Parameter targets still missing

Love's Equation Dashboard

One or more configured mechanism checks failed, so the run should be treated as exploratory rather than validated.

Paper model status: not-yet-supported

Result schema: lovesequation.experiment_result v1

Audit trail: Results are written to experiment_results.json, append-only run records go to audit_log.jsonl, and ML memory is stored at ml_feedback.json.

Result schema
v1
Initial optimization energy
1544.840997
Final optimization energy
1415.700550
Initial full energy
1546.482461
Final full energy
1418.081153
Spectral gap
0.050251
Plateau error
0.157864
Projector overlap
0.000000
Coherence variance
0.001925
Localization length
1.460278
Stress-energy trace
-0.012128
Lambda_eff
2.852960
FRW limit ok
True
Stable fixed point
False
Coherent phase
True

Mechanism acceptance gates

Each gate maps a visible mechanism in the paper-inspired workflow to an explicit pass/fail threshold, so it is obvious why the run is or is not considered aligned.

PASSSpectral gap clears configured floor (0.050250677419714465 >= 0.001)
PASSProjector overlap stays within geometric tolerance (7.373512711365042e-17 <= 0.05)
PASSCoherence variance stays below residual floor (0.0019248472552731617 <= 0.4)
FAILLocalized matter excitation remains finite (1.4602781732469023 <= 1.0)
PASSFRW reduction passes consistency gate (True is True)
FAILRG fixed point remains stable (False is True)
PASSCoherent phase emerges (True is True)

How the mechanism chain works

1Gradient flow lowers the optimization functional (without the Einstein sector) and also reports the full-energy trajectory for transparency.
2Projectors split geometry and matter so the dashboard can show whether the emergent sectors remain well separated.
3Matter observables and FRW reduction expose whether the simulated universe remains physically coherent.
4The ML dial search reuses prior runs to bias future parameter proposals, creating a persistent closed-loop learning cycle.

Lyapunov flow history

StepOptimization energyFull energyGradient normLyapunov drop

Experiment controls

Run new experiments from this dashboard, load prior runs from the audit log, and validate payload integrity against the latest append-only audit record. You can also inject any TheoryConfig override (including integer search dials and ML search size) before launching a run.

INFOReady.
HASHAwaiting integrity check.

Raw result payload

{
  "audit": {
    "results_path": "experiment_results.json",
    "audit_log_path": "audit_log.jsonl",
    "ml_feedback_path": "ml_feedback.json",
    "flow_steps": 5
  },
  "cosmology": {
    "lambda_eff": 2.8529604730279186,
    "frw_limit_ok": true,
    "delta_h": 0.6936269636777679,
    "delta_h_dot": 0.34681348183888394,
    "hubble_dot": 0.2846789716877078,
    "hubble_squared": 1.6616912512229138,
    "lambda_E": 1.1739614436629533,
    "lambda_geom": 6.664308155411632e-33,
    "lambda_grav": 1.6789990293649655
  },
  "energy_final": 1415.7005498841781,
  "energy_initial": 1544.8409972181355,
  "fixed_point": {
    "stable": false,
    "beta_values": {
      "beta_E": -0.013999999999999997,
      "beta_coh": 0.5750000000000001,
      "beta_curv": 0.08250000000000002,
      "beta_sc": 0.05050000000000002
    },
    "couplings": {
      "g_E": 0.2,
      "g_coh": 1.0,
      "g_curv": 0.25,
      "g_sc": 0.2
    },
    "jacobian_eigenvalues": [
      0.5790011291425579,
      0.2334868534103853,
      0.3454807991020865,
      -0.07046878165502936
    ],
    "normalized_invariants": {
      "epsilon_star": 0.2,
      "rho_star": 0.25,
      "s_star": 1.0,
      "xi_star": 0.2
    }
  },
  "full_energy_final": 1418.0811529206326,
  "full_energy_initial": 1546.4824614699985,
  "lyapunov_history": [
    {
      "energy": 1544.8409972181355,
      "full_energy": 1546.4824614699985,
      "gradient_norm": 75.10567687501697,
      "lyapunov_drop": 0.0,
      "step": 0
    },
    {
      "energy": 1507.085239802762,
      "full_energy": 1507.1037289534781,
      "gradient_norm": 122.3268832637283,
      "lyapunov_drop": 37.75575741537341,
      "step": 1
    },
    {
      "energy": 1476.7633628379888,
      "full_energy": 1478.9512776963456,
      "gradient_norm": 130.97202474927053,
      "lyapunov_drop": 30.32187696477331,
      "step": 2
    },
    {
      "energy": 1437.1477738742587,
      "full_energy": 1438.164786829627,
      "gradient_norm": 239.13182703216114,
      "lyapunov_drop": 39.61558896373003,
      "step": 3
    },
    {
      "energy": 1415.7005498841781,
      "full_energy": 1418.0811529206326,
      "gradient_norm": 195.90717102937992,
      "lyapunov_drop": 21.4472239900806,
      "step": 4
    }
  ],
  "matter": {
    "localization_length": 1.4602781732469023,
    "amplitude": -0.1950611903651346,
    "kinetic_trace": 1.369759247519878,
    "quartic_strength": 0.7802095777592671,
    "stress_energy_trace": -0.012128279305932877,
    "einstein_residual": 1.1739614436629533
  },
  "phase": {
    "spectral_gap": 0.050250677419714465,
    "projector_overlap": 7.373512711365042e-17,
    "coherence_variance": 0.0019248472552731617,
    "spectral_plateau_error": 0.15786439833912033,
    "curvature_energy": 123.55503592227576,
    "coherent_phase": true,
    "bounded_curvature": true,
    "matter_geometry_disjoint": true,
    "positive_gap": true,
    "suppressed_coherence": true
  },
  "schema": {
    "name": "lovesequation.experiment_result",
    "version": 1
  },
  "universe_mapping": null
}