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Resonant Fractal Nature Theory — a mathematical framework for coherent patterns on graph-coupled networks.

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© 2026 TNFR project — MIT licensed.DOI 10.5281/zenodo.17602860
docs
grammar
PHYSICS_VERIFICATION.md
API_CONTRACTS.mdCANONICAL_OZ_SEQUENCES.mdEMPIRICAL_CONFRONTATION_EEG.mdREADME.mdSTRUCTURAL_FIELDS_TETRAD.mdSTRUCTURAL_INTERFACE_THEORY.md
theory
APPLIED_STRUCTURAL_ANALYSIS.mdCATALOG_TYPE_HYGIENE_PROGRAMME.mdDISSIPATIVE_AND_OPEN_SYSTEMS.mdEMERGENT_ONTOLOGY.mdEXTENDED_FIELDS_AND_DERIVED_QUANTITIES.mdFUNDAMENTAL_THEORY.mdGAUGE_SYMMETRY_AND_UNIFICATION.mdGLOSSARY.mdMATHEMATICAL_DYNAMICS_BASIS.mdMINIMAL_STRUCTURAL_DEGREES.mdNUCLEUS_A_PRIME_LADDER_ATLAS.mdNUCLEUS_B_EQUIVARIANCE_OBSTRUCTIONS.mdPHYSICAL_REGIME_CORRESPONDENCES.mdREADME.mdREMESH_INFINITY_DERIVATION.mdSTRUCTURAL_CONSERVATION_THEOREM.mdSTRUCTURAL_OPERATORS.mdSTRUCTURAL_STABILITY_AND_DYNAMICS.mdTNFR_BSD_RESEARCH_NOTES.mdTNFR_HODGE_RESEARCH_NOTES.mdTNFR_NAVIER_STOKES_RESEARCH_NOTES.mdTNFR_NUMBER_THEORY.mdTNFR_P_VS_NP_RESEARCH_NOTES.mdTNFR_RIEMANN_RESEARCH_NOTES.mdTNFR_VARIATIONAL_PRINCIPLE.mdTNFR_YANG_MILLS_RESEARCH_NOTES.mdTNFR.pdfUNIFIED_GRAMMAR_RULES.md
factorization-lab
analysis
analyze_patterns.pycertificate_manifest.py
benchmarks
benchmark_analysis.pybenchmark_expansion_suite.pyfull_spectrum_factorization.pypaley_gap_extended.pypaley_gap_smoke.pytest_benchmark_suite.py
demos
experiment_contexts
exp_0b1663cd19b7.jsonexp_0bf0054b7474.jsonexp_75a4c8ca616a.jsonexp_848ee0fd1857.jsonexp_f6fe00562193.jsonexp_fdf3da424e1e.json
failure_telemetry_batch.pyfeedback_integration_demo.pyintegration_demo_snapshots.dbseed_management_integration_demo.pysnapshot_integration_demo.pytrajectory_143.jsontrajectory_77.jsontrajectory_89.jsontrajectory_91.jsontrajectory_97.json
docs
FACTORING_PLAYBOOK.mdFALSE_POSITIVE_TEST_SUITE.mdOPERATOR_CERTIFICATES.mdROADMAP.mdSPECTRAL_ROUTE.md
experiment_contexts
exp_cebe1d9e7d8e.json
notebooks
spectral_history.ipynb
scripts
run_false_positive_tests.py
tests
run_false_positive_test_suite.pytest_cli.pytest_false_positive_methodology.pytest_false_positive_verifier.pytest_feedback_integration.pytest_partitioning.pytest_seed_management.pytest_self_opt_support.pytest_snapshot_system.pytest_spectral_paley.pytest_verification_robustness.py
tnfr_factorization
__init__.pyapi.pycli.pyfailure_telemetry.pyfeedback_adapter.pyfeedback_integration.pypartitioning.pyself_opt_support.pyspectral_paley.py
demo_snapshots.dbLICENSE_SNAPSHOT.mdPACKAGE_SUMMARY.mdREADME.mdseed_management.pysnapshot_system.pytest_certificate_hashing.pytest_installation.pyverification_trajectory_77.json
benchmarks
analyze_tetrad_universality.pyb0star_alpha_canonical_product_graphs.pybenchmark_optimization_tracks.pybenchmark_utils.pyboundary_vibration.pybridge_primes_riemann.pychiral_involution.pycli_utils.pycoherence_projector_sense_index.pycommutant_bridge.pycomposition_arithmetic.pyconfinement_zones_test.pyconservation_law_validation.pydirected_paley_bridge.pyemergent_arithmetic_pulse.pyemergent_atom_dynamics.pyemergent_atomic_shells.pyemergent_base_dimension.pyemergent_dimension_dynamics.pyemergent_fractal_pulse.pyemergent_fractal_simplex_dimension.pyemergent_integers_symmetry.pyemergent_musical_nfr.pyemergent_nfr_geometry.pyemergent_nfr_where.pyemergent_rationals.pyemergent_rhythm.pyemergent_screening.pyemergent_shell_cardinals.pyemergent_shell_ordering.pyemergent_simplex_dimension.pyemergent_substrate_symmetry.pyequivariance_wall.pyexternal_phase_gate_validation.pyfield_methods_battery.pygolden_residue_remesh_bridge.pyintegrated_force_regime_study.pyinverse_spectrum_to_symmetry.pyk_phi_safety_demo.pykuramoto_farey_bridge.pymissing_piece_bridge.pymultichannel_interface_benchmark.pynavier_stokes_recipe_bridge.pynodal_propagator_residue_bridge.pyns_moment_hierarchy_cascade.pyoperational_irreducibility.pypaley_bridge.pyphase_curvature_investigation.pyphase_wall.pyphi_s_confinement_investigation.pyprimes_as_consequence.pypulse_phase_coherence_budget.pyREADME.mdremesh_infinity_riemann_baseline.pyremesh_infinity_riemann_composed.pyremesh_infinity_riemann_modified_graph.pyremesh_infinity_riemann_operator.pyremesh_infinity_riemann_spectral_basis.pyremesh_infinity_riemann_spectral_robustness.pyremesh_infinity_riemann_spectral.pyresidue_phase_vs_riemann.pystructural_interface_benchmark.pytemporal_interface_benchmark.pytetrad_results_aggregate.pyu2_destabilization_irreversibility.pyuniversality_clusters.pyxi_c_fast_experiment.py
primality-test
benchmarks
comprehensive_benchmark.py
docs
ADVANCED_INTEGRATION.mdmathematical_foundation.mdperformance_analysis.md
examples
advanced_examples.pybasic_usage.py
tnfr_primality
__init__.py__main__.pyadvanced_cli.pyadvanced_core.pycli.pyconstants.pycore.pyoptimized.py
MANIFEST.inPACKAGE_SUMMARY.mdREADME.mdRELEASE_NOTES_v1.0.mdsetup.pytest_installation.py
tests
core_physics
__init__.pytest_conservation_laws.pytest_delta_nfr_computation_paths.pytest_delta_nfr.pytest_dispersion_coherence_sign_invariance.pytest_emergent_constants_guard.pytest_lyapunov_operators.pytest_nodal_equation.pytest_structural_triad.py
data
replay_manifests
sample_run
_manifest_summary.json_manifest.json_partition_files.txt.gz
self_opt_validation
seed_alpha
paley.json
seed_beta
integration.json
seed_gamma
unknown.json
self_optimization
test_run
partitioned
test_run
test_run_p0.jsontest_run_p1.json
_manifest_summary.json_manifest.json
engines
test_pattern_discovery_manifest.pytest_self_optimization_engine.py
mathematics
__init__.pytest_autodiff.pytest_backends.pytest_dissipative_dynamics.pytest_epi.pytest_factory_patterns.pytest_metrics.pytest_navier_stokes_refounded.pytest_number_theory_canonical.pytest_operators.pytest_residue_networks.pytest_riemann_nodal_pulse.pytest_riemann_pulse_coherence.pytest_spaces.pytest_transforms.pytest_validator.py
operators
test_canonical_operators_modern.pytest_grammar_canon.pytest_grammar_canonical_consistency.pytest_grammar_dynamics.pytest_operator_contracts.pytest_operator_strategies.py
parallel
test_fractal_partition_manifest.py
physics
test_conservation_gauge_unification.pytest_dissipative_conservation.pytest_emergent_chemistry.pytest_field_cache_invalidation.pytest_gauge.pytest_phase_transition.pytest_signatures.pytest_spectral_conservation.pytest_structural_diffusion.pytest_structural_integrity.pytest_symplectic_substrate.pytest_tetrad_bounds.pytest_variational.pytest_yang_mills_closure.pytest_yang_mills_derivability.pytest_yang_mills_scaling.pytest_yang_mills_structural_gap.pytest_yang_mills_u6_sweep.py
scripts
test_run_self_opt_validation.pytest_run_self_optimization.py
sdk
__init__.pytest_simple_advanced.py
__init__.pyconftest.pyREADME.mdtest_breast_cancer_phase_gate_demo.pytest_classical_mechanics.pytest_distributed_fft.pytest_external_phase_gate_validation.pytest_factorization_entrypoint.pytest_multichannel_interface.pytest_nodal_optimizer.pytest_phase_gate_api.pytest_replay_register_manifest.pytest_signal_confrontation.pytest_structural_interface_api.pytest_structural_interface_baselines.pytest_structural_interface_benchmark.pytest_temporal_interface.pytest_vectorized_coherence_length_regression.pytest_wine_quality_phase_gate_demo.pyutils.py
examples
01_foundations
01_hello_world.py02_musical_resonance.py03_network_formation.py04_operator_sequences.py05_coherence_evolution.py06_network_topologies.py07_phase_transitions.py08_emergent_phenomena.py09_visualization_suite.py10_simplified_sdk_showcase.py
02_physics_regimes
11_classical_limit_comparison.py115_operator_contract_audit.py12_classical_mechanics_demo.py13_quantum_mechanics_demo.py14_uncertainty_and_interference.py15_train_crossing_demo.py17_conservation_law_demo.py26_gauge_structure_demo.py27_variational_principle_demo.py28_dissipative_systems_demo.py29_lyapunov_stability_demo.py30_self_optimization_demo.py31_mathematical_constants_basis.py33_complex_field_unification.py34_conservation_protocol_suite.py35_tetrad_irreducibility.py36_grammar_violation_detector.py37_operator_tetrad_synergy.py38_grammar_energy_landscape.py39_nodal_equation_decomposition.py
03_riemann_zeta
157_nodal_pulse_phase_attack.py41_von_mangoldt_zeta_demo.py42_riemann_zeros_as_resonances.py43_prime_ladder_hamiltonian_demo.py44_weil_explicit_formula_demo.py45_li_keiper_demo.py46_weil_tnfr_positivity_demo.py47_alpha_sweep_demo.py48_admissible_family_sweep_demo.py49_nodeaware_gauge_sweep_demo.py50_uniform_coercivity_demo.py51_adaptive_coercivity_demo.py52_paley_gap_coercivity_demo.py53_lyapunov_spectral_positivity_demo.py54_hilbert_polya_demo.py55_structural_zero_density_demo.py56_spectral_emergence_demo.py57_admissible_rescaling_demo.py58_oscillatory_correction_demo.py
04_riemann_L_twisted
59_dirichlet_l_function_demo.py60_dirichlet_l_continuation_demo.py61_dirichlet_l_hamiltonian_demo.py62_dirichlet_weil_explicit_formula_demo.py63_dirichlet_li_keiper_demo.py64_twisted_weil_positivity_demo.py65_twisted_alpha_sweep_demo.py66_twisted_admissible_family_sweep_demo.py67_twisted_nodeaware_gauge_sweep_demo.py68_twisted_hermite_family_demo.py69_twisted_coercivity_uniform_demo.py70_twisted_paley_gap_coercivity_demo.py71_twisted_lyapunov_spectral_demo.py72_twisted_hilbert_polya_demo.py73_twisted_structural_zero_density_demo.py74_twisted_spectral_emergence_demo.py75_twisted_admissible_rescaling_demo.py76_twisted_oscillatory_correction_demo.py
05_type_hygiene
77_remesh_infinity_residue_split_demo.py78_nuf_type_signature_demo.py79_epi_type_signature_demo.py80_phi_type_signature_demo.py81_dnfr_type_signature_demo.py82_remesh_window_type_signature_demo.py83_delta_phi_max_type_signature_demo.py84_coupling_weights_type_signature_demo.py85_tetrad_closure_signature_demo.py86_currents_closure_signature_demo.py87_aggregates_closure_signature_demo.py88_urules_consistency_signature_demo.py89_operator_catalog_discipline_signature_demo.py
06_navier_stokes
158_navier_stokes_two_face_refounded.py
07_number_theory
100_prime_families_orbits.py101_numbers_as_coupled_network.py102_nodal_flow_primes_equilibria.py116_nuf_emergent_prime_visibility.py146_primality_grammatical_inertness.py147_numbers_as_free_monoid_words.py148_capacity_arm_carries_von_mangoldt.py149_p14_is_the_capacity_arm_operator.py153_structural_frequency_rank_cyclotomy.py40_arithmetic_number_theory.py94_generative_number_construction.py95_primes_from_spectral_waves.py96_spectral_vibration_of_coherence.py97_goldbach_additive_multiplicative.pyemergent_chemistry_particles_demo.py
08_emergent_geometry
103_emergent_substrate_meets_riemann.py106_per_node_polarization_geometry.py107_orthogonal_structure_emergent_geometry.py108_emergent_field_generating_structure.py112_structure_predicts_coherence_flow.py113_overdamped_projection_bridge.py114_substrate_conserved_quantities.py117_emergent_geometry_residue_graph.py118_emergent_vs_classical_operator.py119_phase_sector_directed_residue.py120_symmetry_wall_substrate_vs_spectrum.py121_canonical_symmetry_break_negative.py122_factorization_phase_sector.py123_symmetry_sector_decomposition.py124_emergent_metric_fractal_consistency.py125_node_is_the_emergent_substrate.py126_two_layers_base_fiber.py127_base_is_emergent_not_imposed.py128_base_substrate_coemergence.py129_spectral_gap_base_fiber_clock.py130_operators_break_substrate_charges.py131_coemergent_loop_convergence.py132_geometric_phase_holonomy.py133_psi_topological_defects.py134_spectral_dimension_heat_kernel.py135_arrow_of_time_h_theorem.py136_heat_kernel_coefficients.py137_synchronization_transition.py138_structure_frequency_synchronization.py139_grammar_formal_language.py140_grammar_automaton.py141_grammar_rule_decomposition.py142_grammar_operator_quotient.py143_glyphic_function_sublanguage.py144_branching_combinator.py145_syntactic_monoid_starfree.py150_emergent_grammatical_pattern_parry.py151_grammar_in_emergent_geometry.py152_operator_contract_tetrahedron.py154_conductor_annotated_qr_spectrum.py155_ontological_position_of_numbers.py156_emergence_directness_law.py98_emergent_symplectic_substrate.py99_structural_diffusion.pyunified_fields_showcase.py
09_millennium
109_p_vs_np_coherence_synthesis.py110_bsd_rank_structural_pressure.py111_hodge_discrete_and_honest_gap.py
10_applications
159_empirical_confrontation_pipeline.py90_phase_gate_monitor_demo.py91_breast_cancer_phase_gate_demo.py92_wine_quality_phase_gate_demo.py93_structural_interface_demo.pypytorch_cuda_demo.py
README.md
scripts
replay
__init__.pyregister_manifest.py
__init__.pyREADME.mdrebuild_failure_manifest.pyrun_reproducible_benchmarks.pyrun_self_opt_validation.pyrun_self_optimization.pytnfr_is_prime.pyvalidate_conservation_law.pyverify_internal_references.py
src
core
__init__.pyevaluation.py
tnfr
backends
__init__.pyjax_backend.pynumpy_backend.pyoptimized_numpy.pyREADME.mdtorch_backend.py
cli
__init__.py__init__.pyiarguments.pyarguments.pyiexecution.pyexecution.pyiinteractive_validator.pyREADME.mdutils.pyutils.pyi
compat
__init__.pydataclass.pyjsonschema_stub.pymatplotlib_stub.pynumpy_stub.pyREADME.md
config
__init__.py__init__.pyiconstants.pyconstants.pyidefaults_core.pydefaults_init.pydefaults_metric.pydefaults.pyfeature_flags.pyfeature_flags.pyiglyph_constants.pyoperator_names.pyoperator_names.pyiphysics_derivation.pyprecision_modes.pypresets.pypresets.pyiREADME.mdsecurity.pythresholds.pytnfr_config.py
constants
__init__.py__init__.pyialiases.pyaliases.pyicanonical.pymetric.pymetric.pyioperational.py
core
__init__.pycontainer.pydefault_implementations.pyexceptions.pyinterfaces.pyREADME.md
dynamics
__init__.py__init__.pyiadaptation.pyadaptation.pyiadaptive_sequences.pyadaptive_sequences.pyiadelic.pyadvanced_cache_optimizer.pyadvanced_fft_arithmetic.pyaliases.pyaliases.pyibifurcation.pycache_aware_fft_engine.pycanonical.pycanonical.pyicomputational_hub.pycoordination.pycoordination.pyidistributed_fft.pydnfr.pydnfr.pyidynamic_limits.pyemergent_centralization.pyemergent_integration_engine.pyfeedback.pyfeedback.pyifft_backend.pyfft_cache_coordinator.pyfft_dispatchers.pyfft_engine.pyfft_workers.pyfused_dnfr.pyhomeostasis.pyhomeostasis.pyiintegrators.pyintegrators.pyilearning.pylearning.pyimetabolism.pymulti_modal_cache.pynbody_tnfr.pynbody.pynodal_optimizer.pyoptimization_orchestrator.pypropagation.pyREADME.mdruntime.pyruntime.pyisampling.pysampling.pyiselectors.pyselectors.pyiself_optimizing_engine.pyspectral_structural_fusion.pystructural_cache.pystructural_clip.pysymplectic.pyunified_backend.pyunified_mathematical_cache_orchestrator.py
engines
computation
__init__.pyfft_engine.pyunified_fft_engine.pyunified_gpu_system.py
constants
__init__.pycanonical.pyoperational.py
integration
__init__.pyemergent_integration.py
pattern_discovery
__init__.pymathematical_patterns.pymulti_modal_cache.py
self_optimization
__init__.pyengine.py
__init__.pyREADME.md
errors
__init__.pycontextual.py
factorization
__init__.py
flatten
README.md
gamma
README.md
glyph_history
README.md
glyph_runtime
README.md
immutable
README.md
initialization
README.md
io
README.md
math
__init__.pyfields_symbolic.pygrammar_validators.pyoptimizer.pyREADME.mdsymbolic.py
mathematics
__init__.pybackend.pybackend.pyidynamics.pydynamics.pyiepi.pyepi.pyigenerators.pygenerators.pyiliouville.pymetrics.pymetrics.pyinumber_theory.pyoperators_factory.pyoperators_factory.pyioperators.pyoperators.pyioptimized_primality.pyprojection.pyprojection.pyiREADME.mdruntime.pyruntime.pyispaces.pyspaces.pyispectral.pytransforms.pytransforms.pyiunified_cache.pyunified_numerical.pyzeta.py
metrics
__init__.py__init__.pyibuffer_cache.pybuffer_cache.pyicache_utils.pycoherence.pycoherence.pyicommon.pycommon.pyicore.pycore.pyidiagnosis.pydiagnosis.pyiemergence.pyexport.pyexport.pyiglyph_timing.pyglyph_timing.pyilearning_metrics.pylearning_metrics.pyilocal_coherence.pyphase_coherence.pyphase_compatibility.pyREADME.mdreporting.pyreporting.pyisense_index.pysense_index.pyitelemetry.pytetrad.pytrig_cache.pytrig_cache.pyitrig.pytrig.pyi
multiscale
__init__.pyhierarchical.pyREADME.md
navier_stokes
__init__.pyconservative_face.pyoperator.py
node
README.md
observers
README.md
operators
network_analysis
__init__.pysource_detection.py
postconditions
__init__.pymutation.py
preconditions
__init__.pycoherence.pydissonance.pyemission.pymutation.pyreception.pyresonance.py
strategies
__init__.pydefaults.pygpu_strategies.pystrategy.py
__init__.py__init__.pyialgebra.pycanonical_patterns.pycascade.pycoherence.pycontraction.pycoupling.pycycle_detection.pydefinitions_base.pydefinitions.pydefinitions.pyidissonance.pyemission.pyexpansion.pygrammar_application.pygrammar_canon.pygrammar_context.pygrammar_core.pygrammar_dynamics.pygrammar_error_factory.pygrammar_memoization.pygrammar_patterns.pygrammar_telemetry.pygrammar_types.pygrammar_u6.pygrammar_validate.pygrammar.pygrammar.pyihamiltonian.pyhealth_analyzer.pyintrospection.pyjitter.pyjitter.pyilifecycle.pymetabolism.pymetrics_basic.pymetrics_core.pymetrics_network.pymetrics_structural.pymetrics_u6.pymetrics.pymutation.pynodal_equation.pyoperator_contracts.pypattern_detection.pypatterns.pyREADME.mdreception.pyrecursivity.pyregistry.pyregistry.pyiremesh.pyremesh.pyiresonance.pyself_organization.pysilence.pystructural_units.pytransition.py
parallel
__init__.pyauto_scaler.pydistributed.pyengine.pymonitoring.pypartitioner.pyREADME.md
performance
guardrails.py
physics
__init__.py_helpers.pycalibration.pycanonical.pycell.pyclassical_mechanics.pyconservation_gauge_unification.pyconservation.pydissipative_conservation.pyemergent_chemistry.pyemergent_particles.pyextended.pyfields.pygauge.pyintegrity.pyinteractions.pylife.pylyapunov.pypatterns.pyphase_transition.pyquantum_mechanics.pyREADME.mdsignatures.pyspectral_conservation.pyspectral_metrics.pystructural_diffusion.pysymplectic_substrate.pytelemetry.pyunified.pyvariational.pyvectorized_ops.py
primality
__init__.py
recipes
__init__.pycookbook.pyREADME.md
riemann
__init__.pyadmissible_family_sweep.pyadmissible_rescaling.pyaggregates_closure_signature.pyalpha_sweep.pyanalytic_continuation_dirichlet.pyanalytic_continuation.pycoercivity_uniform.pycoupling_weights_type_signature.pycurrents_closure_signature.pydelta_phi_max_type_signature.pydirichlet_l.pydnfr_type_signature.pyepi_type_signature.pyhilbert_polya.pyli_keiper.pylyapunov_spectral_positivity.pynodal_pulse.pynodeaware_gauge_sweep.pynuf_type_signature.pyoperator_catalog_discipline_signature.pyoperator.pyoscillatory_correction.pypaley_gap_coercivity.pyphi_type_signature.pyprime_ladder_hamiltonian.pypulse_coherence.pyremesh_infinity_residue_split.pyremesh_window_type_signature.pyspectral_emergence.pystructural_zero_density.pytelemetry.pytetrad_closure_signature.pytwisted_admissible_family_sweep.pytwisted_admissible_rescaling.pytwisted_alpha_sweep.pytwisted_coercivity_uniform.pytwisted_hermite_family.pytwisted_hilbert_polya.pytwisted_li_keiper.pytwisted_lyapunov_spectral_positivity.pytwisted_nodeaware_gauge_sweep.pytwisted_oscillatory_correction.pytwisted_paley_gap_coercivity.pytwisted_prime_ladder_hamiltonian.pytwisted_spectral_emergence.pytwisted_structural_zero_density.pytwisted_weil_explicit_formula.pytwisted_weil_positivity.pyurules_consistency_signature.pyvon_mangoldt.pyweil_explicit_formula.pyweil_positivity.py
schemas
__init__.pygrammar.jsonREADME.md
sdk
__init__.py__init__.pyiadaptive_system.pyadaptive_system.pyibuilders.pybuilders.pyifluent.pyfluent.pyiREADME.mdself_opt.pysimple.pytemplates.pytemplates.pyiutils.py
security
__init__.pycrypto.pydatabase.pyREADME.mdsubprocess.pyvalidation.py
sequencing
__init__.pypatterns.pyREADME.md
services
__init__.pyorchestrator.pyREADME.md
sparse
__init__.pyREADME.mdrepresentations.py
structural
README.md
telemetry
__init__.pycache_metrics.pycache_metrics.pyiconstants.pynu_f.pynu_f.pyiREADME.mdunified_telemetry_system.pyverbosity.pyverbosity.pyi
tools
__init__.pydomain_templates.pyREADME.mdsequence_generator.pytnfr_is_prime_cli_optimized.pytnfr_is_prime_cli.py
topology
__init__.pyasymmetry.pyREADME.md
utils
cache_layers.pycache.pycache.pyicallbacks.pycallbacks.pyichunks.pychunks.pyidata.pydata.pyifast_diameter.pygraph.pygraph.pyiinit.pyinit.pyiio.pyio.pyinumeric.pynumeric.pyiREADME.mdtopology.pyunified_cache.py
validation
__init__.py__init__.pyiaggregator.pybase.pycompatibility.pycompatibility.pyiconfig.pygraph.pygraph.pyihealth.pyinput_validation.pyinterface_baselines.pyinvariants.pymultichannel_interface.pyphase_gate.pyREADME.mdrules.pyrules.pyiruntime.pyruntime.pyisequence_validator.pysignal_confrontation.pysoft_filters.pysoft_filters.pyispectral.pyspectral.pyistructural_interface.pytemporal_interface.pyunified_validation_system.pyvalidator.pywindow.pywindow.pyi
visualization
__init__.pycascade_viz.pyhierarchy.pyREADME.mdsequence_plotter.py
yang_mills
__init__.pyclosure.pyderivability.pyscaling.pystructural_gap.pyu6_sweep.py
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tetrad_evaluator.py
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FILE: examples/07_number_theory/148_capacity_arm_carries_von_mangoldt.py

148_capacity_arm_carries_von_mangoldt.py

Example 148 — The Capacity Arm of the Dual-Lever Carries von Mangoldt: the Riemann Zeros Live on the Axis the Substrate Does Not Encode

Examples 146-147 read the operator GRAMMAR as a lens on number theory: primes are the grammatical kernel (ΔNFR=0), and the dual-lever (examples 37/130/147) restricted to arithmetic is the two canonical additive gradings of the free monoid on primes — COUNT Ω (→ the ΔNFR pressure channel) and SIZE log n (→ the νf capacity channel, example 94's atom νf = log p). This example asks the user's key question through that lens: WHICH arm of the dual-lever carries the arithmetic difficulty — the Riemann zeros — and why is the per-node substrate blind to it?

The exact answer

The Riemann difficulty lives entirely on the CAPACITY arm (log = νf), and the per-node substrate encodes the PRESSURE arm (ΔNFR ← Ω), so it is structurally blind to it. Two classical identities, read through the dual-lever, make this exact:

(1) log n = Σ_{d|n} Λ(d) (Möbius-inverse: Λ = μ * log). The CAPACITY grading log n IS the divisor-sum of the von Mangoldt function Λ. So von Mangoldt — and its summatory ψ(x) = Σ_{n≤x} Λ(n), the Chebyshev staircase whose oscillatory residue is S(T) = (1/π)arg ζ(½+iT) (example 96, the sole open obstruction of the TNFR-Riemann program) — sits on the capacity arm.

(2) Σ_n Λ(n) n^{-s} = −ζ'/ζ(s) (P12, the prime-ladder / von Mangoldt zeta). This Dirichlet series has a SIMPLE POLE AT EVERY ZERO ρ of ζ (ζ in the DENOMINATOR). The capacity arm literally has the Riemann zeros as the poles of its generating series. By contrast Σ_n Ω(n) n^{-s} = ζ(s)·P(s) has ζ in the NUMERATOR, so a zero of ζ is a ZERO of the Ω series — the PRESSURE arm does not see the zeros as poles at all.

Why the substrate is blind (the synthesis)

The per-node symplectic substrate encodes the PRESSURE channel: Φ_s is built from the distance-weighted ΔNFR distribution, and on arithmetic nodes ΔNFR is the count-graded primality pressure (Ω). The substrate therefore encodes the SMOOTH, zero-free arm (Ω obeys the Erdős–Kac Gaussian CLT, not a zero-driven oscillation) and is structurally blind to the CAPACITY/von-Mangoldt arm where the zeros live. This is the SAME Fix(G)^⊥ blindness measured in examples 103/116/120: the arithmetic the substrate cannot see is exactly the capacity-arm arithmetic, and the Riemann residual S(T) ∈ ker(R∞) ∩ Fix(S_n)^⊥ is the capacity arm's oscillatory half.

Doctrine compliance

The arithmetic constants and the per-node ΔNFR are canonical (ArithmeticTNFRFormalism). The identities log = Λ*1 and −ζ'/ζ = Σ Λ n^{-s} are classical (Dirichlet convolution; P12 is the latter's TNFR prime-ladder form). Nothing is imposed; the pole structure is measured against mpmath's ζ.

Three measured results

M1 THE CAPACITY ARM IS THE VON MANGOLDT SUM. log n = Σ_{d|n} Λ(d) exactly (residual ~1e-16 over [2,200]); equivalently Λ = μ * log. The size grading log (the νf arm of the dual-lever, ex 147) IS the divisor-sum of von Mangoldt, so ψ(x) = Σ Λ — the Chebyshev staircase carrying S(T) (ex 96) — is the capacity arm's summatory.

M2 THE ZEROS ARE THE POLES OF THE CAPACITY SERIES. −ζ'/ζ(s) = Σ Λ(n) n^{-s} (P12) blows up as a simple pole (residue 1) at the first zero ρ_1 = ½+14.1347i: |−ζ'/ζ(ρ_1+ε)| ≈ 1/ε (measured 9.6, 49.6, 249.6 at ε = 0.1, 0.02, 0.004). The Ω series Σ Ω(n) n^{-s} = ζ(s)·P(s) has ζ in the numerator, so the zeros are invisible to the pressure arm.

M3 THE PRESSURE ARM IS SMOOTH; THE SUBSTRATE ENCODES IT, HENCE IS BLIND. The pressure grading Ω obeys the Erdős–Kac CLT: (Ω(n)−loglog n)/√(loglog n) is Gaussian (measured spread ≈ 1.13 over [3,10^5]; convergence is slow because the scale loglog n ≈ 2.4 is tiny, but the law is a Gaussian CLT, not a zero-driven oscillation). The per-node substrate encodes this pressure arm (Φ_s ← ΔNFR ← Ω), so it is structurally blind to the capacity/von-Mangoldt arm where the zeros live — the Fix(G)^⊥ blindness of ex 103/116/120.

Honest scope

log = Λ1, Λ = μlog, and −ζ'/ζ = Σ Λ n^{-s} (with poles at the zeros) are CLASSICAL facts of analytic number theory (P12 is the TNFR prime-ladder form of the last). The NEW content is the dual-lever reading: it LOCALISES the Riemann zeros on the capacity (νf/log) arm and the smooth primality structure on the pressure (ΔNFR/Ω) arm, and it EXPLAINS the substrate blindness (ex 103/116/120) structurally — the substrate encodes pressure, the zeros live on capacity. This does NOT prove or advance RH (G4 stays open; the residual S(T) ∈ Fix(S_n)^⊥ remains unreachable, the TNFR-Riemann program stays PAUSED at T-HP). It locates the wall on the dual-lever axis the substrate does not encode — a sharper statement of where the obstruction lives, not a closure.

References

  • theory/TNFR_NUMBER_THEORY.md §4-§8 (primality field, dual-lever)
  • examples/07_number_theory/147_numbers_as_free_monoid_words.py (the two gradings)
  • examples/07_number_theory/96_spectral_vibration_of_coherence.py (S(T))
  • examples/07_number_theory/95_primes_from_spectral_waves.py (psi(x) explicit formula)
  • examples/08_emergent_geometry/116_nuf_emergent_prime_visibility.py (substrate blindness)
  • src/tnfr/riemann/von_mangoldt.py (P12, the von Mangoldt prime-ladder zeta)
  • theory/TNFR_RIEMANN_RESEARCH_NOTES.md §13septies (T-HP, the open wall)
  • AGENTS.md "REMESH-∞ Closure" (range/ker R∞, smooth vs oscillatory halves)

Source Code

python
#!/usr/bin/env python3
"""
Example 148 — The Capacity Arm of the Dual-Lever Carries von Mangoldt: the
Riemann Zeros Live on the Axis the Substrate Does Not Encode
==============================================================================

Examples 146-147 read the operator GRAMMAR as a lens on number theory: primes
are the grammatical kernel (ΔNFR=0), and the dual-lever (examples 37/130/147)
restricted to arithmetic is the two canonical additive gradings of the free
monoid on primes — COUNT Ω (→ the ΔNFR pressure channel) and SIZE log n (→ the νf
capacity channel, example 94's atom νf = log p). This example asks the user's key
question through that lens: WHICH arm of the dual-lever carries the arithmetic
difficulty — the Riemann zeros — and why is the per-node substrate blind to it?

The exact answer
----------------
The Riemann difficulty lives entirely on the CAPACITY arm (log = νf), and the
per-node substrate encodes the PRESSURE arm (ΔNFR ← Ω), so it is structurally
blind to it. Two classical identities, read through the dual-lever, make this
exact:

  (1) log n = Σ_{d|n} Λ(d)   (Möbius-inverse: Λ = μ * log).
      The CAPACITY grading log n IS the divisor-sum of the von Mangoldt function
      Λ. So von Mangoldt — and its summatory ψ(x) = Σ_{n≤x} Λ(n), the Chebyshev
      staircase whose oscillatory residue is S(T) = (1/π)arg ζ(½+iT) (example 96,
      the sole open obstruction of the TNFR-Riemann program) — sits on the
      capacity arm.

  (2) Σ_n Λ(n) n^{-s} = −ζ'/ζ(s)   (P12, the prime-ladder / von Mangoldt zeta).
      This Dirichlet series has a SIMPLE POLE AT EVERY ZERO ρ of ζ (ζ in the
      DENOMINATOR). The capacity arm literally has the Riemann zeros as the poles
      of its generating series. By contrast Σ_n Ω(n) n^{-s} = ζ(s)·P(s) has ζ in
      the NUMERATOR, so a zero of ζ is a ZERO of the Ω series — the PRESSURE arm
      does not see the zeros as poles at all.

Why the substrate is blind (the synthesis)
-------------------------------------------
The per-node symplectic substrate encodes the PRESSURE channel: Φ_s is built from
the distance-weighted ΔNFR distribution, and on arithmetic nodes ΔNFR is the
count-graded primality pressure (Ω). The substrate therefore encodes the SMOOTH,
zero-free arm (Ω obeys the Erdős–Kac Gaussian CLT, not a zero-driven oscillation)
and is structurally blind to the CAPACITY/von-Mangoldt arm where the zeros live.
This is the SAME Fix(G)^⊥ blindness measured in examples 103/116/120: the
arithmetic the substrate cannot see is exactly the capacity-arm arithmetic, and
the Riemann residual S(T) ∈ ker(R∞) ∩ Fix(S_n)^⊥ is the capacity arm's
oscillatory half.

Doctrine compliance
-------------------
The arithmetic constants and the per-node ΔNFR are canonical
(ArithmeticTNFRFormalism). The identities log = Λ*1 and −ζ'/ζ = Σ Λ n^{-s} are
classical (Dirichlet convolution; P12 is the latter's TNFR prime-ladder form).
Nothing is imposed; the pole structure is measured against mpmath's ζ.

Three measured results
----------------------
M1 THE CAPACITY ARM IS THE VON MANGOLDT SUM. log n = Σ_{d|n} Λ(d) exactly
   (residual ~1e-16 over [2,200]); equivalently Λ = μ * log. The size grading log
   (the νf arm of the dual-lever, ex 147) IS the divisor-sum of von Mangoldt, so
   ψ(x) = Σ Λ — the Chebyshev staircase carrying S(T) (ex 96) — is the capacity
   arm's summatory.

M2 THE ZEROS ARE THE POLES OF THE CAPACITY SERIES. −ζ'/ζ(s) = Σ Λ(n) n^{-s} (P12)
   blows up as a simple pole (residue 1) at the first zero ρ_1 = ½+14.1347i:
   |−ζ'/ζ(ρ_1+ε)| ≈ 1/ε (measured 9.6, 49.6, 249.6 at ε = 0.1, 0.02, 0.004). The
   Ω series Σ Ω(n) n^{-s} = ζ(s)·P(s) has ζ in the numerator, so the zeros are
   invisible to the pressure arm.

M3 THE PRESSURE ARM IS SMOOTH; THE SUBSTRATE ENCODES IT, HENCE IS BLIND. The
   pressure grading Ω obeys the Erdős–Kac CLT: (Ω(n)−loglog n)/√(loglog n) is
   Gaussian (measured spread ≈ 1.13 over [3,10^5]; convergence is slow because
   the scale loglog n ≈ 2.4 is tiny, but the law is a Gaussian CLT, not a
   zero-driven oscillation). The per-node substrate encodes this pressure arm
   (Φ_s ← ΔNFR ← Ω), so it is structurally blind to the capacity/von-Mangoldt
   arm where the zeros live — the Fix(G)^⊥ blindness of ex 103/116/120.

Honest scope
------------
log = Λ*1, Λ = μ*log, and −ζ'/ζ = Σ Λ n^{-s} (with poles at the zeros) are
CLASSICAL facts of analytic number theory (P12 is the TNFR prime-ladder form of
the last). The NEW content is the dual-lever reading: it LOCALISES the Riemann
zeros on the capacity (νf/log) arm and the smooth primality structure on the
pressure (ΔNFR/Ω) arm, and it EXPLAINS the substrate blindness (ex 103/116/120)
structurally — the substrate encodes pressure, the zeros live on capacity. This
does NOT prove or advance RH (G4 stays open; the residual S(T) ∈ Fix(S_n)^⊥
remains unreachable, the TNFR-Riemann program stays PAUSED at T-HP). It locates
the wall on the dual-lever axis the substrate does not encode — a sharper
statement of where the obstruction lives, not a closure.

References
----------
- theory/TNFR_NUMBER_THEORY.md §4-§8 (primality field, dual-lever)
- examples/07_number_theory/147_numbers_as_free_monoid_words.py (the two gradings)
- examples/07_number_theory/96_spectral_vibration_of_coherence.py (S(T))
- examples/07_number_theory/95_primes_from_spectral_waves.py (psi(x) explicit formula)
- examples/08_emergent_geometry/116_nuf_emergent_prime_visibility.py (substrate blindness)
- src/tnfr/riemann/von_mangoldt.py (P12, the von Mangoldt prime-ladder zeta)
- theory/TNFR_RIEMANN_RESEARCH_NOTES.md §13septies (T-HP, the open wall)
- AGENTS.md "REMESH-∞ Closure" (range/ker R∞, smooth vs oscillatory halves)
"""

import math
import os
import statistics
import sys

sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "..", "src"))

import mpmath as mp
import sympy as sp


def von_mangoldt(n):
    """Lambda(n) = log p if n = p^k (a prime power), else 0."""
    factorisation = sp.factorint(n)
    if len(factorisation) == 1:
        prime = next(iter(factorisation))
        return math.log(prime)
    return 0.0


def experiment_1_capacity_is_von_mangoldt():
    print("=" * 72)
    print("M1: the CAPACITY arm log n = sum_{d|n} Lambda(d) (von Mangoldt sum)")
    print("=" * 72)
    max_resid = 0.0
    for n in range(2, 201):
        lhs = math.log(n)
        rhs = sum(von_mangoldt(d) for d in sp.divisors(n))
        max_resid = max(max_resid, abs(lhs - rhs))
    print(f"  max |log n - sum_d|n Lambda(d)| over [2,200] = {max_resid:.2e}")
    print("  equivalently (Mobius inverse) Lambda(n) = sum_{d|n} mu(d) log(n/d).")
    print("  sample Lambda(n) (supported on prime powers, weight log p):")
    for n in (2, 4, 6, 8, 9, 12, 16, 30):
        lam = von_mangoldt(n)
        kind = "prime power" if lam > 0 else "several primes"
        print(f"    n={n:3d}  Lambda={lam:.4f}  ({kind})")
    print("  -> the capacity grading log (the nu_f arm, ex 147) IS the divisor-sum")
    print("     of von Mangoldt; psi(x)=sum Lambda carries S(T) (ex 96).")


def experiment_2_zeros_are_the_poles():
    print()
    print("=" * 72)
    print("M2: the Riemann ZEROS are the POLES of the capacity series -zeta'/zeta")
    print("=" * 72)
    mp.mp.dps = 25
    gamma1 = mp.zetazero(1).imag
    print(f"  first nontrivial zero: rho_1 = 0.5 + {float(gamma1):.4f} i")
    print(
        "  von Mangoldt Dirichlet series = -zeta'/zeta(s) = sum Lambda(n) n^-s (P12):"
    )
    for eps in (0.1, 0.02, 0.004):
        s = mp.mpf("0.5") + eps + 1j * gamma1
        vm = -mp.zeta(s, derivative=1) / mp.zeta(s)
        print(
            f"    s = rho_1 + {float(eps):.3f}:  |-zeta'/zeta(s)| = "
            f"{float(abs(vm)):8.2f}   (~ 1/eps -> simple POLE, residue 1)"
        )
    print("  -> the CAPACITY series has a simple pole at every zero of zeta.")
    print("  contrast: sum_n Omega(n) n^-s = zeta(s)*P(s) has zeta in the")
    print("  NUMERATOR, so a zero of zeta is a ZERO of the Omega series -- the")
    print("  PRESSURE arm does NOT see the zeros as poles.")


def experiment_3_pressure_smooth_substrate_blind():
    print()
    print("=" * 72)
    print("M3: the PRESSURE arm is smooth (Erdos-Kac); the substrate encodes it")
    print("    and is therefore blind to the capacity/von-Mangoldt arithmetic")
    print("=" * 72)
    limit = 100000
    spf = list(range(limit + 1))
    for i in range(2, int(limit**0.5) + 1):
        if spf[i] == i:
            for j in range(i * i, limit + 1, i):
                if spf[j] == j:
                    spf[j] = i
    zs = []
    for n in range(3, limit + 1):
        m, count = n, 0
        while m > 1:
            m //= spf[m]
            count += 1
        ll = math.log(math.log(n))
        if ll > 0:
            zs.append((count - ll) / math.sqrt(ll))
    spread = statistics.pstdev(zs)
    within2 = sum(1 for z in zs if abs(z) <= 2) / len(zs)
    print(f"  Omega via (Omega(n)-loglog n)/sqrt(loglog n) over [3,{limit}]:")
    print(
        f"    spread {spread:.3f} (Gaussian N(0,1): 1.0), within 2-sigma "
        f"{within2:.3f} (0.954)"
    )
    print("    (convergence is slow -- the scale loglog n is only ~2.4 -- but the")
    print("     law is a Gaussian CLT, NOT a zero-driven oscillation).")
    print("  SYNTHESIS: the per-node substrate encodes the PRESSURE arm")
    print("    (Phi_s <- dNFR <- Omega), the smooth zero-free side. It is")
    print("    structurally BLIND to the CAPACITY/von-Mangoldt arm where the")
    print("    Riemann zeros live -- the Fix(G)^perp blindness of ex 103/116/120.")
    print("    S(T) in ker(R_inf) cap Fix(S_n)^perp is the capacity arm's")
    print("    oscillatory half; the wall lives on the axis the substrate omits.")


def main():
    print()
    print("#" * 72)
    print("# Example 148 - The Capacity Arm Carries von Mangoldt: the Riemann")
    print("#               Zeros Live Where the Substrate Is Blind")
    print("#" * 72)
    print()
    experiment_1_capacity_is_von_mangoldt()
    experiment_2_zeros_are_the_poles()
    experiment_3_pressure_smooth_substrate_blind()
    print()
    print("=" * 72)
    print("Summary")
    print("=" * 72)
    print("  The dual-lever splits the arithmetic by smoothness. CAPACITY (log =")
    print("  nu_f) is the von Mangoldt sum (log = Lambda*1), and its Dirichlet")
    print("  series -zeta'/zeta has poles at the Riemann zeros -- the wall S(T)")
    print("  lives here. PRESSURE (Omega = dNFR) is the smooth Erdos-Kac side")
    print("  (zeta in the numerator, zeros invisible). The per-node substrate")
    print("  encodes pressure, so it is structurally blind to the capacity arm")
    print("  where the zeros live -- the Fix(G)^perp blindness, now LOCATED on")
    print("  the dual-lever axis. Classical identities read through the lens; no")
    print("  RH advance, the program stays paused at T-HP.")
    print()


if __name__ == "__main__":
    main()