Status: Technical reference Version: 0.0.3.3 Date: March 2026
This document describes domain-specific applications of the nodal equation that are implemented, tested, and computationally verified in this repository. The primary application is spectral factorization via Paley–Jacobi graphs, which has a dedicated implementation with 10 test modules, structural verification criteria, and reproducible certificates.
Demonstration-level examples (particle collisions, elemental structure) are listed in §5 with their actual verification status.
For a composite integer , the Paley–Jacobi graph constructed from quadratic residues exhibits spectral coherence structure under the canonical decoder sequence . The resulting partitions expose periodicities corresponding to the prime factors of .
TNFR_PARTITION_TARGET_SIZE).A factor candidate is TNFR-certified when of 8 structural criteria hold and of partition endorsements are positive:
| Criterion | Threshold | Basis |
|---|---|---|
| gain | drop | Nodal equation convergence |
| Coherence ratio | Structural similarity bounds | |
| delta | Structural-potential confinement (empirical) | |
| Gradient delta | Phase desynchronization limits | |
| Curvature delta | Geometric torsion stability | |
| Periodicity confidence | Structural mode certification | |
| Stabilized fraction | Multi-scale coherence (U5) | |
| Coverage fraction | Spatial completeness |
Structural periodicity confidence:
When TNFR_PURE_MODE=1, factor certification relies exclusively on structural confidence () without arithmetic divisibility checks. This isolates the TNFR-specific signal from classical number-theoretic shortcuts.
| Artifact | Format | Location |
|---|---|---|
| Operator certificates | JSON | results/certificates/ |
| Partition manifests | JSON (gzip above threshold) | results/certificates/partitioned/ |
| Factor telemetry | dict with , $ | \nabla\phi |
| Failure diagnostics | JSON | Configurable via TNFR_FAILURE_TELEMETRY |
All artifacts include SHA256 hashes and deterministic seeds for reproducibility.
| Component | Path |
|---|---|
| High-level API | factorization-lab/tnfr_factorization/api.py |
| Spectral Paley decoder | factorization-lab/tnfr_factorization/spectral_paley.py |
| Partition planner | factorization-lab/tnfr_factorization/partitioning.py |
| Seed management | factorization-lab/tnfr_factorization/seed_management.py |
| Self-optimization support | factorization-lab/tnfr_factorization/self_opt_support.py |
| Feedback integration | factorization-lab/tnfr_factorization/feedback_integration.py |
| Snapshot/replay system | factorization-lab/tnfr_factorization/snapshot_system.py |
| CLI entry point | factorization-lab/tnfr_factorization/cli.py |
from tnfr_factorization.api import factorize
result = factorize(221, pure=True, trace=True)
# result.candidate_factors -> [13, 17]
# result.tnfr_certified_factors -> [13, 17]
# result.telemetry -> {phi_s, phase_gradient, ...}
# result.certificate_path -> path to operator certificateTNFRAdvancedFFTEngine for spectral computationTNFRSelfOptimizingEngine for operator sequence recommendationvalidate_sequence()ArithmeticTNFRParameters and ArithmeticStructuralTerms for number-theoretic integration| Test module | Scope |
|---|---|
test_spectral_paley.py | Nodal decoder derives partition factors; FFT integration; certificate grammar compliance |
test_verification_robustness.py | Verification criteria ranges; false-positive resistance parameters; boundary conditions |
test_false_positive_verifier.py | False-positive resistance testing |
test_false_positive_methodology.py | Simulated verification methodology |
test_partitioning.py | Partition planner correctness |
test_feedback_integration.py | Feedback loop integration |
test_self_opt_support.py | Self-optimization engine support |
test_seed_management.py | Deterministic seed management |
test_snapshot_system.py | Snapshot/replay system integrity |
test_cli.py | Command-line interface |
Key verified properties:
min_partition_flags , dnfr_gain_min , periodicity_confidence_min .| Example | Concept from this document |
|---|---|
| 41_von_mangoldt_zeta_demo.py | Prime-ladder von Mangoldt series (P12) |
| 42_riemann_zeros_as_resonances.py | Riemann zeros as resonance poles (P13) |
| 43_prime_ladder_hamiltonian_demo.py | Canonical νf prime-ladder Hamiltonian (P14) |
Note: the attack uses the emergent prime-NFR nodal pulse (νf = log n; zeros as
destructive interference).
src/tnfr/riemann/nodal_pulse.py — canonical nodal-pulse foundation (νf = log n; zeros as destructive interference)src/tnfr/riemann/prime_ladder_hamiltonian.py — canonical νf prime-ladder Hamiltonian (P14)src/tnfr/riemann/von_mangoldt.py — prime-ladder von Mangoldt reproduction (P12)