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GRM Base-t Rebuild: Yang–Mills Existence and Mass Gap

GRM Base-t Rebuild: Yang–Mills Existence and Mass Gap Subtitle Reconstructing a Scoped, Reopenable Source Organization Before Testing Quantum Yang–Mills Representability, Confinement, Poincaré Dynamics, Spectral Structure, and the Hamiltonian Mass Gap Part I — Reconstructing the Problem Without Allowing the Target to Construct the Source 1. Separating the Requested Yang–Mills Theorem From the Source Organization That Could Support It 1.1 Treating Conventional Yang–Mills Theory as a Downstream Mathematical Representation Rather Than the Primitive Physical Starting Point 1.2 Separating Mathematical Existence of a Yang–Mills Representation From Physical Existence of the Source Organization Being Represented 1.3 Separating the Requested Positive Mass Gap From Any Upstream Criterion Used to Select Source Constructors 1.4 Preserving the Requested Theorem as an Obligation Interface Without Granting It Ontological Authority 1.5 Distinguishing Target Erasure From Target Amnesia So That Removing...
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GRM × BASE-t Rebuild of the Source-Level Physical Dynamics Underlying Electromagnetic and Quantum Phenomena

  GRM × BASE-t Rebuild of the Source-Level Physical Dynamics Underlying Electromagnetic and Quantum Phenomena Table of Contents Part I — Rebuild Contract 1.  Rebuild Target and Scope 1.1 Physical phenomena precede explanatory representation 1.2 Source dynamics versus mathematical description 1.3 Why successful prediction does not determine ontology 1.4 Why GRM does not explain QED as an object 1.5 Why GRM does not replace QM, QED, or the Standard Model 1.6 Phenomenon → source constraint → reconstruction → representation 1.7 Observable agreement as downstream validation 1.8 Source reconstruction as the primary task 1.9 Representation as a compression of source dynamics 1.10 The rebuild target 1.11 The minimum-source principle 1.12 The erasure-survival principle 1.13 Local success versus global source authority 1.14 Representation singularity versus source singularity 1.15 Completion and frontier criteria 2. What Counts as a Phenomenal Constraint 2.1 Repeatable physical conseque...
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Implementing AI

IMPLEMENTING AI Table of Contents Capability, Institutions, Infrastructure, Evidence, and Power Preface — Implementation Is a Coupled Selection Problem AI is not implemented by deploying a model. It is implemented by coupling a computational architecture to evidence, tools, infrastructure, institutions, capital, authority, physical resources, and consequences. Those elements interact without becoming interchangeable. The governing distinctions are: CAPABILITY ≠ VALIDITY VALIDITY ≠ AUTHORITY AUTHORITY ≠ TRUTH INFRASTRUCTURE ≠ SOVEREIGNTY but also: SEMANTIC PROCESS ↔ DEPLOYMENT CONDITIONS A useful theory must preserve both facts at once. Deployment cannot define truth. Deployment can determine what evidence is reachable, what validation is affordable, which actions are possible, and which architectures survive. PART I — THE IMPLEMENTED OBJECT 1. From Model to Operational Ecology The model ceases to be the unit of analysis Context, memory, retrieval, tools, search, external state, humans,...
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