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#!/usr/bin/env python3
"""Audit and gate hierarchical feedback-parameter causal capture."""
import argparse
import glob
import json
import math
import os
SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"
RATES = (0.1, 1.0, 10.0)
def load(path):
with open(path) as handle:
record = json.load(handle)
args = record["args"]
mode = args.get("mode")
if mode not in ("hfa", "lhfa"):
raise ValueError(f"{path}: unexpected mode")
expected = {
"depth": 20, "width": 16, "seed": 0, "loader_seed": 0,
"epochs": 0, "train_limit": 10000, "val_examples": 5000,
"split_seed": 2027, "eval_split": "validation", "eval_every": 0,
"normalization": "batchnorm", "a_scale": 1.0,
"pert_directions": 1, "pert_every": 4, "pert_sigma": 0.01,
"perturb_seed": 1000, "alignment_probe": 64,
}
for key, value in expected.items():
if args.get(key) != value:
raise ValueError(f"{path}: {key} drift")
expected_warmup = 0 if mode == "hfa" else 400
if args.get("a_warmup_steps") != expected_warmup:
raise ValueError(f"{path}: feedback warmup drift")
if mode == "lhfa" and float(args["eta_A"]) not in RATES:
raise ValueError(f"{path}: unregistered feedback rate")
if record["provenance"]["git_tracked_dirty"]:
raise ValueError(f"tracked-dirty result: {path}")
if record["split"]["validation_index_sha256"] != SPLIT_HASH:
raise ValueError(f"split drift: {path}")
if record["evaluation_protocol"]["test_evaluations"]:
raise ValueError(f"test touched: {path}")
expected_space = (None if mode == "hfa"
else "hierarchical_feedback_parameters")
if record.get("calibration_metric_space") != expected_space:
raise ValueError(f"metric-space drift: {path}")
diagnostics = record.get("diagnostics")
if diagnostics is None:
raise ValueError(f"missing diagnostics: {path}")
values = diagnostics["teaching_negative_gradient_cosine"]
early_count = max(1, len(values) // 3)
norm_ratios = diagnostics["feedback_forward_norm_ratio"]
feedback_cosines = diagnostics["feedback_forward_cosine"]
metrics = {
"early_third_alignment": sum(values[:early_count]) / early_count,
"all_layer_alignment": sum(values) / len(values),
"mean_feedback_forward_cosine": (
sum(feedback_cosines) / len(feedback_cosines)),
"min_feedback_forward_norm_ratio": min(norm_ratios),
"max_feedback_forward_norm_ratio": max(norm_ratios),
}
warmup = record.get("apical_warmup", {}).get("mean")
if mode == "lhfa":
if warmup is None:
raise ValueError(f"missing calibration aggregate: {path}")
metrics.update({
"mean_calibration_mse": warmup["calibration_mse"],
"mean_target_power": warmup["target_power"],
"mean_prediction_target_cosine": (
warmup["prediction_target_cosine"]),
"mean_parameter_update_rms": warmup["parameter_update_rms"],
})
finite = (bool(record["final"]["finite"])
and all(math.isfinite(value) for value in metrics.values()))
return {
"path": path, "mode": mode,
"eta_A": (None if mode == "hfa" else float(args["eta_A"])),
"metrics": metrics, "finite": finite,
"logical_batch_loss_queries": int(
record["work"]["logical_batch_loss_queries"]),
"total_macs": int(record["work"]["total_macs_estimate"]),
"source_commit": record["provenance"]["git_commit"],
}
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--input", default="results/oral_a_v4_calibration")
parser.add_argument(
"--out", default="results/oral_a_v4_calibration_gate.json")
args = parser.parse_args()
rows = [load(path) for path in sorted(
glob.glob(os.path.join(args.input, "*.json")))]
references = [row for row in rows if row["mode"] == "hfa"]
candidates = [row for row in rows if row["mode"] == "lhfa"]
if len(references) != 1 or len(candidates) != len(RATES):
raise ValueError("incomplete V4-1 method grid")
if {row["eta_A"] for row in candidates} != set(RATES):
raise ValueError("incomplete V4-1 rate grid")
if len({row["source_commit"] for row in rows}) != 1:
raise ValueError("V4-1 source commits differ")
eligible = [row for row in candidates if row["finite"]]
eligible.sort(key=lambda row: (
-row["metrics"]["early_third_alignment"],
-row["metrics"]["all_layer_alignment"], row["eta_A"]))
selected = eligible[0] if eligible else None
reference = references[0]
checks = {
"all_four_records_finite": all(row["finite"] for row in rows),
"candidate_selected": selected is not None,
}
if selected is None:
checks.update({
"early_third_at_least_0.05": False,
"all_layer_at_least_0.10": False,
"early_gain_over_fixed_hfa_at_least_0.04": False,
"feedback_norm_ratios_in_0.1_to_3": False,
})
else:
metrics = selected["metrics"]
checks.update({
"early_third_at_least_0.05": (
metrics["early_third_alignment"] >= 0.05),
"all_layer_at_least_0.10": (
metrics["all_layer_alignment"] >= 0.10),
"early_gain_over_fixed_hfa_at_least_0.04": (
metrics["early_third_alignment"]
- reference["metrics"]["early_third_alignment"] >= 0.04),
"feedback_norm_ratios_in_0.1_to_3": (
metrics["min_feedback_forward_norm_ratio"] >= 0.1
and metrics["max_feedback_forward_norm_ratio"] <= 3.0),
})
output = {
"protocol": "oral_a_v4_hierarchical_causal_capture_v1",
"status": "passed" if all(checks.values()) else "failed",
"checks": checks, "rows": rows, "matched_fixed_hfa": reference,
"selected_v4": selected, "confirmation_test_seeds_touched": False,
"review_score_before": 5, "review_score_after": 5,
"score_change_rule": "causal capture alone cannot raise score",
}
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"], "checks": checks,
"reference": reference, "selected_v4": selected,
}, indent=2))
if __name__ == "__main__":
main()
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