TNFR Structural Field Tetrad (Canonical)

Status: CANONICAL (Updated 2025-11-12)

This guide centralizes the physics, math, implementation, telemetry, and usage of the four structural fields that characterize TNFR networks across scales. It is the single canonical source for formal definitions of Φ_s, |∇φ|, K_φ, and ξ_C; other documents SHOULD reference this file instead of restating the equations.

• Structural Potential (Φ_s): Global potential from ΔNFR distribution (inverse-square law analog)
• Phase Gradient (|∇φ|): Local phase desynchronization (stress proxy)
• Phase Curvature (K_φ): Geometric confinement/torsion
• Coherence Length (ξ_C): Spatial correlation scale of local coherence

References (single sources of truth): - Theory: TNFR.pdf (§1–2), UNIFIED_GRAMMAR_RULES.md (§U1–U6) - Canonical status and thresholds: AGENTS.md (Structural Field Tetrad) - Implementation: src/tnfr/physics/fields.py - Research background: docs/TNFR_FORCES_EMERGENCE.md (§14–15) - Physics module overview: src/tnfr/physics/README.md (unified, expandable)

1. Physics Basis

Nodal equation (core TNFR dynamics):

[\frac{\partial EPI}{\partial t} = \nu_f\, \Delta NFR(t)]

Integrated form and boundedness (U2):

[EPI(t_f) = EPI(t_0) + \int_{t_0}^{t_f}! \nu_f(\tau)\,\Delta NFR(\tau)\, d\tau,\quad \text{require } \int \nu_f\, \Delta NFR\, dt < \infty]

Operator grammar enforces convergence via stabilizers (IL, THOL) around destabilizers (OZ, ZHIR, VAL) and requires phase-verification for coupling (U3). Telemetry fields below are read-only; they do not mutate EPI.

2. Canonical Field Definitions

All functions live in tnfr.physics.fields and accept a NetworkX graph G with the following node attributes when applicable: - theta or phase (float in [0, 2π)) - delta_nfr or dnfr (float; structural pressure proxy) - Optional coherence (float ∈ (0,1]) for ξ_C estimation

2.1 Structural Potential Φ_s (Global)

Definition (α = 2 by default):

[\Phi_s(i) = \sum_{j\neq i} \frac{\Delta NFR_j}{d(i,j)^\alpha}]

• Long-range, global potential derived from ΔNFR distribution
• Safety criterion (telemetry-based): ΔΦ_s < 2.0 typical; escape threshold at ~2.0 (classical: von Koch bounds)
• Implementation: compute_structural_potential(G, alpha=2.0)

2.2 Phase Gradient |∇φ| (Local stress)

Wrapped neighbor differences (circular topology):

[|\nabla\varphi|(i) = \operatorname{mean}_{j\in N(i)} \big|\operatorname{wrap}(\varphi_j-\varphi_i)\big|]

• Early warning for fragmentation via local desynchronization
• Safety criterion: |∇φ| < 0.2904 for stable operation (classical: harmonic analysis + Kuramoto)
• Implementation: compute_phase_gradient(G)

2.3 Phase Curvature K_φ (Geometric confinement)

Deviation from circular neighbor mean:

[K_\varphi(i) = \varphi_i - \frac{1}{\deg(i)} \sum_{j\in N(i)} \varphi_j]

• Use circular mean (unit vectors) and wrap deltas to (−π, π]
• Local threshold: |K_φ| ≥ 2.8274 flags confinement/fault zones (classical: 90% of theoretical maximum π)
• Multiscale behavior: var(K_φ) ~ 1/r^α with α≈2.76 (asymptotic freedom)
• Implementation:
• compute_phase_curvature(G)
• compute_k_phi_multiscale_variance(G, scales)
• fit_k_phi_asymptotic_alpha(var_by_scale)
• k_phi_multiscale_safety(G, alpha_hint=2.76)

2.4 Coherence Length ξ_C (Spatial correlations)

Local coherence: (c_i = 1 / (1 + |\Delta NFR_i|)) and spatial autocorrelation (C(r) = \langle c_i c_j\rangle) for pairs at distance r. Fit exponential decay:

[C(r) \sim \exp(-r/\xi_C)]

• Critical point behavior: ξ_C diverges near I_c (phase transitions)
• Safety cues: ξ_C > system diameter (critical), ξ_C > π × mean_distance (watch, π≈3.1416), ξ_C < mean_distance (stable)
• Implementation:
• estimate_coherence_length(G, coherence_key='coherence')
• fit_correlation_length_exponent(Is, xi_vals, I_c, min_distance)

3. Contracts, Units, and Invariants

• Read-only telemetry: No EPI mutation; fields compute from current node attributes
• Units: ν_f in Hz_str; do not mix with physical Hz (Invariant #2)
• Phase coupling requires explicit verification |Δφ| ≤ Δφ_max (U3)
• Valid sequences must satisfy U1 initiation/closure and U2 boundedness
• Nested EPIs require stabilizers at each level (U5)

Edge cases: - Isolated nodes: return 0.0 for gradients/curvature; ignore in Φ_s sums - Missing attributes: functions attempt sensible defaults; callers should initialize at least theta/phase and delta_nfr/dnfr

4. API Summary (tnfr.physics.fields)

• compute_structural_potential(G, alpha: float = 2.0) -> dict[int,float]
• compute_phase_gradient(G) -> dict[int,float]
• compute_phase_curvature(G) -> dict[int,float]
• compute_k_phi_multiscale_variance(G, scales: tuple[int,...]) -> dict[int,float]
• fit_k_phi_asymptotic_alpha(var_by_scale: dict[int,float]) -> dict
• k_phi_multiscale_safety(G, scales=(1,2,3,5), alpha_hint=2.76, tolerance_factor=2.0, fit_min_r2=0.5) -> dict
• estimate_coherence_length(G, coherence_key='coherence') -> float
• fit_correlation_length_exponent(Is: array, xi_vals: array, I_c: float, min_distance=0.01) -> dict
• measure_phase_symmetry(G) -> dict
• path_integrated_gradient(G, path: list[int]) -> float

Each function documents parameters and return types inline in fields.py.

5. Validation and Safety Thresholds

Canonical telemetry thresholds (empirical, cross-topology): - Φ_s: maintain ΔΦ_s < 2.0 (escape threshold) — see AGENTS.md (U6) - |∇φ|: keep < 0.38 for stable operation; track spikes as early warning - K_φ: flag |K_φ| ≥ 2.8274 as hotspots; assess multiscale decay var(K_φ) ~ 1/r^α - ξ_C: monitor divergence around I_c; large ξ_C indicates global reorganization

Minimum tests (see tests/ and AGENTS.md): - Coherence monotonicity under IL - Dissonance-triggered bifurcation with handlers present - Resonance propagation increases phase synchrony - Silence preserves EPI - Mutation threshold crossing changes phase label - Multiscale nested EPIs maintain coherence - Seed reproducibility

6. Workflows and Tooling

• Integrated study: benchmarks/integrated_force_regime_study.py (six-task harness)
• Methods comparison: benchmarks/grammar_2_0_benchmarks.py and summaries
• Plotting: benchmarks/plot_force_study_summaries.py → saves to results/plots/*.png
• Notebook: notebooks/Force_Fields_Tetrad_Exploration.ipynb (end-to-end)
• Static report: results/reports/Force_Fields_Tetrad_Exploration.html
• VS Code tasks: .vscode/tasks.json
• “Export TNFR tetrad HTML report”
• “Generate force study plots”

Artifacts: - results/integrated_force_study_summary.json - results/field_methods_battery_summary.json - results/plots/*.png

7. Minimal Example

import networkx as nx
from tnfr.physics.fields import (
    compute_structural_potential,
    compute_phase_gradient,
    compute_phase_curvature,
    estimate_coherence_length,
)

G = nx.watts_strogatz_graph(60, k=4, p=0.2, seed=42)
# Initialize minimal telemetry
for n in G.nodes():
    G.nodes[n]['theta'] = 0.1 * (n/59.0)
    G.nodes[n]['delta_nfr'] = 0.1

phi = compute_structural_potential(G, alpha=2.0)
grad = compute_phase_gradient(G)
kphi = compute_phase_curvature(G)
xi = estimate_coherence_length(G, coherence_key='coherence')  # if provided

8. Governance and Traceability

• Physics-first: all field definitions derive from nodal equation semantics
• No ad-hoc mutations: fields are telemetry-only; EPI changes go through operators
• Units and invariants preserved (see AGENTS.md invariants 1–10)
• Canonical docs: this page + UNIFIED_GRAMMAR_RULES.md are the reference; research-phase content (e.g., TNFR_FORCES_EMERGENCE.md) is linked but not normative

9. Further Reading

• AGENTS.md — Canonical invariants and field promotions (Φ_s, |∇φ|, K_φ, ξ_C)
• UNIFIED_GRAMMAR_RULES.md — U1–U6 derivations and constraints
• TNFR_FORCES_EMERGENCE.md — Historical research path for field validation
• SHA_ALGEBRA_PHYSICS.md — Supporting mathematical apparatus

10. FAQ

Q1. What node attributes are required to compute each field? - Φ_s: requires delta_nfr or dnfr on nodes; uses graph distances. - |∇φ| and K_φ: require theta or phase on nodes (float in [0, 2π)). - ξ_C: optionally uses coherence on nodes; if absent, it estimates from delta_nfr via c_i = 1/(1+|ΔNFR_i|).

Q2. Why are phase differences wrapped? Can I just subtract angles? - Phases live on the circle. Direct subtraction misinterprets, e.g., 0 and 2π as far apart. We compute circular means (via unit vectors) and wrap differences to (−π, π] to preserve correct geometry.

Q3. How should I choose α in Φ_s? - α = 2.0 is canonical (inverse-square analog) and validated across topologies. Deviations are research-only; if you change α, document and justify the physics in your application.

Q4. Are thresholds (ΔΦ_s < 2.0, |∇φ| < 0.2904, |K_φ| ≥ 2.8274) universal? - They are telemetry-based and robust across the tested families (WS, scale-free, grid, trees) but still empirical. Treat them as safety guidance, not as hard correctness proofs. Monitor trends over time, not just single snapshots.

Q5. What graphs are supported? Weighted? Directed? - Implementations are designed for undirected, unweighted graphs. Φ_s currently uses unweighted shortest-path distances. If your graph is weighted or directed, pre-process to an appropriate undirected/unweighted view or extend the distance routine consistently with TNFR physics.

Q6. What happens if attributes are missing? - Functions fall back conservatively (e.g., 0.0 for empty neighborhoods) but you should initialize at least theta/phase and delta_nfr/dnfr. For ξ_C, if coherence is missing, it infers local coherence from ΔNFR magnitudes.

Q7. ξ_C returned NaN/inf. What does that mean? - Near criticality, an exponential fit may be ill-posed (flat or noisy C(r)). Re-run with more samples, verify coherence distribution, or widen the r-range. If the system is truly at/near I_c, very large ξ_C is expected; treat it as a warning for imminent system-wide reorganization.

Q8. How does the tetrad relate to C(t) and Si? - C(t) is a global coherence scalar; Si measures stable reorganization capacity. The tetrad provides complementary structure: Φ_s (global field), |∇φ| (local stress), K_φ (geometric confinement), ξ_C (spatial correlation scale). Use them together for a complete picture. Note: C(t) is invariant to proportional ΔNFR scaling; |∇φ| often captures early local stress better.

Q9. Performance tips for large graphs? - Φ_s requires many distance evaluations; on large graphs, consider limiting to a radius, sampling source nodes, or caching all-pairs shortest paths if topology is static. |∇φ| and K_φ are O(E) and scale well. ξ_C can subsample pairs at each distance bin.

Q10. What should I do when safety flags trigger? - Apply stabilizers (IL, THOL), verify phase-compatibility before coupling (U3), reduce destabilizer intensity (OZ, ZHIR, VAL), and monitor ΔΦ_s, |∇φ|, and K_φ decay across scales. Ensure ν_f units remain in Hz_str and do not mutate EPI outside operators.

Q11. Reproducibility and randomness? - Set seeds (Python, NumPy) before generating telemetry or randomized structures. The same seed must yield identical trajectories and telemetry (Invariant #8).

Q12. Can I extend these fields or add new ones? - Yes, but only with physics-first justification. Derive from the nodal equation, preserve invariants, map to operators where applicable, and add tests and documentation. Experimental fields must be clearly labeled non-canonical until validated.

Appendix: Topological winding (Q) — complementary telemetry

While not part of the field tetrad, the topological winding number around a closed loop provides a complementary invariant for identifying phase defects and vortex-like structures:

Definition: Q = round( (1 / 2π) · Σ wrap(φ_{i+1} − φ_i) ) over a closed cycle.

• Implementation: tnfr.physics.fields.compute_phase_winding(G, cycle_nodes)
• Usage: helpful to distinguish plane-wave-like (Q≈0) from vortex-like (Q=±1) configurations in pattern studies.
• Related initializations: tnfr.physics.patterns.apply_vortex, apply_plane_wave, apply_quark_triplet_cluster.

This metric is telemetry-only and preserves all canonical invariants.