Informational Filtrations, Global Dependencies, and the Structural Boundary of Deterministic Polynomial-Time Computation

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Authors: Michael Arias
Cited by: 0

Abstract

This paper develops a structural characterization of deterministic polynomial-time computation based on informational filtrations induced by execution. Building on earlier work showing that polynomial-time algorithms cannot eliminate global inconsistency at an exponential informational cost, it defines compatibility complexes and associates to each computation a monotone filtration reflecting progressive refinement of compatible inputs. The Informational/Filtrational Principle is proved, showing that such filtrations admit only locally certified changes and cannot create higher-dimensional global dependencies. Using this framework, the paper formalizes Global Non-Local Irreducible Dependencies and proves that they are incompatible with polynomial-time computation and preserved under standard reductions. As a consequence, the P versus NP problem is reduced to the question of whether NP-complete problems necessarily contain such dependencies.

Fields of Study & Subjects

Complexity and Algorithms in Graphs Computability Logic AI Algorithms Advanced Graph Theory Research

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
4 February 2026
Language
EN
Document type
Preprint