Set Zenodo description to the concise canonical summary

Author: TNFR AI Agent

.zenodo.json

@@ -1,6 +1,6 @@
 {
   "title": "TNFR-Python-Engine: Resonant Fractal Nature Theory Implementation",
-  "description": "TNFR-Python-Engine is the canonical computational implementation of Resonant Fractal Nature Theory (TNFR): a framework for modeling coherent patterns that persist through resonance on graph-coupled networks, rather than modeling discrete objects. Every node evolves under a single nodal equation (∂EPI/∂t = νf·ΔNFR), structural change occurs exclusively through 13 canonical operators governed by a unified grammar (rules U1-U6), and system state is characterized by four structural fields — the Universal Tetrahedral Correspondence between the constants (φ, γ, π, e) and the fields (Φ_s structural potential, |∇φ| phase gradient, K_φ phase curvature, ξ_C coherence length). From this single equation a complete transport and geometric structure emerges, measured by the engine and verified to machine precision against classical, experimentally-established physics: a transport layer (graph-Laplacian diffusion, Kuramoto synchronization, random walks, effective resistance, standing-wave modes), an emergent symplectic substrate (conserved Noether charges, a Hamiltonian equal to the energy functional, complete integrability, and a polarization structure / Stokes parameters on the per-node Poincaré sphere), conservation laws, a structural integrity monitor, and per-operator contracts anchored to the nodal equation. The package includes a simplified and a fluent SDK, grammar-aware dynamics, a self-optimizing engine, and a comprehensive test suite (2043 passing). It is also used to probe famous open problems through honest, in-progress research programs — TNFR-Riemann, Navier-Stokes, Yang-Mills, P vs NP, Birch-Swinnerton-Dyer, and Hodge — each with a classified obstruction and an explicit scope statement; these are TNFR-native reformulations that do not claim proofs. Install with: pip install tnfr",
+  "description": "<p>TNFR-Python-Engine is the canonical computational implementation of Resonant Fractal Nature Theory (TNFR): a framework for modeling coherent patterns that persist through resonance on graph-coupled networks, rather than modeling discrete objects.</p><p>Every node evolves under a single nodal equation, ∂EPI/∂t = νf · ΔNFR(t). Structural change occurs exclusively through 13 canonical operators governed by a unified grammar (rules U1–U6), and system state is characterized by four structural fields — the Universal Tetrahedral Correspondence between the constants (φ, γ, π, e) and the fields (Φ_s structural potential, |∇φ| phase gradient, K_φ phase curvature, ξ_C coherence length).</p><p>From this single equation a complete transport and geometric structure emerges, measured by the engine and verified to machine precision against classical, experimentally-established physics.</p><p>Install: pip install tnfr · Documentation and source: https://github.com/fermga/TNFR-Python-Engine</p>",
   "version": "0.0.3.4",
   "publication_date": "2026-06-17",
   "doi": "10.5281/zenodo.17602860",