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#!/usr/bin/env python3
"""Audit the frozen D1 dynamic neutral-projection training prefix."""
import argparse
import json
import math
import os
import statistics
SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"
def numeric_leaves(value):
if isinstance(value, bool) or value is None:
return
if isinstance(value, (int, float)):
yield float(value)
elif isinstance(value, dict):
for child in value.values():
yield from numeric_leaves(child)
elif isinstance(value, (list, tuple)):
for child in value:
yield from numeric_leaves(child)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--input", default="results/kp_dynamic_projection/dynamic.json")
parser.add_argument(
"--out", default="results/kp_dynamic_projection_gate.json")
args = parser.parse_args()
with open(args.input) as handle:
record = json.load(handle)
expected = {
"protocol": "kp_dynamic_neutral_projection_diagnosis_v1",
"scope": "training_only_no_validation_or_test_evaluation",
"rule": "innovation",
"predictor_mode": "closed_form",
"predictor_every": 0,
"stability_margin": 0.0,
"neutral_projection": True,
"max_steps": 352,
"validation_evaluations": 0,
"test_evaluations": 0,
}
for key, value in expected.items():
if record.get(key) != value:
raise ValueError(f"D1 {key} drift")
if record["provenance"]["git_tracked_dirty"]:
raise ValueError("tracked-dirty D1 record")
if record["split"]["validation_index_sha256"] != SPLIT_HASH:
raise ValueError("D1 split drift")
trajectory = record["trajectory"]
if len(trajectory) != 352:
raise ValueError("D1 trajectory is incomplete")
losses = [float(row["batch_loss"]) for row in trajectory]
signal_ratios = [
float(row["teaching_rms"])
/ max(float(row["instruction_rms"]), 1e-30)
for row in trajectory]
reports = [row["neutral_projection"] for row in trajectory]
if any(report is None for report in reports):
raise ValueError("D1 projection report is missing")
state_finite = all(
value["all_finite"]
for row in trajectory
for value in row["parameter_state"].values())
all_finite = all(math.isfinite(value)
for value in numeric_leaves(record))
def state_max(group):
return max(float(row["parameter_state"][group][
"max_abs_over_finite_tensors"]) for row in trajectory)
forward_weight_max = state_max("forward_weight")
feedback_weight_max = state_max("feedback_weight")
forward_momentum_max = state_max("forward_momentum")
feedback_momentum_max = state_max("feedback_momentum")
fit = record["predictor_warmup"]["closed_form_fit"]
ratio_errors = [abs(float(value) - 4.0) for value in
record["traffic_calibration"][
"realized_traffic_instruction_rms_ratio"]]
maximum_post_ratio = max(float(report[
"post_projection_traffic_rms_ratio"]) for report in reports)
maximum_post_slope = max(float(report[
"max_absolute_post_projection_soma_slope"]) for report in reports)
maximum_pre_ratio = max(float(report[
"pre_projection_traffic_rms_ratio"]) for report in reports)
maximum_correction_slope = max(float(report[
"max_absolute_correction_slope"]) for report in reports)
observation_counts = [int(report["observations"]) for report in reports]
instruction_observations = [
int(report["instruction_observations"]) for report in reports]
checks = {
"record_trajectory_and_state_finite": (
all_finite and state_finite
and record["first_any_nonfinite_step"] is None
and record["first_training_failure_step"] is None),
"maximum_batch_loss_at_most_10": max(losses) <= 10.0,
"final_32_mean_loss_at_most_2p5": (
statistics.mean(losses[-32:]) <= 2.5),
"used_instruction_rms_ratio_within_1e_minus_4": (
max(abs(value - 1.0) for value in signal_ratios) <= 1e-4),
"post_projection_traffic_ratio_at_most_1e_minus_5": (
maximum_post_ratio <= 1e-5),
"post_projection_soma_slope_at_most_1e_minus_5": (
maximum_post_slope <= 1e-5),
"paired_local_neutral_observations_only": (
min(observation_counts) >= 72
and max(observation_counts) <= 128
and max(instruction_observations) == 0),
"forward_and_feedback_weight_max_at_most_10": (
forward_weight_max <= 10.0 and feedback_weight_max <= 10.0),
"forward_and_feedback_momentum_max_at_most_50": (
forward_momentum_max <= 50.0
and feedback_momentum_max <= 50.0),
"zero_margin_64_observation_slow_fit": (
int(fit["observations"]) == 64
and float(fit["stability_margin"]) == 0.0),
"slow_predictor_frozen_during_task": all(
row["predictor_updated"] is False for row in trajectory),
"traffic_ratio_calibrated": max(ratio_errors) <= 1e-5,
"task_loader_state_restored": (
record["predictor_warmup"][
"task_loader_state_restored"] is True),
"no_held_out_evaluations": (
record["validation_evaluations"] == 0
and record["test_evaluations"] == 0),
}
passed = all(checks.values())
output = {
"protocol": "kp_dynamic_neutral_projection_training_prefix_v1",
"status": "passed" if passed else "failed",
"checks": checks,
"metrics": {
"maximum_batch_loss": max(losses),
"final_32_mean_loss": statistics.mean(losses[-32:]),
"maximum_used_instruction_rms_ratio_error": max(
abs(value - 1.0) for value in signal_ratios),
"maximum_pre_projection_traffic_rms_ratio": maximum_pre_ratio,
"maximum_post_projection_traffic_rms_ratio": maximum_post_ratio,
"maximum_post_projection_soma_slope": maximum_post_slope,
"maximum_correction_slope": maximum_correction_slope,
"minimum_projection_observations": min(observation_counts),
"maximum_projection_observations": max(observation_counts),
"maximum_forward_weight": forward_weight_max,
"maximum_feedback_weight": feedback_weight_max,
"maximum_forward_momentum": forward_momentum_max,
"maximum_feedback_momentum": feedback_momentum_max,
"maximum_initial_traffic_ratio_error": max(ratio_errors),
},
"source_commit": record["provenance"]["git_commit"],
"validation_evaluations": 0,
"test_evaluations": 0,
"review_score_before": 5,
"review_score_after": 5,
"score_change_rule": (
"training-only stability evidence cannot change the paper 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(output, indent=2))
if __name__ == "__main__":
main()
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