OPSEC: minimize author name (remove surname)

README.md

@@ -14,7 +14,7 @@ base_model:
  
 **A Referential Introduction to the Measure-Theoretic Framework for Singular Analysis and Structure-Aware Machine Learning**
  
-Roy S. Colca Jr.
+Roy C
 *Convergent Intelligence LLC: Research Division*
 [convergentintel.com](https://convergentintel.com)
  
@@ -24,7 +24,7 @@ March 2026
  
 ## Abstract
  
-We present the core definitions, axioms, and principal theorems of Discrepancy Calculus (DISC) — a measure-theoretic framework that treats singularities as primary mathematical structure rather than pathology. The central object is the *discrepancy operator*, which quantifies the mismatch between integration and differentiation on metric-measure spaces; classical calculus is recovered as a degenerate smooth limit. We state the eight axioms of DISC, prove the Mesh Fundamental Identity (the DISC replacement for the Fundamental Theorem of Calculus), introduce the counter-derivative construction that unfolds singular calculus into ordinary analysis, and establish three key results: (i) the Classical Shadow theorem showing exact recovery of Newton/Lagrange/Hamilton in smooth regimes, (ii) the DISC Incompleteness theorem proving classical Sobolev spaces cannot extend to gap-rich domains, and (iii) the Meta-Discrepancy theorem establishing a fundamental impossibility — when gap measure and discrepancy energy are both positive, the classical derivative/FTC/MVT package cannot hold on any set of positive measure. We demonstrate operational deployment across 49 published models on HuggingFace (22,500+ downloads) via Topological Knowledge Distillation, which uses the BV decomposition to preserve structural information that standard knowledge distillation provably destroys. The full proof apparatus, together with extensions to graph structures, quantum mechanics, and unified field theory, is developed in the companion monograph *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026).
+We present the core definitions, axioms, and principal theorems of Discrepancy Calculus (DISC) — a measure-theoretic framework that treats singularities as primary mathematical structure rather than pathology. The central object is the *discrepancy operator*, which quantifies the mismatch between integration and differentiation on metric-measure spaces; classical calculus is recovered as a degenerate smooth limit. We state the eight axioms of DISC, prove the Mesh Fundamental Identity (the DISC replacement for the Fundamental Theorem of Calculus), introduce the counter-derivative construction that unfolds singular calculus into ordinary analysis, and establish three key results: (i) the Classical Shadow theorem showing exact recovery of Newton/Lagrange/Hamilton in smooth regimes, (ii) the DISC Incompleteness theorem proving classical Sobolev spaces cannot extend to gap-rich domains, and (iii) the Meta-Discrepancy theorem establishing a fundamental impossibility — when gap measure and discrepancy energy are both positive, the classical derivative/FTC/MVT package cannot hold on any set of positive measure. We demonstrate operational deployment across 49 published models on HuggingFace (22,500+ downloads) via Topological Knowledge Distillation, which uses the BV decomposition to preserve structural information that standard knowledge distillation provably destroys. The full proof apparatus, together with extensions to graph structures, quantum mechanics, and unified field theory, is developed in the companion monograph *"On the Formal Analysis of Discrepancy Calculus"* (CIx, 2026).
  
 ---
  
@@ -61,7 +61,7 @@ whenever the limit exists (possibly $+\infty$). If $f$ is $C^1$, then $Df(x) = |
  
 This paper presents the **core analytical foundations** of DISC: the axioms, principal theorems, and one application domain (machine learning). The full proof apparatus (203 pages, 41 chapters), together with extensions to graph structures (Part II), quantum mechanics (Part III), and unified field theory including the Theory of Other (Part IV), is developed in the companion monograph:
  
-> R. S. Colca Jr., *"On the Formal Analysis of Discrepancy Calculus: A Measure-Theoretic and Symbolic Framework for Singular Structures and Stability,"* Convergent Intelligence LLC: Research Division, March 2026.
+> Convergent Intelligence LLC, *"On the Formal Analysis of Discrepancy Calculus: A Measure-Theoretic and Symbolic Framework for Singular Structures and Stability,"* Convergent Intelligence LLC: Research Division, March 2026.
  
 All theorem numbering in this paper matches the monograph for cross-referencing.
  
@@ -309,11 +309,11 @@ The full theory — including graph-theoretic extensions (Measure-Theoretic Hami
  
 ## References
  
-1. R. S. Colca Jr., *"On the Formal Analysis of Discrepancy Calculus: A Measure-Theoretic and Symbolic Framework for Singular Structures and Stability,"* Convergent Intelligence LLC: Research Division, March 2026.
+1. Convergent Intelligence LLC, *"On the Formal Analysis of Discrepancy Calculus: A Measure-Theoretic and Symbolic Framework for Singular Structures and Stability,"* Convergent Intelligence LLC: Research Division, March 2026.
  
-2. R. S. Colca Jr., "Structure Over Scale: CPU-Native Training of Sparse Cognitive Architectures at $1.60 Per Model," HuggingFace, DOI: [10.57967/hf/8165](https://doi.org/10.57967/hf/8165), March 2026.
+2. Convergent Intelligence LLC, "Structure Over Scale: CPU-Native Training of Sparse Cognitive Architectures at $1.60 Per Model," HuggingFace, DOI: [10.57967/hf/8165](https://doi.org/10.57967/hf/8165), March 2026.
  
-3. R. S. Colca Jr., "From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale," HuggingFace, DOI: [10.57967/hf/8184](https://doi.org/10.57967/hf/8184), March 2026.
+3. Convergent Intelligence LLC, "From Three Teachers to Dual Cognition: Topology-Aware Multi-Teacher Distillation and Role-Conditioned Self-Critique at 1.7B Scale," HuggingFace, DOI: [10.57967/hf/8184](https://doi.org/10.57967/hf/8184), March 2026.
  
 4. G. Hinton, O. Vinyals, and J. Dean, "Distilling the Knowledge in a Neural Network," *NIPS Deep Learning Workshop*, 2015.