Oriented Border Recursion (ROBΩ). Minimal Kinematics of Ω and Construction of the Border Transducer

A derivative annex to the Ω framework. It adds no axioms, no Q, no limits, and no ontology to the underlying Volumes; it characterizes which variations are readable on the border of a constrained system without producing indistinction or an impossible border. The kinematics is stated as a decomposition of status, σ ⇒ ω: σ is the component of the border–limit relation, ω the component intrinsic to the border, read as the oriented structure of the traversable residue A_S(B) = A(B) \\ [A₁(B) ∪ A_Ø(B)]. The document formulates three kinematic NOs, a 3–2–1 reading of the limit (three static limit states, two dynamic forms, one oriented relation), and a non-arbitrary rule for constructing the border transducer T_B ≃ (N_{B|L}, O_{A_S(B)}), canonical only up to monotone transformations that preserve the border and subject to seven admissibility constraints (boundary-first, outcome-blind, minimality, separability, representation invariance, S/1/Ø fidelity, falsifiability). Available checks are summarized, the balanced distribution of the fifth component across the 36 canonical Q signatures, cross-domain cases, and Maxwell's equations as an operator-level continuous test, together with the v3 extension on dynamic completeness (Living Dance Floor, M3 audit). Formulas marked as candidate or kinematic hypothesis are internal derivations to be tested; falsification conditions and open problems are stated explicitly.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-18
DOI
https://doi.org/10.5281/zenodo.22835103
Primary Topic
Model Reduction and Neural Networks
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
preprint

Oriented Border Recursion (ROBΩ). Minimal Kinematics of Ω and Construction of the Border Transducer

davide lugli
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

Oriented Border Recursion (ROBΩ). Minimal Kinematics of Ω and Construction of the Border Transducer

davide lugli
preprint en

Abstract

A derivative annex to the Ω framework. It adds no axioms, no Q, no limits, and no ontology to the underlying Volumes; it characterizes which variations are readable on the border of a constrained system without producing indistinction or an impossible border. The kinematics is stated as a decomposition of status, σ ⇒ ω: σ is the component of the border–limit relation, ω the component intrinsic to the border, read as the oriented structure of the traversable residue A_S(B) = A(B) \ [A₁(B) ∪ A_Ø(B)]. The document formulates three kinematic NOs, a 3–2–1 reading of the limit (three static limit states, two dynamic forms, one oriented relation), and a non-arbitrary rule for constructing the border transducer T_B ≃ (N_{B|L}, O_{A_S(B)}), canonical only up to monotone transformations that preserve the border and subject to seven admissibility constraints (boundary-first, outcome-blind, minimality, separability, representation invariance, S/1/Ø fidelity, falsifiability). Available checks are summarized, the balanced distribution of the fifth component across the 36 canonical Q signatures, cross-domain cases, and Maxwell's equations as an operator-level continuous test, together with the v3 extension on dynamic completeness (Living Dance Floor, M3 audit). Formulas marked as candidate or kinematic hypothesis are internal derivations to be tested; falsification conditions and open problems are stated explicitly.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Model Reduction and Neural Networks
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.