analysis: localize early-layer feedback bottlenecks

2 files changed, 285 insertions, 2 deletions

diff --git a/results/oral_a_failure_diagnosis.json b/results/oral_a_failure_diagnosis.json
index 5239e8b..1b142df 100644
--- a/results/oral_a_failure_diagnosis.json
+++ b/results/oral_a_failure_diagnosis.json
@@ -44,14 +44,17 @@
     "target_power_growth_epoch1_to_87": 2126827049.3932126
   },
   "diagnosis": {
-    "classification": "unit_target_regression_snr_inadequate_before_runaway",
+    "classification": "early_credit_limited_by_estimator_efficiency_and_feedback_capacity",
     "evidence": [
       "instantaneous prediction-target cosine remains effectively zero",
       "calibration MSE remains indistinguishable from stochastic target power",
       "target power grows by orders of magnitude before nonfiniteness",
-      "matched frozen-forward unit targets later yield only 0.0011 early-layer alignment"
+      "matched frozen-forward unit targets later yield only 0.0011 early-layer alignment",
+      "structured learning reaches 0.0072 versus a 0.0240 cross-validated family oracle",
+      "unconstrained coefficients raise the same spatial basis oracle only to 0.0549"
     ],
     "interpretation_limit": "instantaneous target metrics alone do not prove zero conditional learning; structured v2 has low instantaneous cosine yet positive exact-gradient alignment",
+    "next_design_constraint": "do not add the tested naive local-average/channel-mean bases; improve causal sample efficiency and use a more informative hierarchical or high-level contextual feedback signal",
     "next_test_outcome": "representable-subspace perturbation improved exact alignment but failed the frozen early-layer causal-capture gate"
   },
   "frozen_outcome": {
@@ -72,6 +75,12 @@
       "loss": null
     }
   },
+  "post_failure_representation_refinement": {
+    "channel_gated_cv_oracle_early_third_alignment": 0.02396580002561188,
+    "naive_local_context_cv_early_third_alignment": 0.022457713873793972,
+    "per_example_spatial_oracle_early_third_alignment": 0.05494018487684554,
+    "trained_structured_early_third_alignment": 0.007209055746595065
+  },
   "post_failure_v2_refinement": {
     "structured_all_layer_alignment": 0.05273995646520665,
     "structured_early_third_alignment": 0.007209055746595065,
@@ -93,6 +102,7 @@
   "source_paths": {
     "full": "results/oral_a_dev/sdil_full_r20_s0.json",
     "gate": "results/oral_a_full_gate.json",
+    "representation": "results/oral_a_representation_diagnosis.json",
     "short": "results/oral_a_short/sdil_channel_gated_lr0.03.json",
     "v2_gate": "results/oral_a_v2_calibration_gate.json"
   }
diff --git a/results/oral_a_representation_diagnosis.json b/results/oral_a_representation_diagnosis.json
new file mode 100644
index 0000000..3c170c1
--- /dev/null
+++ b/results/oral_a_representation_diagnosis.json
@@ -0,0 +1,273 @@
+{
+  "cosine": {
+    "channel_gated_cv": {
+      "all_layer_mean": 0.26860839708781414,
+      "early_third_mean": 0.02396580002561188,
+      "per_layer": [
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+    "local_context_cv": {
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+      "early_third_mean": 0.022457713873793972,
+      "per_layer": [
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+    },
+    "per_example_spatial_oracle": {
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+      "early_third_mean": 0.05494018487684554,
+      "per_layer": [
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+        1.0000000000000022
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+    },
+    "spatial_template_cv": {
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+      "early_third_mean": 0.04796723142241297,
+      "per_layer": [
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+  },
+  "interpretation": {
+    "channel_gated_cv": "actual v2 vectorizer family fit on 32 examples and evaluated on 32 disjoint examples",
+    "local_context_cv": "diagnostic four-field family adding local spatial average and cross-channel somatic context",
+    "per_example_spatial_oracle": "upper bound for the two [1,tanh(h)] fields with unconstrained per-example/channel coefficients",
+    "spatial_template_cv": "output-error linear map with an independent coefficient at every hidden unit",
+    "status": "post_failure_diagnosis_not_a_learning_result_or_gate"
+  },
+  "network": {
+    "depth": 20,
+    "forward_state": "deterministic_initialization_no_updates",
+    "normalization": "batchnorm",
+    "residual_scale": 1.0,
+    "seed": 0,
+    "width": 16
+  },
+  "prediction_energy_over_target_energy": {
+    "channel_gated_cv": {
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+      "early_third_mean": 0.002141493001862695,
+      "per_layer": [
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+        0.6184532017563165,
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+  },
+  "probe": {
+    "batchnorm_cv": "fit and evaluation halves use separate 32-example batch statistics and exact-gradient graphs",
+    "evaluation_examples": 32,
+    "examples": 64,
+    "fit_examples": 32,
+    "source": "unaugmented first training-prefix examples",
+    "test_examples_touched": 0
+  },
+  "protocol": "oral_a_post_failure_representation_diagnosis_v1",
+  "provenance": {
+    "git_commit": "ba50c8fc286f6071c688a12606216cfb871a7ee0",
+    "git_tracked_dirty": false
+  },
+  "split": {
+    "dataset": "cifar10",
+    "input_layout": "NCHW",
+    "input_shape": [
+      3,
+      32,
+      32
+    ],
+    "loader_seed": 0,
+    "normalization_mean": [
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+      0.4821999967098236,
+      0.4465000033378601
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+    "normalization_std": [
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+      0.2434999942779541,
+      0.26159998774528503
+    ],
+    "split_from_training_only": true,
+    "split_seed": 2027,
+    "test_examples": 10000,
+    "train_examples": 10000,
+    "training_augmentation": "none",
+    "validation_class_counts": {
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+      "1": 500,
+      "2": 500,
+      "3": 500,
+      "4": 500,
+      "5": 500,
+      "6": 500,
+      "7": 500,
+      "8": 500,
+      "9": 500
+    },
+    "validation_examples": 5000,
+    "validation_index_sha256": "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"
+  }
+}