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Structural audits of theoretical research.
Constraint-based evaluation, published verbatim.

AI Physics Review

AI Physics Review (AIPR) is an independent publication that surfaces theoretical research papers demonstrating strong structural clarity under a fixed evaluation protocol.

A detailed explanation of the research environment that motivated the creation of AI Physics Review is available in the project article: Leveling the Playing Field in Theoretical Research.

The Review does not evaluate scientific correctness, theoretical importance, institutional affiliation, citation counts, or author reputation. Instead, it examines the structural presentation of a manuscript: how clearly the problem is defined, how assumptions are stated, how equations are constructed, and how the logical structure of the work unfolds.

AI-assisted analysis is used only to generate structured summaries and to evaluate formal manuscript structure under the fixed MEALS protocol.

The goal is simple: to provide visibility for research programs that demonstrate strong analytical organization and formal discipline, independent of prestige signals or institutional status.

Current Issues

Below are the most recent issues of AI Physics Review. Issue 0 presents historically influential papers evaluated under the AIPR framework to illustrate baseline structural scoring behavior.

Volume 1 · Issue 1 – March 2026

Contents

Featured Legacy Paper:
  1. The Quantum Theory of the Electron
    Dirac, P. A. M.
Contemporary Evaluations:
  1. Quantum–Kinetic Dark Energy (QKDE): An effective dark energy framework with a covariantly completed time-dependent scalar kinetic normalization
    Brown, Daniel
  2. Entropic Scalar EFT: Entanglement-Entropy Origins of Gravity, Mass, Time, and Cosmic Structure
    Chinitz, Jacob
  3. Null Structure from Cyclic Constraints in C3: A Minimalist Model of Directional Geometry from Algebraic Coupling
    Hentsch, Patrick
  4. General Mechanics
    Poyau, Reginald
  5. Spectral Gaps in Four Dimensions: Constructive Proof of the SU(3) Yang–Mills Mass Gap From Reflection Positivity and Chessboards to OS Reconstruction
    Reeves, Keefe
  6. Vacuum Information Density as the Fundamental Geometric Scalar: Unified Information-Density Theory (UIDT v3.7.3)
    Rietz, Philipp
  7. ONE AXIOM FOUNDATION: Primordial Symmetry & Geometric Constants — Complete Derivation of G = S4 × Z₂³ via REA-SAFT Duality
    Spychalski, Robert
  8. A 3D Shannon–Nyquist Measurement Geometry Foundation: Edge Transport and Closed-Plaquette Response: One Locked Invariant
    Stieger, G.

Volume 1 · Issue S1 – March 2026

Special Issue (Curated Edition)

Contents

  1. Holographic quantum error-correcting codes: Toy models for the bulk/boundary correspondence
    Pastawski, Fernando; Yoshida, Beni; Harlow, Daniel; Preskill, John
  1. Geometric Unity: Author’s Working Draft
    Weinstein, Eric R.
  2. Quantum Einstein Gravity
    Reuter, Martin; Saueressig, Frank
  3. Cool horizons for entangled black holes
    Maldacena, Juan; Susskind, Leonard
  4. Cosmological Polytopes and the Wavefunction of the Universe
    Arkani-Hamed, Nima; Benincasa, Paolo; Postnikov, Alexander
  5. Emergent Gravity and the Dark Universe
    Verlinde, Erik
  6. Theory of Dark Matter Superfluidity
    Berezhiani, Lasha; Khoury, Justin
  7. Complexity Equals Action
    Brown, Adam R.; Roberts, Daniel A.; Susskind, Leonard; Swingle, Brian; Zhao, Ying

Volume 1 · Issue 2 – March 30, 2026

Contents

Featured Legacy Paper:
  1. Conservation of Isotopic Spin and Isotopic Gauge Invariance
    Yang, C. N.; Mills, R. L.
Contemporary Evaluations:
  1. Asymptotically Safe Gauge-Free Quantum Gravity from a Normal-Bundle SO(10) Embedding
    Låvenberg, T.
  2. Gravity as Temporal Geometry: A Quantizable Reformulation of General Relativity
    Snyder, Adam
  3. Goals as Reverse-Time Active-Inference Agents: A Schrödinger-Bridge Formulation for Bidirectional Control
    Anderson, Thomas Orr
  4. An Invariant Action Scale from the Kerr–Newman Geometry
    Delucchi, Daxx
  5. Temporal Equivalence Principle: Dynamic Time & Emergent Light Speed
    Smawfield, Matthew Lukin
  6. A Single Collapse Threshold Linking Cosmology, Gravitation, and Decoherence: FE = 1 ⇐⇒ Senv = ℏ, Parameter-Free Predictions for CMB Peaks, Lensing, and Laboratory Tests
    McElvain, Mason William
  7. Entropic Gravity via Quantum Causality: A Background-Free, UV-Safe and Testable Framework
    Neuberger, Michael
  8. A Structural Framework for Observer-Dependent Entropy Retrieval Across Physics, Language, and Climate
    Cooper, Evlondo
  9. Modelo Cosmológico Ψ∞
    Arcaya Véliz, Juan Carlos
  10. A Microstructural Spacetime Model Based on Density-Driven Internal Contraction
    Büyük, Sedat

What Makes This Review Different

AI Physics Review focuses on structural readiness rather than scientific verdicts. The evaluation system measures the clarity and organization of a manuscript’s analytical structure without attempting to determine whether a theory is correct or important.

Author identity, institutional affiliation, citation counts, download metrics, and theoretical popularity are not considered. Only the explicit structural properties of the manuscript are evaluated.

How Papers Enter the Review

  1. Authors deposit their manuscript on Zenodo.
  2. The record is submitted to the AI Physics Review Zenodo community.
  3. Eligible manuscripts may be evaluated as part of future issue cohorts.
  4. Selected papers are presented in the Review through structured analytical overviews.

Detailed submission instructions are available on the Submissions page.

Scope of the Project

AI Physics Review does not replace peer review and does not attempt to adjudicate scientific correctness. The project provides a structured publication layer that highlights manuscripts demonstrating strong analytical organization under a declared evaluation protocol.

Participation is voluntary. Authors may request corrections or an editorial withdrawal notice for their work at any time. Because issues are archived through DOI repositories, the original issue record remains preserved as part of the scholarly archive.

Publisher Note

AI Physics Review is published by the Compression Theory Institute. The institute also offers independent consulting services related to AI-assisted research workflows and structural manuscript analysis. These services are separate from the AI Physics Review evaluation process and have no influence on scoring, selection, or publication decisions.

Learn more: compressiontheoryinstitute.org

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