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Published November 23, 2025 | Version v2
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PRH | Inter | 5.11 • A Category of Blur and the Grand Lemma

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We axiomatize blur-a tunable neighbourhood/relaxation-as a categorical construction. A blurred object is a diagram $\mathcal{B}_X: I \rightarrow \mathcal{C}$ indexed by blur scales with a reading map $\rho_X: \lim _I \mathcal{B}_X \rightarrow X$ (sharp limit) and exhaustion $\operatorname{colim}_I \mathcal{B}_X \simeq 1$. An uncertainty window is a lax retract pair $j_X^{\varepsilon}: X \rightarrow B_{\varepsilon} X, r_X^{\varepsilon}: B_{\varepsilon} X \rightarrow X$. We prove a property-transport principle: for properties stable under retracts and filtered limits and monotone for $\precsim, \mathrm{P}(X)$ holds iff $\mathrm{P}_{\varepsilon}\left(B_{\varepsilon} X\right)$ holds eventually. Blur-morphisms are natural transformations; the Grand Lemma shows sending sharp equals sending blurred then reading, yielding a simple string diagram calculus and graded (co)monad scale composition. Examples include analytic blur (Markov/convolution semigroups) and logical blur (quantale-enriched nuclei).

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References

  • F. W. Lawvere, Metric spaces, generalized logic, and closed categories, Rendiconti del Seminario Matematico e Fisico di Milano 43 (1973), 135–166.
  • G. M. Kelly, Basic Concepts of Enriched Category Theory, London Mathematical Society Lecture Note Series, vol. 64, Cambridge University Press, 1982.
  • S. Abramsky and A. Jung, Domain theory, in S. Abramsky, D. M. Gabbay, and T. S. E. Maibaum (eds.), Handbook of Logic in Computer Science, vol. 3, Oxford University Press, 1994, pp. 1–168.
  • C. Jones and G. D. Plotkin, A probabilistic powerdomain of evaluations, in Proceedings of the 4th IEEE Symposium on Logic in Computer Science (LICS), 1989, pp. 186–195.
  • M. Giry, A categorical approach to probability theory, in B. Banaschewski (ed.), Categorical Aspects of Topology and Analysis, Lecture Notes in Mathematics, vol. 915, Springer, 1982, pp. 68–85.
  • T. Fritz, A synthetic approach to Markov kernels, conditional independence, and theorems on sufficient statistics, Advances in Mathematics 370 (2020), 107239.
  • K. Weihrauch, Computable Analysis, Springer, 2000.
  • M. B. Pour-El and J. I. Richards, Computability in Analysis and Physics, Springer, 1989.
  • A. M. Turing, On computable numbers, with an application to the Entscheidungsproblem, Proceedings of the London Mathematical Society (2) 42 (1936), 230–265.
  • H. Weyl, Über die Gleichverteilung von Zahlen mod. Eins, Mathematische Annalen 77 (1916), 313–352.
  • W. Rudin, Real and Complex Analysis, 3rd ed., McGraw–Hill, 1987.
  • E. M. Stein and R. Shakarchi, Fourier Analysis: An Introduction, Princeton University Press, 2003.
  • A. Perišić, Blur Between Addition and Multiplication, Zenodo, 2025.
  • A. Perišić, Blur as a Universal Principle, Zenodo, 2025.