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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
__init__.py__init__.pyi_compat.py_version.py_version.pyialias.pyalias.pyibackend_config.pycache.pycache.pyiexecution.pyexecution.pyiflatten.pyflatten.pyigamma.pygamma.pyiglyph_history.pyglyph_history.pyiglyph_runtime.pyglyph_runtime.pyiimmutable.pyimmutable.pyiinitialization.pyinitialization.pyiio.pyio.pyilocking.pylocking.pyinode.pynode.pyiobservers.pyobservers.pyiontosim.pyontosim.pyipy.typedrng.pyrng.pyisecure_config.pyselector.pyselector.pyisense.pysense.pyistructural.pystructural.pyitokens.pytokens.pyitrace.pytrace.pyitypes.pytypes.pyiunits.pyunits.pyi
tetrad_evaluator.py
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FILE: benchmarks/inverse_spectrum_to_symmetry.py

inverse_spectrum_to_symmetry.py

Inverse Falsifier: Spectrum -> Symmetry Group -> Predict an UNMEASURED Degeneracy

The forward harness (emergent_integers_symmetry.py) showed that the integers emerging as structural-Laplacian multiplicities match the irreducible-representation (irrep) dimensions of the manifold's symmetry group. That is necessary but weak: a skeptic can say "you matched a template you already knew."

This harness runs the HARD test — a genuine out-of-sample prediction:

text
1. OBSERVE only the low modes of a manifold (hide the rest).
2. INFER the symmetry group from the partial multiplicity fingerprint alone.
3. PREDICT, from pure group theory, a degeneracy that was NOT in the observed
   data (an irrep dimension the low modes never revealed).
4. REVEAL the hidden modes (or a held-out sibling manifold) and check whether
   the predicted integer actually appears.

If the prediction lands, the integer was dictated by structure, not fitted — the claim "TNFR explains what an integer IS (a structural invariant)" survives a real falsification attempt. If it fails, the earlier match was a template artifact.

HEADLINE CASE (the cleanest, least circular): The icosahedral rotation group I has irreps of dimensions {1, 3, 3, 4, 5} (sum of squares 1+9+9+16+25 = 60 = |I|). But the 12-vertex icosahedron graph decomposes as 12 = 1+3+5+3 and NEVER exhibits a multiplicity of 4. So:

text
  observe icosahedron low modes [1, 3, 5]
    -> the 5 forces group = I (no smaller point group has a 5D irrep)
    -> group theory: I HAS a 4D irrep (the "G" representation)
    -> PREDICT a degeneracy of 4 must appear in this symmetry family,
       even though the icosahedron itself never shows it.
  verify on the held-out dodecahedron (dual polyhedron, same group I_h):
    20 = 1+3+5+4+4+3  -> the 4 APPEARS.  Prediction confirmed.

The integer 4 was absent from the input and predicted from structure alone.

IRREDUCIBLE vs COMPOSITE DEGENERACY (the deeper finding): A naive "ceiling" prediction (no multiplicity above the max irrep dim) is FALSE: the truncated cube (octahedral, max irrep dim 3) shows a 5-fold degeneracy, because 5 = 2 + 3 is an ACCIDENTAL coincidence of a 2D and a 3D irrep. So emergent degeneracies split into two kinds: - PROTECTED (irreducible rep): multiplicity = a single irrep dimension, symmetry-forced, stable. <chi,chi> = 1. - ACCIDENTAL (reducible / direct sum): multiplicity = a SUM of irrep dims, not symmetry-forced. <chi,chi> = number of irreps in the sum > 1. We verify this with the representation-theoretic inner product <chi,chi> computed from the graph's automorphism group: the icosahedron's 5 is irreducible (protected); the truncated cube's 5 is reducible (2 + 3, accidental). This is the rep-theory notion of irreducibility — the "indivisible building block" idea, realized for degeneracies (the same irreducible/composite intuition that underlies primes).

SHARP EXCLUSION that DOES hold: the icosahedral group has no 2D irrep, so an icosahedral manifold shows NO generic 2-fold degeneracy. Verified on the icosahedron and the dodecahedron.

HONEST SCOPE: The predictive engine is any Aut(G)-equivariant operator commuting with Aut(G) (a known theorem); TNFR supplies the physical reading: the emergent L_rw = I - D^-1 W is the discrete ΔNFR / phase curvature (D - A shares its eigenspaces on these vertex-transitive graphs). We predict CARDINALS (degeneracies), not the arithmetic ring. This does not derive (+, ×) or primality. It does demonstrate that the emergent integers carry, and let us predict, structural facts we did not put in.

Run: python benchmarks/inverse_spectrum_to_symmetry.py

Theoretical anchor: AGENTS.md (nodal equation; discrete-mode regime; the emergent L_rw = I - D^-1 W as discrete ΔNFR). Status: RESEARCH (inverse falsifier).

Source Code

python
"""
Inverse Falsifier: Spectrum -> Symmetry Group -> Predict an UNMEASURED Degeneracy
=================================================================================

The forward harness (``emergent_integers_symmetry.py``) showed that the integers
emerging as structural-Laplacian multiplicities match the irreducible-representation
(irrep) dimensions of the manifold's symmetry group. That is necessary but weak:
a skeptic can say "you matched a template you already knew."

This harness runs the HARD test — a genuine out-of-sample prediction:

    1. OBSERVE only the low modes of a manifold (hide the rest).
    2. INFER the symmetry group from the partial multiplicity fingerprint alone.
    3. PREDICT, from pure group theory, a degeneracy that was NOT in the observed
       data (an irrep dimension the low modes never revealed).
    4. REVEAL the hidden modes (or a held-out sibling manifold) and check whether
       the predicted integer actually appears.

If the prediction lands, the integer was dictated by structure, not fitted —
the claim "TNFR explains what an integer IS (a structural invariant)" survives a
real falsification attempt. If it fails, the earlier match was a template artifact.

HEADLINE CASE (the cleanest, least circular):
  The icosahedral rotation group I has irreps of dimensions {1, 3, 3, 4, 5}
  (sum of squares 1+9+9+16+25 = 60 = |I|). But the 12-vertex icosahedron graph
  decomposes as 12 = 1+3+5+3 and NEVER exhibits a multiplicity of 4. So:

      observe icosahedron low modes [1, 3, 5]
        -> the 5 forces group = I (no smaller point group has a 5D irrep)
        -> group theory: I HAS a 4D irrep (the "G" representation)
        -> PREDICT a degeneracy of 4 must appear in this symmetry family,
           even though the icosahedron itself never shows it.
      verify on the held-out dodecahedron (dual polyhedron, same group I_h):
        20 = 1+3+5+4+4+3  -> the 4 APPEARS.  Prediction confirmed.

  The integer 4 was absent from the input and predicted from structure alone.

IRREDUCIBLE vs COMPOSITE DEGENERACY (the deeper finding):
  A naive "ceiling" prediction (no multiplicity above the max irrep dim) is FALSE:
  the truncated cube (octahedral, max irrep dim 3) shows a 5-fold degeneracy,
  because 5 = 2 + 3 is an ACCIDENTAL coincidence of a 2D and a 3D irrep. So
  emergent degeneracies split into two kinds:
    - PROTECTED  (irreducible rep): multiplicity = a single irrep dimension,
      symmetry-forced, stable.  <chi,chi> = 1.
    - ACCIDENTAL (reducible / direct sum): multiplicity = a SUM of irrep dims,
      not symmetry-forced.  <chi,chi> = number of irreps in the sum > 1.
  We verify this with the representation-theoretic inner product <chi,chi>
  computed from the graph's automorphism group: the icosahedron's 5 is
  irreducible (protected); the truncated cube's 5 is reducible (2 + 3,
  accidental). This is the rep-theory notion of irreducibility — the
  "indivisible building block" idea, realized for degeneracies (the same
  irreducible/composite intuition that underlies primes).

  SHARP EXCLUSION that DOES hold: the icosahedral group has no 2D irrep, so an
  icosahedral manifold shows NO generic 2-fold degeneracy. Verified on the
  icosahedron and the dodecahedron.

HONEST SCOPE:
  The predictive engine is any Aut(G)-equivariant operator commuting with Aut(G)
  (a known theorem); TNFR supplies the physical reading: the emergent
  L_rw = I - D^-1 W is the discrete ΔNFR / phase curvature (D - A shares its
  eigenspaces on these vertex-transitive graphs). We predict
  CARDINALS (degeneracies), not the arithmetic ring. This does not derive (+, ×)
  or primality. It does demonstrate that the emergent integers carry, and let us
  predict, structural facts we did not put in.

Run:
    python benchmarks/inverse_spectrum_to_symmetry.py

Theoretical anchor: AGENTS.md (nodal equation; discrete-mode regime; the emergent
L_rw = I - D^-1 W as discrete ΔNFR). Status: RESEARCH (inverse falsifier).
"""

from __future__ import annotations

from dataclasses import dataclass

import networkx as nx
import numpy as np

# ---------------------------------------------------------------------------
# Emergent integers: eigenvalue multiplicities of the canonical emergent operator
# L_rw = I - D^-1 W (the discrete ΔNFR); on vertex-transitive graphs these equal
# the multiplicities of the imposed D - A used below (operator-invariant).
# ---------------------------------------------------------------------------


def laplacian_multiplicities(
    G: nx.Graph, *, tol: float = 1e-6
) -> list[tuple[float, int]]:
    """Return (eigenvalue, multiplicity) pairs of the canonical EMERGENT operator
    L_sym (self-adjoint twin of the ΔNFR random-walk L_rw), ascending. On
    vertex-transitive graphs L_sym shares D - A's eigenspaces, so the
    multiplicities (the emergent integers) are operator-invariant; the geometry
    read is now emergent, not imposed."""
    from tnfr.physics.structural_diffusion import symmetric_normalized_laplacian

    G = nx.Graph(G)
    _, L_sym = symmetric_normalized_laplacian(G)
    evals = np.sort(np.linalg.eigvalsh(L_sym))
    groups: list[list[float]] = [[float(evals[0])]]
    for ev in evals[1:]:
        if abs(ev - groups[-1][-1]) <= tol:
            groups[-1].append(float(ev))
        else:
            groups.append([float(ev)])
    return [(float(np.mean(g)), len(g)) for g in groups]


# ---------------------------------------------------------------------------
# Group inference from a partial multiplicity fingerprint (rep-theory table)
# ---------------------------------------------------------------------------

# Rotation point groups relevant to the polyhedral manifolds, with the full
# multiset of irreducible-representation dimensions (independent ground truth).
IRREP_DIMS: dict[str, list[int]] = {
    "C/D (cyclic/dihedral)": [1, 1, 2],  # dims that occur: {1,2}
    "T (tetrahedral)": [1, 1, 1, 3],  # |T| = 12
    "O (octahedral)": [1, 1, 2, 3, 3],  # |O| = 24
    "I (icosahedral)": [1, 3, 3, 4, 5],  # |I| = 60
}


def allowed_dims(group: str) -> set[int]:
    return set(IRREP_DIMS[group])


def infer_group(observed_mults: set[int]) -> str:
    """Infer the minimal symmetry group consistent with the observed nontrivial
    multiplicities. The inference uses ONLY the observed integers.
    """
    nz = {m for m in observed_mults if m > 1}
    if 5 in nz or 4 in nz:
        return "I (icosahedral)"  # only icosahedral has dims 4 or 5
    if 3 in nz and 2 in nz:
        return "O (octahedral)"  # 2 and 3 together -> octahedral
    if 3 in nz:
        return "T (tetrahedral)"  # 3 without 2 -> tetrahedral
    if 2 in nz:
        return "C/D (cyclic/dihedral)"
    return "C/D (cyclic/dihedral)"


# ---------------------------------------------------------------------------
# Prediction primitives
# ---------------------------------------------------------------------------


@dataclass(frozen=True)
class Prediction:
    inferred_group: str
    observed: list[int]
    predicted_existing: set[int]  # irrep dims the group HAS but we have not seen
    forbidden_above: int  # no multiplicity may exceed this


def make_prediction(observed_seq: list[int]) -> Prediction:
    group = infer_group(set(observed_seq))
    dims = allowed_dims(group)
    seen = {m for m in observed_seq}
    predicted_existing = {d for d in dims if d > 1 and d not in seen}
    return Prediction(
        inferred_group=group,
        observed=observed_seq,
        predicted_existing=predicted_existing,
        forbidden_above=max(dims),
    )


def low_modes(G: nx.Graph, n_groups: int) -> list[int]:
    """Reveal only the multiplicities of the first ``n_groups`` distinct
    eigenvalues (the lowest structural modes). The rest stay hidden."""
    return [m for _ev, m in laplacian_multiplicities(G)][:n_groups]


def full_modes(G: nx.Graph) -> list[int]:
    return [m for _ev, m in laplacian_multiplicities(G)]


# ---------------------------------------------------------------------------
# Representation-theoretic irreducibility test (protected vs accidental)
# ---------------------------------------------------------------------------


def automorphism_matrices(G: nx.Graph, *, limit: int = 5000) -> list[np.ndarray]:
    """All graph automorphisms of G as permutation matrices (capped at ``limit``).

    Aut(G) is exactly the symmetry group with which L = D - A commutes; averaging
    over it gives the rep-theory inner product used to detect irreducibility.
    """
    from networkx.algorithms.isomorphism import GraphMatcher

    G = nx.Graph(G)
    nodes = sorted(G.nodes())
    idx = {v: i for i, v in enumerate(nodes)}
    n = len(nodes)
    mats: list[np.ndarray] = []
    for mapping in GraphMatcher(G, G).isomorphisms_iter():
        M = np.zeros((n, n))
        for src, dst in mapping.items():
            M[idx[dst], idx[src]] = 1.0
        mats.append(M)
        if len(mats) >= limit:
            break
    return mats


def eigenspace_irreducibility(
    G: nx.Graph, *, tol: float = 1e-5
) -> list[tuple[float, int, float]]:
    """For each Laplacian eigenspace return (eigenvalue, multiplicity, <chi,chi>).

    <chi,chi> = (1/|Aut|) Σ_g |trace(P_λ · M_g)|² counts the irreducible
    representations inside the eigenspace: 1 => irreducible (symmetry-protected);
    k>1 => reducible (an accidental sum of k irreps).
    """
    G = nx.Graph(G)
    nodes = sorted(G.nodes())
    A = nx.to_numpy_array(G, nodelist=nodes)
    L = np.diag(A.sum(axis=1)) - A
    evals, evecs = np.linalg.eigh(L)
    mats = automorphism_matrices(G)
    order = len(mats)

    groups: list[list[int]] = [[0]]
    for i in range(1, len(evals)):
        if abs(evals[i] - evals[groups[-1][-1]]) <= tol:
            groups[-1].append(i)
        else:
            groups.append([i])

    out: list[tuple[float, int, float]] = []
    for grp in groups:
        U = evecs[:, grp]  # n x d, orthonormal columns
        P = U @ U.T  # projector onto the eigenspace
        s = sum(float(np.trace(P @ M)) ** 2 for M in mats)
        out.append((float(evals[grp[0]]), len(grp), s / order))
    return out


# ---------------------------------------------------------------------------
# Experiments
# ---------------------------------------------------------------------------


def _rule(title: str) -> None:
    print("\n" + "=" * 78)
    print(title)
    print("=" * 78)


def headline_icosahedral_prediction() -> bool:
    """Predict the unmeasured '4' from the icosahedron's low modes; verify on
    the held-out dodecahedron AND on the icosahedron's own hidden modes."""
    _rule("HEADLINE — predict an unmeasured degeneracy (the hidden '4')")

    ico = nx.icosahedral_graph()
    observed = low_modes(ico, 3)  # reveal only [1, 3, 5]
    pred = make_prediction(observed)
    print(f"  observed (icosahedron, low modes only): {observed}")
    print(f"  inferred group (from the 5)           : {pred.inferred_group}")
    print(
        f"  group-theory irrep dims               : "
        f"{sorted(allowed_dims(pred.inferred_group))}"
    )
    print(
        f"  PREDICTION: a degeneracy in {sorted(pred.predicted_existing)} must exist in"
    )
    print("              this symmetry family, though unseen in the input.")
    print(f"  PREDICTION: no multiplicity will ever exceed {pred.forbidden_above}.")

    # Verify on the held-out dodecahedron (dual polyhedron, same group I_h).
    dodeca_full = full_modes(nx.dodecahedral_graph())
    dodeca_set = set(dodeca_full)
    print(f"\n  held-out dodecahedron full spectrum   : {dodeca_full}")
    four_appears = 4 in dodeca_set
    no_excess = max(dodeca_full) <= pred.forbidden_above
    print(f"  predicted 4 appears in dodecahedron   : {four_appears}")
    print(f"  no multiplicity exceeds {pred.forbidden_above}            : {no_excess}")

    # Also confirm the icosahedron itself genuinely hides the 4.
    ico_full = full_modes(ico)
    print(
        f"  icosahedron full spectrum             : {ico_full}  "
        f"(note: never shows a 4)"
    )

    ok = four_appears and no_excess and (4 not in set(ico_full))
    print(
        f"\n  VERDICT: {'PASS — predicted an integer absent from the input' if ok else 'FAIL'}"
    )
    return ok


def control_irreducibility() -> bool:
    """Prove the protected/accidental split with the character inner product, and
    verify the sharp exclusion (no 2-fold degeneracy in icosahedral symmetry)."""
    _rule("IRREDUCIBLE vs COMPOSITE — protected degeneracy = irreducible rep")
    print("  <chi,chi> counts irreps in an eigenspace: 1 => irreducible (protected),")
    print("  k>1 => reducible (accidental sum of k irreps). Computed over Aut(G).\n")

    ok = True

    print("  Icosahedron (group I — HAS a 5D irrep):")
    for _ev, mult, norm in eigenspace_irreducibility(nx.icosahedral_graph()):
        kind = (
            "irreducible (protected)"
            if abs(norm - 1) < 0.3
            else f"reducible (~{round(norm)} irreps)"
        )
        flag = "   <- the 5 is a PROTECTED irrep" if mult == 5 else ""
        print(f"    mult={mult}  <chi,chi>={norm:4.1f}  {kind}{flag}")
        if mult == 5:
            ok = ok and abs(norm - 1) < 0.3

    tc = getattr(nx, "truncated_cube_graph", None)
    if tc is not None:
        print("\n  Truncated cube (group O — NO 5D irrep, so a 5 must be accidental):")
        for _ev, mult, norm in eigenspace_irreducibility(tc()):
            if abs(norm - 1) < 0.3:
                kind = "irreducible (protected)"
            else:
                kind = f"reducible: {mult} = sum of {round(norm)} irreps (ACCIDENTAL)"
            flag = "   <- the 5 = 2(+)3, NOT protected" if mult == 5 else ""
            print(f"    mult={mult}  <chi,chi>={norm:4.1f}  {kind}{flag}")
            if mult == 5:
                ok = ok and (round(norm) == 2)

    print("\n  Sharp exclusion (icosahedral has NO 2D irrep -> no generic 2-fold):")
    ico_m = full_modes(nx.icosahedral_graph())
    dod_m = full_modes(nx.dodecahedral_graph())
    no2 = (2 not in ico_m) and (2 not in dod_m)
    print(f"    icosahedron {ico_m}, dodecahedron {dod_m}: no 2-fold = {no2}")
    ok = ok and no2

    print(
        f"\n  VERDICT: {'PASS — same integer 5 is irreducible in I, reducible (2+3) in O; exclusion holds' if ok else 'FAIL'}"
    )
    return ok


def within_manifold_prediction() -> bool:
    """Strongest non-circular form: reveal a manifold's low modes, predict its
    OWN hidden higher modes contain a structurally-required integer."""
    _rule("WITHIN-MANIFOLD — predict a manifold's own hidden modes")

    dodeca = nx.dodecahedral_graph()
    full = full_modes(dodeca)
    observed = full[:3]  # reveal [1, 3, 5]; hide [4, 4, 3]
    hidden = full[3:]
    pred = make_prediction(observed)
    print(f"  dodecahedron — revealed low modes     : {observed}")
    print(f"  dodecahedron — hidden higher modes    : {'?' * len(hidden)} (concealed)")
    print(f"  inferred group (from the 5)           : {pred.inferred_group}")
    print(
        f"  PREDICTION: hidden modes must include a degeneracy in "
        f"{sorted(pred.predicted_existing)}."
    )

    revealed_hidden = hidden
    hit = bool(pred.predicted_existing & set(revealed_hidden))
    print(f"\n  reveal hidden modes                   : {revealed_hidden}")
    print(f"  predicted integer found in hidden set : {hit}")
    print(
        f"\n  VERDICT: {'PASS — hidden degeneracy predicted before revealing' if hit else 'FAIL'}"
    )
    return hit


def main() -> None:
    print(__doc__)
    r1 = headline_icosahedral_prediction()
    r2 = within_manifold_prediction()
    r3 = control_irreducibility()

    _rule("SUMMARY")
    print(f"  headline (predict hidden 4 on sibling)   : {'PASS' if r1 else 'FAIL'}")
    print(f"  within-manifold (predict own hidden mode): {'PASS' if r2 else 'FAIL'}")
    print(f"  irreducible/composite + sharp exclusion  : {'PASS' if r3 else 'FAIL'}")
    overall = r1 and r2 and r3
    print(f"\n  OVERALL: {'ALL PASS' if overall else 'MISMATCH'}")
    print("\n  Reading: the inverse map (partial spectrum -> group -> unseen integer)")
    print("  succeeds, and the emergent degeneracies carry an irreducible/composite")
    print("  structure: protected integers are irreducible reps, accidental ones are")
    print("  sums. This is the structural reading of Aut(G)-equivariance; the emergent")
    print("  L_rw = discrete ΔNFR yields cardinals and their irreducibility,")
    print("  NOT the arithmetic ring; (+, ×, primality) of integers stay open.")


if __name__ == "__main__":
    main()