Pith. sign in

REVIEW 2 cited by

Higher Operads, Higher Categories

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv math/0305049 v1 pith:RKAFOI5M submitted 2003-05-02 math.CT math.AGmath.ATmath.QA

Tom Leinster This is my paper · ORCID
classification math.CTmath.AGmath.ATmath.QA
keywords categorieshigheroperadssubjectalgebraareasbookbraided
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Higher-dimensional category theory is the study of n-categories, operads, braided monoidal categories, and other such exotic structures. It draws its inspiration from areas as diverse as topology, quantum algebra, mathematical physics, logic, and theoretical computer science. This is the first book on the subject and lays its foundations. Many examples are given throughout. There is also an introductory chapter motivating the subject for topologists.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Logical relations for call-by-push-value models, via internal fibrations in a 2-category

    cs.LO 2025-05 conditional novelty 7.0 of 10

    A 2-categorical fibrational framework gives a uniform notion of logical relations for CBPV models, with a pullback theorem that constructs new relational models from old ones.

  2. Towards a Comparative Framework for Compositional AI Models

    cs.CL 2025-06 conditional novelty 5.0 of 10

    A categorical framework for compositional generalisation is applied to DisCoCirc models, showing quantum circuits outperform neural networks on systematicity while neural models overfit more.

Pith tools