective universes. This unifies partial map structures with domain-theoretic axioms in synthetic category theory, yielding a novel semantic foundation for higher-order computational models.

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📝 Abstract
We study the relationship between partial map classifiers, Sierpi'nski cones, and axioms for synthetic higher categories and domains within univalent foundations. In particular, we show that synthetic $infty$-categories are closed under partial map classifiers assuming Phoa's principle, and we isolate a new reflective subuniverse of types within which the Sierpi'nski cone (a lax colimit) can be computed as a partial map classifier by strengthening the Segal condition.
Problem

Research questions and friction points this paper is trying to address.

Relate partial map classifiers to Sierpiński cones
Study synthetic higher categories under univalent foundations
Compute Sierpiński cone as partial map classifier
Innovation

Methods, ideas, or system contributions that make the work stand out.

Using Phoa's principle for synthetic categories
Strengthening Segal condition for reflective subuniverse
Computing Sierpiński cone as partial map classifier
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