audit: reject nonfinite confirmation trajectories

1 files changed, 31 insertions, 4 deletions

diff --git a/experiments/analyze_kp_innovation_confirmation.py b/experiments/analyze_kp_innovation_confirmation.py
index 37b06b5..aa97108 100644
--- a/experiments/analyze_kp_innovation_confirmation.py
+++ b/experiments/analyze_kp_innovation_confirmation.py
@@ -76,6 +76,12 @@ def main():
                 raise ValueError(
                     f"tracked-dirty MT-3 seed {seed} {condition}")
             source_commits.add(record["provenance"]["git_commit"])
+            expected_space = (
+                "reciprocal_local_activity_products" if condition == "clean"
+                else "reciprocal_local_activity_products_with_mixed_apical_traffic")
+            if record.get("calibration_metric_space") != expected_space:
+                raise ValueError(
+                    f"MT-3 seed {seed} {condition} metric-space drift")
             if condition != "clean":
                 warmup = record.get("predictor_warmup", {})
                 if (warmup.get("instruction_present") is not False
@@ -138,23 +144,43 @@ def main():
             if any(value is None for value in tracking):
                 invariant_failures.append(f"seed{seed}_{condition}:tracking")
                 continue
+            for value in tracking:
+                finite_values.extend([
+                    float(value["mean_feedback_forward_cosine"]),
+                    float(value["mean_feedback_forward_relative_error"]),
+                    float(value["min_feedback_forward_cosine"]),
+                    float(value["max_feedback_forward_relative_error"]),
+                ])
             final_cosine = float(
                 record["diagnostics"]["mean_feedback_forward_cosine"])
             late_cosine = statistics.mean(float(
                 value["mean_feedback_forward_cosine"]) for value in tracking[150:])
+            finite_values.extend([final_cosine, late_cosine])
             if final_cosine < 0.95 or late_cosine < 0.95:
                 invariant_failures.append(f"seed{seed}_{condition}:kp_tracking")
             if condition != "clean":
                 ratios = record["traffic_calibration"][
                     "realized_traffic_instruction_rms_ratio"]
+                finite_values.extend(float(value) for value in ratios)
                 if max(abs(float(value) - 4.0) for value in ratios) > 1e-5:
                     invariant_failures.append(f"seed{seed}_{condition}:ratio")
                 diagnostics = record["diagnostics"]
-                if float(diagnostics[
-                        "predictor_traffic_residual_rms_ratio"]) > 0.05:
+                predictor_ratio = float(diagnostics[
+                    "predictor_traffic_residual_rms_ratio"])
+                norm_error = float(diagnostics["max_norm_match_relative_error"])
+                finite_values.extend([predictor_ratio, norm_error])
+                for row in trajectory:
+                    mixed = row.get("mixed_apical")
+                    if mixed is None:
+                        invariant_failures.append(
+                            f"seed{seed}_{condition}:mixed_trajectory")
+                        break
+                    finite_values.extend(float(mixed[key]) for key in (
+                        "teaching_rms", "instruction_rms", "raw_apical_rms",
+                        "innovation_rms", "traffic_rms"))
+                if predictor_ratio > 0.05:
                     invariant_failures.append(f"seed{seed}_{condition}:predictor")
-                if condition == "matched" and float(diagnostics[
-                        "max_norm_match_relative_error"]) > 1e-6:
+                if condition == "matched" and norm_error > 1e-6:
                     invariant_failures.append(f"seed{seed}_{condition}:norm")
                 if int(record["work"][
                         "elementwise_operations_estimate"]) <= 0:
@@ -164,6 +190,7 @@ def main():
                 used_early = float(diagnostics["early_third_mean"])
                 raw_early = mean_early(
                     diagnostics["raw_negative_gradient_cosine"])
+                finite_values.extend([used_early, raw_early])
                 if used_early < 0.80 or used_early - raw_early < 0.15:
                     invariant_failures.append(f"seed{seed}:alignment")