results: close Oral-A v2 causal-capture gate

8 files changed, 2512 insertions, 7 deletions

diff --git a/results/oral_a_failure_diagnosis.json b/results/oral_a_failure_diagnosis.json
index 7c6b31b..5239e8b 100644
--- a/results/oral_a_failure_diagnosis.json
+++ b/results/oral_a_failure_diagnosis.json
@@ -44,14 +44,15 @@
     "target_power_growth_epoch1_to_87": 2126827049.3932126
   },
   "diagnosis": {
-    "classification": "causal_target_not_captured_before_runaway",
+    "classification": "unit_target_regression_snr_inadequate_before_runaway",
     "evidence": [
-      "prediction-target cosine remains effectively zero",
-      "calibration MSE remains indistinguishable from target power",
+      "instantaneous prediction-target cosine remains effectively zero",
+      "calibration MSE remains indistinguishable from stochastic target power",
       "target power grows by orders of magnitude before nonfiniteness",
-      "short-run teaching alignment therefore cannot be attributed to learned A"
+      "matched frozen-forward unit targets later yield only 0.0011 early-layer alignment"
     ],
-    "next_test": "reduce estimator variance by perturbing the representable channel-gated feedback subspace, not every spatial unit"
+    "interpretation_limit": "instantaneous target metrics alone do not prove zero conditional learning; structured v2 has low instantaneous cosine yet positive exact-gradient alignment",
+    "next_test_outcome": "representable-subspace perturbation improved exact alignment but failed the frozen early-layer causal-capture gate"
   },
   "frozen_outcome": {
     "confirmation_test_seeds_touched": false,
@@ -71,7 +72,14 @@
       "loss": null
     }
   },
-  "protocol": "oral_a_A3_failure_diagnosis_v1",
+  "post_failure_v2_refinement": {
+    "structured_all_layer_alignment": 0.05273995646520665,
+    "structured_early_third_alignment": 0.007209055746595065,
+    "structured_gate_status": "failed",
+    "unit_target_all_layer_alignment": 0.011664319529173602,
+    "unit_target_early_third_alignment": 0.0011051012067279469
+  },
+  "protocol": "oral_a_A3_failure_diagnosis_v2",
   "short_screen_contrast": {
     "accuracy": 0.4198,
     "early_third_alignment": 0.08889565182228883,
@@ -85,6 +93,7 @@
   "source_paths": {
     "full": "results/oral_a_dev/sdil_full_r20_s0.json",
     "gate": "results/oral_a_full_gate.json",
-    "short": "results/oral_a_short/sdil_channel_gated_lr0.03.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_v2_calibration/channel_subspace_etaA0.01.json b/results/oral_a_v2_calibration/channel_subspace_etaA0.01.json
new file mode 100644
index 0000000..4282d51
--- /dev/null
+++ b/results/oral_a_v2_calibration/channel_subspace_etaA0.01.json
@@ -0,0 +1,393 @@
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diff --git a/results/oral_a_v2_calibration/channel_subspace_etaA0.1.json b/results/oral_a_v2_calibration/channel_subspace_etaA0.1.json
new file mode 100644
index 0000000..95a7fbb
--- /dev/null
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index 0000000..7da80b1
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index 0000000..a648c1b
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+        "mean_prediction_target_cosine": -1.0456776873674686e-06,
+        "mean_target_power": 306.80684325786916
+      },
+      "path": "results/oral_a_v2_calibration/unit_targets_etaA0.01.json",
+      "source_commit": "fc8fe99504fe86a3636721031a7dc3d41a7909a6"
+    }
+  },
+  "status": "failed"
+}