A Unified Proof That Travels: Transport-Ready Proofpacks Across P vs NP, Holography, and Kepler

Glowing 3D geometric structures overlaid on complex mathematical equations against a dark background.

A Unified Proof That Travels: Transport-Ready Proofpacks Across P vs NP, Holography, and Kepler

Mathine: Transport-Ready Proofpack Protocol Machine
Link: https://doi.org/10.5281/zenodo.18809224

Across mathematics and theoretical physics, high-impact claims often become socially stable long before their verification surfaces become portable. In high-throughput settings, solvers can generate candidate arguments, reductions, and computational checks faster than communities can replay and defend them under dispute.

This paper proposes a transport-first validation format: Protocol → Proofpacks. A protocol specifies promotability rules—declared regimes, admissible transforms, falsifiers, and verification budgets—while a proofpack is the portable unit of progress: a receipt-complete, replayable bundle whose status can travel across teams, tools, and time under the declared policy.

The unified schema makes non-closure legitimate and productive. Instead of forcing binary “proved / not proved,” the framework emits explicit HOLD ledgers with reason codes, so disagreements become localizable to missing receipts, regime mismatches, or budget limits rather than turning into narrative disputes.

The paper demonstrates the schema across three contrasting domains. In P vs NP, it uses closed-class baselines (2-SAT and Horn-SAT) to define what “closure without HOLD” looks like and to expose where transport breaks under reductions. In holography, it introduces regime-scoped Circle-of-Equivalence checks so duality claims become replayable under explicit limits. In the Kepler conjecture, it separates reduction closure from computation-dependent closure using coverage gates and toolchain trust-root gates.

The goal is not to “solve everything faster,” but to make legitimate understanding scale. By making promotability explicit and bundling progress as proofpacks, Learner Machines can distill cross-domain motifs and repair templates without inflating claims beyond what receipts support.

— © 2026 Rogério Figurelli. This article is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt this material for any purpose, even commercially, provided that appropriate credit is given to the author and the source. To explore more on this and other related topics and books, visit the author’s page (Amazon).