Reproduce the numerical appendix from archived runs

.gitignore

@@ -236,3 +236,9 @@ STYLE.md
 *.bib.bak*
 figures/
 references/
+
+# Curated appendix reproduction inputs; other paper PDFs stay ignored.
+!/data/appendix/reference_figures/*.pdf
+
+# Saved-array exports are generated, never source inputs.
+/out/

README.md

@@ -32,6 +32,16 @@ python e1_kuramoto.py --smoke --device cpu --no-mirror
 
 This runs a 20-iteration workflow check on a small grid and network. Results appear under `out_1/`, including `untwist.gif`, `kernel_time.gif`, and `metrics.json`.
 
+## Reproduce the appendix
+
+Regenerate the existing appendix figures and table statistics from the bundled original results, without training or simulation:
+
+```bash
+python reproduce_appendix.py --manifest appendix_manifest.json --output out/appendix-regenerated
+```
+
+See [appendix reproduction](docs/APPENDIX_REPRODUCTION.md) for the archived runs, verification checks, and noise-data limitation.
+
 ## Code
 
 | File | Contents |

appendix_manifest.json

@@ -0,0 +1,61 @@
+{
+  "schema_version": 1,
+  "description": "Original runs for the existing Miller\u2013Su numerical appendix. Historical inputs retained unchanged; no retraining.",
+  "runs": {
+    "bc": {
+      "path": "data/appendix/runs/e2_eps1_T10",
+      "sha256": {
+        "config.json": "d1ca4cb1465da0066238d5aad0f6a8db19db384930861453fc69b4dac952ed3c",
+        "metrics.json": "5eb0369d0221b785a7ad2df440651030e663cc0c74b602f1bcb6ebaf403dd4b7",
+        "trajectories.npz": "994ee45d108fa67ca96859322fe1839ecc326693fffa93d7f02645f071d28f9a"
+      }
+    },
+    "k1": {
+      "path": "data/appendix/runs/e1_k1_T5",
+      "sha256": {
+        "config.json": "18fe4364db0476d7456f309a0f633c0b537431aa0f3f5207c117f8238ce2013f",
+        "metrics.json": "b42fcc26c74ab045557b2cefb5e9c3aa8b41ba06735208f7dcdd746d04a01bc6",
+        "trajectories.npz": "f91fa4e9bd80514d2f5f1a4ca2d4b947af0bff483a1600611987b6199d7b04a7"
+      }
+    },
+    "k2": {
+      "path": "data/appendix/runs/e1_k2_T12",
+      "sha256": {
+        "config.json": "8fc0b04e36aca41d50332ed1344f4093650ffd148185a1e612ca73b311759731",
+        "metrics.json": "5787be6e8cf28f4bc39997318705abf71424fdc92578b0e7a4e07cd3d3e70b44",
+        "trajectories.npz": "d982c7da096d611948220f7ee22e8caede394f7a63b9b91b3e4a6c47e2602032"
+      }
+    },
+    "k3": {
+      "path": "data/appendix/runs/e1_k3_T20",
+      "sha256": {
+        "config.json": "4a13f2ec59bd6b06e03dc6027b9a9c1d99f1ff8f27d58a100f6d66f85553c0c6",
+        "metrics.json": "78e161f2bbef70e20100b145eee8fae1e34051542a37ca5057b24d0d883fec63",
+        "trajectories.npz": "d599a52abc17b22ead159bd527096afedc081b5286ff9e2c661de59be97ed15b"
+      }
+    },
+    "noise": {
+      "path": "data/appendix/runs/e1_k2_T12_sig03",
+      "sha256": {
+        "config.json": "0e3e703619b9876159949ebdf13a389a570400c11d26e580a48e25a9d214e77c",
+        "metrics.json": "31212452d9d34e9bba01b772aecf8e1e6eef2cc464c0133f1254fa8644fadf2d",
+        "trajectories.npz": "58c6322f096cbd34c240a25119efb2b0b76368c502801bae986dff625dd0f9b7"
+      }
+    }
+  },
+  "output_dir": "out/appendix-reference",
+  "figures": {
+    "bc_opinion_traj.pdf": "data/appendix/reference_figures/bc_opinion_traj.pdf",
+    "bc_kernel_strip.pdf": "data/appendix/reference_figures/bc_kernel_strip.pdf",
+    "kur_rw_panel.pdf": "data/appendix/reference_figures/kur_rw_panel.pdf",
+    "kur_kernel_strip.pdf": "data/appendix/reference_figures/kur_kernel_strip.pdf",
+    "kur_noise_panel.pdf": "data/appendix/reference_figures/kur_noise_panel.pdf"
+  },
+  "figures_sha256": {
+    "bc_opinion_traj.pdf": "13fea9efb675fe284756047ecfe6b40d14d0c7bb5efd537ca944c0599253c253",
+    "bc_kernel_strip.pdf": "ae25938cb270330ab7a12264432c168c66c2d97f9e07ec9c758a6350d7d2e139",
+    "kur_rw_panel.pdf": "8fbd92e2bed0ac2524c648999752e915a760ad3ab9e86d0ccd624dd6219d47b3",
+    "kur_kernel_strip.pdf": "75e93c47be3df16626003550ec8eaf8d1957d6f2041d751281edb91c6f9160ae",
+    "kur_noise_panel.pdf": "54db656b1fd1c2f48fd8367a0b63af6f25a66c276af53b4c13b2ce57d82b86ea"
+  }
+}

data/appendix/runs/e1_k1_T5/config.json

@@ -0,0 +1,24 @@
+{
+  "k": 1,
+  "jitter": 0.1,
+  "ring_range": 0.1,
+  "N": 64,
+  "T": 5.0,
+  "dt": 0.05,
+  "sigma": 0.0,
+  "beta": 10.0,
+  "lambda_terminal": 0.0,
+  "gamma_target": 100.0,
+  "x_target": 0.0,
+  "batch_size": 16,
+  "num_steps": 2000,
+  "lr": 0.001,
+  "grad_clip": 1.0,
+  "num_fourier_freqs": 8,
+  "embed_dim": 64,
+  "hidden_embed": 128,
+  "hidden_score": 128,
+  "seed": 0,
+  "device": "cpu",
+  "time_chunk": 50
+}

data/appendix/runs/e1_k1_T5/metrics.json

@@ -0,0 +1,48 @@
+{
+  "k": 1,
+  "costs": {
+    "meanfield": {
+      "total": 66.44532775878906,
+      "state_cost": 16.44532585144043,
+      "l2_reg_cost": 50.0,
+      "terminal_var": 3.28906512260437,
+      "terminal_target": 1.3877787807814457e-15,
+      "terminal_mean": 3.725290298461914e-09
+    },
+    "ring": {
+      "total": 353.7691955566406,
+      "state_cost": 16.44532585144043,
+      "l2_reg_cost": 337.3238830566406,
+      "terminal_var": 3.28906512260437,
+      "terminal_target": 1.3877787807814457e-15,
+      "terminal_mean": 3.725290298461914e-09
+    },
+    "trained": {
+      "total": 57.51552963256836,
+      "state_cost": 5.354661464691162,
+      "l2_reg_cost": 52.1602668762207,
+      "terminal_var": 0.010279948823153973,
+      "terminal_target": 0.0006043039611540735,
+      "terminal_mean": -0.0024582594633102417
+    }
+  },
+  "r_initial": 2.6371218098120153e-08,
+  "r_final": {
+    "meanfield": 2.6371218098120153e-08,
+    "ring": 2.6371218098120153e-08,
+    "trained": 0.9948722720146179
+  },
+  "var_final": {
+    "meanfield": 3.28906512260437,
+    "ring": 3.28906512260437,
+    "trained": 0.010279948823153973
+  },
+  "terminal_mean_trained": -0.0024582594633102417,
+  "vxi": {
+    "trained_max": 3.740018129348755,
+    "trained_final": 0.06472654640674591,
+    "ring": 43.59681701660156,
+    "meanfield": 0.0
+  },
+  "value_gap_meanfield_minus_trained": 8.929798126220703
+}

data/appendix/runs/e1_k2_T12/config.json

@@ -0,0 +1,24 @@
+{
+  "k": 2,
+  "jitter": 0.1,
+  "ring_range": 0.1,
+  "N": 64,
+  "T": 12.0,
+  "dt": 0.05,
+  "sigma": 0.0,
+  "beta": 10.0,
+  "lambda_terminal": 0.0,
+  "gamma_target": 100.0,
+  "x_target": 0.0,
+  "batch_size": 16,
+  "num_steps": 2000,
+  "lr": 0.001,
+  "grad_clip": 1.0,
+  "num_fourier_freqs": 8,
+  "embed_dim": 64,
+  "hidden_embed": 128,
+  "hidden_score": 128,
+  "seed": 0,
+  "device": "cpu",
+  "time_chunk": 50
+}

data/appendix/runs/e1_k2_T12/metrics.json

@@ -0,0 +1,48 @@
+{
+  "k": 2,
+  "costs": {
+    "meanfield": {
+      "total": 277.8751220703125,
+      "state_cost": 157.87513732910156,
+      "l2_reg_cost": 120.0,
+      "terminal_var": 13.15626049041748,
+      "terminal_target": 5.551115123125783e-15,
+      "terminal_mean": 7.450580596923828e-09
+    },
+    "ring": {
+      "total": 967.452392578125,
+      "state_cost": 157.87513732910156,
+      "l2_reg_cost": 809.5772705078125,
+      "terminal_var": 13.15626049041748,
+      "terminal_target": 5.551115123125783e-15,
+      "terminal_mean": 7.450580596923828e-09
+    },
+    "trained": {
+      "total": 176.0917205810547,
+      "state_cost": 32.88923263549805,
+      "l2_reg_cost": 143.1961212158203,
+      "terminal_var": 1.5446259567397647e-05,
+      "terminal_target": 0.006367044057697058,
+      "terminal_mean": -0.00797937624156475
+    }
+  },
+  "r_initial": 2.8638169169425964e-08,
+  "r_final": {
+    "meanfield": 2.8638169169425964e-08,
+    "ring": 2.8638169169425964e-08,
+    "trained": 0.999992311000824
+  },
+  "var_final": {
+    "meanfield": 13.15626049041748,
+    "ring": 13.15626049041748,
+    "trained": 1.5446259567397647e-05
+  },
+  "terminal_mean_trained": -0.00797937624156475,
+  "vxi": {
+    "trained_max": 12.010483741760254,
+    "trained_final": 0.5435858964920044,
+    "ring": 43.59681701660156,
+    "meanfield": 0.0
+  },
+  "value_gap_meanfield_minus_trained": 101.78340148925781
+}

data/appendix/runs/e1_k2_T12_sig03/config.json

@@ -0,0 +1,25 @@
+{
+  "k": 2,
+  "jitter": 0.1,
+  "ring_range": 0.1,
+  "x0_level": 0,
+  "N": 64,
+  "T": 12.0,
+  "dt": 0.05,
+  "sigma": 0.3,
+  "beta": 10.0,
+  "lambda_terminal": 0.0,
+  "gamma_target": 100.0,
+  "x_target": 0.0,
+  "batch_size": 16,
+  "num_steps": 2000,
+  "lr": 0.001,
+  "grad_clip": 1.0,
+  "num_fourier_freqs": 8,
+  "embed_dim": 64,
+  "hidden_embed": 128,
+  "hidden_score": 128,
+  "seed": 0,
+  "device": "cpu",
+  "time_chunk": 50
+}

data/appendix/runs/e1_k2_T12_sig03/metrics.json

@@ -0,0 +1,61 @@
+{
+  "k": 2,
+  "costs": {
+    "meanfield": {
+      "total": 277.8751220703125,
+      "state_cost": 157.87513732910156,
+      "l2_reg_cost": 120.0,
+      "terminal_var": 13.15626049041748,
+      "terminal_target": 5.551115123125783e-15,
+      "terminal_mean": 7.450580596923828e-09
+    },
+    "ring": {
+      "total": 967.452392578125,
+      "state_cost": 157.87513732910156,
+      "l2_reg_cost": 809.5772705078125,
+      "terminal_var": 13.15626049041748,
+      "terminal_target": 5.551115123125783e-15,
+      "terminal_mean": 7.450580596923828e-09
+    },
+    "trained": {
+      "total": 190.85398864746094,
+      "state_cost": 31.176782608032227,
+      "l2_reg_cost": 159.62045288085938,
+      "terminal_var": 8.732880814932287e-06,
+      "terminal_target": 0.05675093084573746,
+      "terminal_mean": -0.02382245473563671
+    }
+  },
+  "r_initial": 2.8638169169425964e-08,
+  "r_final": {
+    "meanfield": 2.8638169169425964e-08,
+    "ring": 2.8638169169425964e-08,
+    "trained": 0.9999955892562866
+  },
+  "var_final": {
+    "meanfield": 13.15626049041748,
+    "ring": 13.15626049041748,
+    "trained": 8.732880814932287e-06
+  },
+  "winding_final": {
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+    "ring": 2.0,
+    "trained": -7.589098771632052e-08
+  },
+  "terminal_mean_trained": -0.02382245473563671,
+  "vxi": {
+    "trained_max": 14.46484088897705,
+    "trained_final": 0.5247852206230164,
+    "ring": 43.59681701660156,
+    "meanfield": 0.0
+  },
+  "value_gap_meanfield_minus_trained": 87.02113342285156,
+  "noisy_eval": {
+    "sigma": 0.3,
+    "n_replicates": 32,
+    "r_final_mean": 0.9742251634597778,
+    "r_final_q10": 0.9684785008430481,
+    "r_final_q90": 0.9789402484893799,
+    "winding_final_mean": -8.300576226361045e-09
+  }
+}

data/appendix/runs/e1_k3_T20/config.json

@@ -0,0 +1,25 @@
+{
+  "k": 3,
+  "jitter": 0.1,
+  "ring_range": 0.1,
+  "x0_level": 0,
+  "N": 64,
+  "T": 20.0,
+  "dt": 0.05,
+  "sigma": 0.0,
+  "beta": 10.0,
+  "lambda_terminal": 0.0,
+  "gamma_target": 100.0,
+  "x_target": 0.0,
+  "batch_size": 16,
+  "num_steps": 3000,
+  "lr": 0.001,
+  "grad_clip": 1.0,
+  "num_fourier_freqs": 8,
+  "embed_dim": 64,
+  "hidden_embed": 128,
+  "hidden_score": 128,
+  "seed": 0,
+  "device": "cpu",
+  "time_chunk": 50
+}