From 931273085034d48db5d93b71669ba9bc30b8bc4d Mon Sep 17 00:00:00 2001
From: YurenHao0426 <Blackhao0426@gmail.com>
Date: Wed, 22 Jul 2026 06:22:25 -0500
Subject: experiments: freeze oral A development gates

---
 experiments/analyze_oral_a_apical.py | 100 +++++++++++++++++++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100755 experiments/analyze_oral_a_apical.py

(limited to 'experiments/analyze_oral_a_apical.py')

diff --git a/experiments/analyze_oral_a_apical.py b/experiments/analyze_oral_a_apical.py
new file mode 100755
index 0000000..1d10edb
--- /dev/null
+++ b/experiments/analyze_oral_a_apical.py
@@ -0,0 +1,100 @@
+#!/usr/bin/env python3
+"""Apply the frozen A2a selector to convolutional apical-screen records."""
+import argparse
+import glob
+import json
+import math
+import os
+
+
+MODES = ("spatial_template", "channel_gated")
+EXPECTED_SCALES = (0.0, 1.0)
+EXPECTED_RATES = (0.001, 0.01, 0.1)
+
+
+def load(path):
+    with open(path) as handle:
+        record = json.load(handle)
+    args = record["args"]
+    if record["provenance"]["git_tracked_dirty"]:
+        raise ValueError(f"tracked-dirty result: {path}")
+    expected = {
+        "mode": "sdil", "depth": 20, "width": 16, "seed": 0,
+        "epochs": 0, "train_limit": 10000, "val_examples": 5000,
+        "a_warmup_steps": 100, "pert_directions": 1, "pert_every": 4,
+        "normalization": "batchnorm",
+    }
+    for key, value in expected.items():
+        if args.get(key) != value:
+            raise ValueError(f"{path}: {key}={args.get(key)!r}, expected {value!r}")
+    if record["split"]["validation_index_sha256"] != (
+            "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"):
+        raise ValueError(f"split drift: {path}")
+    diagnostics = record.get("diagnostics")
+    warmup = record.get("apical_warmup", {}).get("last")
+    if diagnostics is None or warmup is None:
+        raise ValueError(f"missing diagnostics/warmup: {path}")
+    values = diagnostics["teaching_negative_gradient_cosine"]
+    early_count = max(1, len(values) // 3)
+    metrics = {
+        "early_third_alignment": sum(values[:early_count]) / early_count,
+        "all_layer_alignment": sum(values) / len(values),
+        "calibration_mse": warmup["calibration_mse"],
+        "prediction_target_cosine": warmup["prediction_target_cosine"],
+    }
+    finite = (record["final"]["finite"]
+              and all(math.isfinite(value) for value in metrics.values()))
+    return {
+        "path": path,
+        "source_commit": record["provenance"]["git_commit"],
+        "vectorizer_mode": args["vectorizer_mode"],
+        "a_scale": float(args["a_scale"]),
+        "eta_A": float(args["eta_A"]),
+        "metrics": metrics,
+        "eligible": finite and metrics["early_third_alignment"] > 0.0,
+        "vectorizer_parameters": record["architecture"]["vectorizer_parameters"],
+    }
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--input", default="results/oral_a_apical_screen")
+    parser.add_argument("--out", default="results/oral_a_apical_selection.json")
+    args = parser.parse_args()
+    paths = sorted(glob.glob(os.path.join(args.input, "*.json")))
+    rows = [load(path) for path in paths]
+    observed = {(row["vectorizer_mode"], row["a_scale"], row["eta_A"])
+                for row in rows}
+    expected = {(mode, scale, rate) for mode in MODES
+                for scale in EXPECTED_SCALES for rate in EXPECTED_RATES}
+    if observed != expected or len(rows) != len(expected):
+        raise ValueError(f"incomplete A2a grid: missing={expected-observed}, extra={observed-expected}")
+    if len({row["source_commit"] for row in rows}) != 1:
+        raise ValueError("A2a source commits differ")
+    selected = {}
+    for mode in MODES:
+        eligible = [row for row in rows
+                    if row["vectorizer_mode"] == mode and row["eligible"]]
+        if eligible:
+            eligible.sort(key=lambda row: (
+                -row["metrics"]["early_third_alignment"],
+                -row["metrics"]["all_layer_alignment"],
+                row["eta_A"], row["a_scale"]))
+            selected[mode] = eligible[0]
+    output = {
+        "protocol": "oral_a_A2a_v1",
+        "status": ("selected" if len(selected) == len(MODES)
+                   else "failed_no_eligible_family"),
+        "rows": rows,
+        "selected": selected,
+        "confirmation_test_seeds_touched": False,
+    }
+    os.makedirs(os.path.dirname(os.path.abspath(args.out)), exist_ok=True)
+    with open(args.out, "w") as handle:
+        json.dump(output, handle, indent=2, sort_keys=True)
+        handle.write("\n")
+    print(json.dumps({"status": output["status"], "selected": selected}, indent=2))
+
+
+if __name__ == "__main__":
+    main()
-- 
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