Published December 30, 2025 | Version 1.0
Preprint Open

Boundary-Augmented Phase–Scalar Reconstruction (PSR-B): A Diagnostic Audit Protocol for Dissolving Physics Contradictions

  • 1. Independent Researcher, danceScape, Burlington, Ontario, Canada

Description

Persistent contradictions across physics—spanning thermodynamics, quantum foundations, relativity, and cosmology—are often interpreted as indicators of incomplete or inconsistent physical theory. Building on Phase–Scalar Reconstruction (PSR), which diagnoses such contradictions as representational mismatches between scalar descriptions (magnitude, duration, accumulation) and phase descriptions (relational structure, boundary completion, state locking), this work introduces Boundary-Augmented Phase–Scalar Reconstruction (PSR-B).

PSR-B refines PSR by introducing explicit boundary axioms, a residual classification system (R0–R4), and a frozen audit rubric designed for reproducibility and inter-rater reliability. The framework is applied to a systematic audit of fifty canonical physics contradictions. In all cases, the apparent contradiction dissolves at the representational level once scalar and phase roles are properly separated. Remaining questions are localized as boundary-scoped empirical residuals rather than logical inconsistencies.

Results: All 50 cases exhibit representational dissolution. Residual distribution is as follows: R0 (64%, fully dissolved), R1–R2 (16%, boundary or regime clarification required), and R3–R4 (20%, open empirical mechanisms). No case retains a logical contradiction after reconstruction.

PSR-B is a methodological framework, not a physical theory. It does not propose new mechanisms, particles, or equations. Its contribution is clarifying which questions are ill-posed due to category collapse and which remain legitimate targets for empirical investigation.

This work builds on Phase–Scalar Reconstruction (PSR) introduced in Tang (2024), DOI: 10.5281/zenodo.18088686, and forms part of the broader Human–AI Collaborative Research (HAICR) methodological research program.

Files included in this record:
(1) Main paper (PSR-B): framework, boundary axioms, frozen audit rubric, and results summary.
(2) Supplementary Appendix A: the complete set of standardized audit cards for all fifty cases, provided to enable independent replication and validation.

Files

Boundary-Augmented_Phase-Scalar_Reconstruction_PSR-B_Physics_Diagnostic_Audit_Tang_2025.pdf

Additional details

Related works

Is supplemented by
Preprint: 10.5281/zenodo.18088686 (DOI)

Dates

Issued
2025-12-30
Persistent contradictions across physics—spanning thermodynamics, quantum foundations, relativity, and cosmology—are often interpreted as indicators of incomplete or inconsistent physical theory. Building on Phase–Scalar Reconstruction (PSR), which diagnoses such contradictions as representational mismatches between scalar descriptions (magnitude, duration, accumulation) and phase descriptions (relational structure, boundary completion, state locking), this work introduces Boundary-Augmented Phase–Scalar Reconstruction (PSR-B). PSR-B refines PSR by introducing explicit boundary axioms, a residual classification system (R0–R4), and a frozen audit rubric designed for reproducibility and inter-rater reliability. The framework is applied to a systematic audit of fifty canonical physics contradictions. In all cases, the apparent contradiction dissolves at the representational level once scalar and phase roles are properly separated. Remaining questions are localized as boundary-scoped empirical residuals rather than logical inconsistencies. Results: All 50 cases exhibit representational dissolution. Residual distribution is as follows: R0 (64%, fully dissolved), R1–R2 (16%, boundary or regime clarification required), and R3–R4 (20%, open empirical mechanisms). No case retains a logical contradiction after reconstruction. PSR-B is a methodological framework, not a physical theory. It does not propose new mechanisms, particles, or equations. Its contribution is clarifying which questions are ill-posed due to category collapse and which remain legitimate targets for empirical investigation. This work builds on Phase–Scalar Reconstruction (PSR) introduced in Tang (2024), DOI: 10.5281/zenodo.18088686, and forms part of the broader Human–AI Collaborative Research (HAICR) methodological research program. Files included in this record: (1) Main paper (PSR-B): framework, boundary axioms, frozen audit rubric, and results summary. (2) Supplementary Appendix A: the complete set of standardized audit cards for all fifty cases, provided to enable independent replication and validation.

References