phaseT: analysis report

.gitattributes

@@ -40,3 +40,4 @@ phaseQ_reports/g_class_probe_v3.png filter=lfs diff=lfs merge=lfs -text
 phaseQ_reports/p_rank09_probe.png filter=lfs diff=lfs merge=lfs -text
 phaseQ_reports/p_rank09_probe_v2.png filter=lfs diff=lfs merge=lfs -text
 phaseS_reports/analysis/phaseS_plots.png filter=lfs diff=lfs merge=lfs -text
+phaseT_reports/analysis/phaseT_plots.png filter=lfs diff=lfs merge=lfs -text

phaseT_reports/analysis/phaseT_analysis.json

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+      "9": 320.0,
+      "10": 343.3333333333333,
+      "11": 370.0,
+      "12": 630.0,
+      "13": 420.0,
+      "14": 0.0,
+      "15": 235.0
+    }
+  },
+  "thresholds": {
+    "projective_clean": 0.05,
+    "a3_reference_deviation": 0.016
+  }
+}

phaseT_reports/analysis/phaseT_summary.md

@@ -0,0 +1,97 @@
+# Phase T — D=5 Convergence Sweep Analysis
+
+Generated from 64 successful configs.
+
+**Sweep grid:** 4 architectures × 16 noise types = 64 configs
+**Per-config:** 1000 batches Adam @ lr=3e-3 (matches A3 reference)
+
+**Projective-clean threshold:** |band_deviation| < 0.05
+**A3 D=5 V=32 gaussian reference:** deviation +0.016
+
+---
+
+## Headline finding
+
+✗ **Projective-clean does NOT hold universally at D=5.** Only 25/64 configs (39.1%) converged. A3's gaussian result was likely the lucky case, not the general one.
+
+---
+
+## G2) Per-architecture universality
+
+How many noise types converged to projective-clean for each architecture.
+
+| Architecture | % clean | Mean dev | Range | Mean erank/5 |
+|---|---|---|---|---|
+| `h64_V16_dp1_nx1` | 62% (10/16) | +0.0389 | [-0.028, +0.193] | 4.98 |
+| `h64_V32_dp0_nx1` | 50% (8/16) | +0.0450 | [-0.002, +0.090] | 4.99 |
+| `h64_V8_dp1_nx0` | 25% (4/16) | +0.1160 | [-0.040, +0.297] | 4.97 |
+| `h64_V32_dp1_nx1` | 19% (3/16) | +0.0678 | [+0.039, +0.116] | 4.99 |
+
+**Architectures that failed on some noises:**
+
+- `h64_V16_dp1_nx1`: uniform(+0.066), pink(+0.052), brown(+0.056), sparse_impulses(+0.193), gradient_gaussian(+0.051), four_quadrant(+0.083)
+- `h64_V32_dp0_nx1`: uniform(+0.053), uniform_scaled(+0.067), poisson(+0.085), sparse_impulses(+0.082), block_upsampled(+0.063), gradient_gaussian(+0.052), gauss_uniform_mix(+0.067), four_quadrant(+0.090)
+- `h64_V8_dp1_nx0`: gaussian(+0.106), uniform(+0.106), poisson(+0.297), pink(+0.178), brown(+0.195), sparse_impulses(+0.099), block_upsampled(+0.173), gradient_gaussian(+0.127), gauss_uniform_mix(+0.180), four_quadrant(+0.106), cauchy(+0.077), exponential(+0.236)
+- `h64_V32_dp1_nx1`: gaussian(+0.090), uniform(+0.067), uniform_scaled(+0.064), poisson(+0.055), pink(+0.051), block_upsampled(+0.059), gradient_gaussian(+0.071), checker(+0.066), gauss_uniform_mix(+0.082), four_quadrant(+0.072), cauchy(+0.078), exponential(+0.116), laplace(+0.090)
+
+## G2-dual) Per-noise universality
+
+How many architectures converged to projective-clean for each noise type.
+
+| Noise | % clean | Mean dev | Mean erank/5 |
+|---|---|---|---|
+| salt_pepper | 100% (4/4) | +0.0146 | 5.00 |
+| checker | 75% (3/4) | +0.0103 | 4.95 |
+| laplace | 75% (3/4) | +0.0145 | 5.00 |
+| gaussian | 50% (2/4) | +0.0609 | 5.00 |
+| uniform_scaled | 50% (2/4) | +0.0430 | 5.00 |
+| brown | 50% (2/4) | +0.0730 | 4.91 |
+| cauchy | 50% (2/4) | +0.0559 | 5.00 |
+| exponential | 50% (2/4) | +0.1054 | 5.00 |
+| poisson | 25% (1/4) | +0.1149 | 4.99 |
+| pink | 25% (1/4) | +0.0713 | 4.91 |
+| sparse_impulses | 25% (1/4) | +0.1038 | 5.00 |
+| block_upsampled | 25% (1/4) | +0.0836 | 4.98 |
+| gauss_uniform_mix | 25% (1/4) | +0.0839 | 5.00 |
+| uniform | 0% (0/4) | +0.0729 | 5.00 |
+| gradient_gaussian | 0% (0/4) | +0.0752 | 4.98 |
+| four_quadrant | 0% (0/4) | +0.0876 | 4.97 |
+
+## G3) V-axis dependence
+
+Does projective-clean depend on V at D=5?
+
+| V | % clean | Mean dev | Range | erank | CV |
+|---|---|---|---|---|---|
+| 8 | 25% (4/16) | +0.1160 | [-0.040, +0.297] | 4.97 | 0.643 |
+| 16 | 62% (10/16) | +0.0389 | [-0.028, +0.193] | 4.98 | 0.600 |
+| 32 | 34% (11/32) | +0.0564 | [-0.002, +0.116] | 4.99 | 0.620 |
+
+## G4) Convergence dynamics
+
+Mean batches to CV convergence: 360 (across 38 configs with detectable convergence points).
+
+**Per-noise convergence speed (mean batches):**
+
+- exponential: ~0 batches
+- brown: ~90 batches
+- pink: ~220 batches
+- laplace: ~235 batches
+- sparse_impulses: ~240 batches
+- block_upsampled: ~285 batches
+- gradient_gaussian: ~320 batches
+- checker: ~343 batches
+- gaussian: ~363 batches
+- gauss_uniform_mix: ~370 batches
+- cauchy: ~420 batches
+- uniform: ~423 batches
+- poisson: ~502 batches
+- uniform_scaled: ~580 batches
+- salt_pepper: ~625 batches
+- four_quadrant: ~630 batches
+
+---
+
+**Generated by `cell_t_analyzer.py` from `results_phaseT.json`.**
+
+Outputs: `phaseT_summary.md` (this file), `phaseT_analysis.json`, `phaseT_plots.png`.