Expand DISC Mathematical Foundations with formal axioms, update portfolio stats (49 models, 22,598 downloads)

README.md

@@ -226,13 +226,21 @@ inputs = tokenizer(prompt, return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=256)
 ```
 
-## Mathematical Foundation
+## Mathematical Foundations: Discrepancy Calculus (DISC)
 
-The metric attention mechanism is grounded in the Discrepancy Calculus (DISC), a measure-theoretic framework for singularity analysis developed by the author. The triangle inequality regularizer enforces that the learned attention geometry satisfies d(a,c) ≤ d(a,b) + d(b,c) across sampled triples, ensuring the distance function used for attention scoring is a proper metric — not merely a similarity function.
+The metric attention mechanism is grounded in Discrepancy Calculus — a measure-theoretic framework for singularity analysis developed by the author. The core operator:
 
-The ball pruning mechanism (learnable per-head origins and radii) creates adaptive sparse attention patterns that emerge from the geometry itself rather than from fixed masking heuristics.
+$$Df(x) := \limsup_{r \downarrow 0} \sup_{0 < d(x,y) < r} \frac{|f(y) - f(x)|}{d(x,y)}$$
 
-BlackHoleRoPE extends standard rotary position encoding with learned phase perturbations synthesized from a Fourier basis, maintaining the unitary property on Q/K while adding bounded amplitude modulation on V — ensuring position-dependent energy gating stays within Lyapunov-stable bounds.
+On metric-measure spaces (Axiom 11.1), this replaces the classical derivative with a scale-free local Lipschitz seminorm. The triangle inequality regularizer enforces that the learned attention geometry satisfies $d(a,c) \leq d(a,b) + d(b,c)$ across sampled triples — ensuring the distance function is a proper metric, not merely a similarity function.
+
+The **ball pruning mechanism** (learnable per-head origins and radii) creates adaptive sparse attention patterns that emerge from **gap geometry** (Axiom 11.4): the gap set $\Delta(E) = \{x : \theta^*_E(x) > \theta_{E*}(x)\}$ defines where attention should be sparse. Tokens outside the ball are in the gap — structurally irrelevant to the current computation.
+
+**BlackHoleRoPE** extends standard rotary position encoding with learned phase perturbations from a Fourier basis, maintaining the unitary property on Q/K while adding bounded amplitude modulation on V. The boundedness condition is the DISC **discrepancy energy** criterion: $E_{\text{disc}} < \infty$ ensures Lyapunov stability.
+
+**L2-star discrepancy** for thermodynamic governance measures training dynamics as a discrepancy field — gradient magnitude distributions analyzed through the same measure-theoretic lens that DISC applies to function spaces.
+
+Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165).
 
 ## Lineage
 
@@ -291,7 +299,7 @@ HuggingFace: [reaperdoesntknow](https://huggingface.co/reaperdoesntknow)
 | [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 |
 | [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
 
-**Total Portfolio: 41 models | 2,781 total downloads**
+**Total Portfolio: 49 models, 22,598 total downloads**
 
 
 *Last updated: 2026-03-28 12:55 UTC*
@@ -313,4 +321,4 @@ Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org
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-<sub>Part of the [reaperdoesntknow research portfolio](https://huggingface.co/reaperdoesntknow) — 48 models, 12,094 total downloads | Last refreshed: 2026-03-29 21:05 UTC</sub>
+<sub>Part of the [reaperdoesntknow research portfolio](https://huggingface.co/reaperdoesntknow) — 49 models, 22,598 total downloads | Last refreshed: 2026-03-30 12:10 UTC</sub>