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#!/usr/bin/env python3
"""Audit and gate the frozen MT-1 mixed-traffic innovation panel."""
import argparse
import json
import math
import os
RULES = ("raw", "matched", "innovation")
SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b"
BP_EPOCH20_MACS = 109_487_808_000_000
KP_SHORT_ACCURACY = 0.8266
def mean_early(values):
count = max(1, len(values) // 3)
return sum(float(value) for value in values[:count]) / count
def numeric_leaves(value):
"""Yield every numeric audit value while excluding boolean flags."""
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_dir", default="results/kp_innovation_short")
parser.add_argument("--kp_full_gate", default="results/kp_full_gate.json")
parser.add_argument("--out", default="results/kp_innovation_short_gate.json")
args = parser.parse_args()
with open(args.kp_full_gate) as handle:
kp_gate = json.load(handle)
if (kp_gate.get("protocol") != "kolen_pollack_full_v1"
or kp_gate.get("status") != "passed"):
raise ValueError("a KP-2 pass is required")
records = {}
expected_common = {
"mode": "kp_traffic", "depth": 20, "width": 16, "seed": 0,
"loader_seed": 0, "batch_size": 128, "epochs": 20,
"train_limit": 0, "val_examples": 5000, "split_seed": 2027,
"eval_split": "validation", "eval_every": 0,
"augment_train": 1, "lr": 0.1, "output_lr": 0.1,
"lr_schedule": "step", "lr_milestones": "100,150",
"lr_gamma": 0.1, "warmup_epochs": 0, "momentum": 0.9,
"weight_decay": 1e-4, "normalization": "batchnorm",
"a_scale": 1.0, "traffic_seed": 4000, "traffic_ratio": 4.0,
"traffic_calibration_examples": 64, "learn_P": 1, "eta_P": 0.1,
"predictor_warmup_steps": 20, "predictor_every": 16,
"alignment_probe": 32,
}
source_commits = set()
for rule in RULES:
with open(os.path.join(args.input_dir, f"{rule}.json")) as handle:
record = json.load(handle)
records[rule] = record
run_args = record["args"]
for key, value in {**expected_common, "traffic_rule": rule}.items():
if run_args.get(key) != value:
raise ValueError(f"MT-1 {rule} {key} drift")
if record["provenance"]["git_tracked_dirty"]:
raise ValueError(f"tracked-dirty MT-1 {rule} result")
source_commits.add(record["provenance"]["git_commit"])
if record["split"]["validation_index_sha256"] != SPLIT_HASH:
raise ValueError(f"MT-1 {rule} split drift")
protocol = record["evaluation_protocol"]
if protocol["test_evaluations"] or protocol["test_used_for_selection"]:
raise ValueError(f"MT-1 {rule} touched test")
if record.get("calibration_metric_space") != (
"reciprocal_local_activity_products_with_mixed_apical_traffic"):
raise ValueError(f"MT-1 {rule} metric-space drift")
warmup = record.get("predictor_warmup", {})
if (warmup.get("instruction_present") is not False
or warmup.get("task_loader_state_restored") is not True):
raise ValueError(f"MT-1 {rule} neutral-warmup invariant failed")
if len(source_commits) != 1:
raise ValueError("MT-1 conditions must share one source revision")
accuracies = {rule: float(records[rule]["final"]["accuracy"])
for rule in RULES}
diagnostics = {rule: records[rule]["diagnostics"] for rule in RULES}
innovation_early = float(diagnostics["innovation"]["early_third_mean"])
innovation_raw_early = mean_early(
diagnostics["innovation"]["raw_negative_gradient_cosine"])
tracking = {
rule: [row.get("feedback_tracking") for row in records[rule]["epochs"]]
for rule in RULES
}
trajectory_values = []
for rule in RULES:
if len(tracking[rule]) != 20 or any(value is None for value in tracking[rule]):
raise ValueError(f"MT-1 {rule} tracking trajectory is incomplete")
for row, values in zip(records[rule]["epochs"], tracking[rule]):
mixed = row.get("mixed_apical")
if mixed is None:
raise ValueError(f"MT-1 {rule} mixed-apical trajectory is incomplete")
trajectory_values.extend([
float(row["train_loss"]),
float(values["mean_feedback_forward_cosine"]),
float(values["mean_feedback_forward_relative_error"]),
float(values["min_feedback_forward_cosine"]),
float(values["max_feedback_forward_relative_error"]),
float(mixed["teaching_rms"]),
float(mixed["instruction_rms"]),
float(mixed["raw_apical_rms"]),
float(mixed["innovation_rms"]),
float(mixed["traffic_rms"]),
])
initial_ratio_errors = []
predictor_residual_ratios = []
total_macs = {}
queries = {}
elementwise = {}
for rule, record in records.items():
initial_ratio_errors.extend(abs(float(value) - 4.0) for value in
record["traffic_calibration"]["realized_traffic_instruction_rms_ratio"])
predictor_residual_ratios.append(float(
record["predictor_warmup"]["post_warmup_traffic_residual_rms_ratio"]))
total_macs[rule] = int(record["work"]["total_macs_estimate"])
queries[rule] = int(record["work"]["logical_batch_loss_queries"])
elementwise[rule] = int(record["work"]["elementwise_operations_estimate"])
matched_norm_error = float(
diagnostics["matched"]["max_norm_match_relative_error"])
final_feedback_cosine = float(
diagnostics["innovation"]["mean_feedback_forward_cosine"])
late_feedback_cosine = sum(float(value["mean_feedback_forward_cosine"])
for value in tracking["innovation"][10:]) / 10
audit_values = trajectory_values + [innovation_early, innovation_raw_early,
final_feedback_cosine,
late_feedback_cosine,
matched_norm_error]
for record in records.values():
audit_values.extend(numeric_leaves(record["final"]))
audit_values.extend(numeric_leaves(record["diagnostics"]))
audit_values.extend(numeric_leaves(record["traffic_calibration"]))
audit_values.extend(numeric_leaves(record["predictor_warmup"]))
all_finite = all(record["final"]["finite"] for record in records.values())
all_finite = all_finite and all(
math.isfinite(value) for value in audit_values)
checks = {
"records_trajectories_and_diagnostics_finite": all_finite,
"innovation_accuracy_at_least_0.75": accuracies["innovation"] >= 0.75,
"innovation_within_5_points_of_clean_kp": (
accuracies["innovation"] >= KP_SHORT_ACCURACY - 0.05),
"innovation_gain_over_raw_at_least_0.05": (
accuracies["innovation"] - accuracies["raw"] >= 0.05),
"innovation_gain_over_matched_at_least_0.03": (
accuracies["innovation"] - accuracies["matched"] >= 0.03),
"innovation_early_alignment_at_least_0.70": innovation_early >= 0.70,
"same_network_alignment_gain_over_raw_at_least_0.15": (
innovation_early - innovation_raw_early >= 0.15),
"final_feedback_cosine_at_least_0.80": final_feedback_cosine >= 0.80,
"epoch11_to20_feedback_cosine_at_least_0.70": (
late_feedback_cosine >= 0.70),
"initial_layer_ratio_error_at_most_1e-5": (
max(initial_ratio_errors) <= 1e-5),
"post_warmup_predictor_residual_ratio_at_most_0.25": (
max(predictor_residual_ratios) <= 0.25),
"matched_norm_relative_error_at_most_1e-6": matched_norm_error <= 1e-6,
"zero_task_loss_queries": all(value == 0 for value in queries.values()),
"each_affine_mac_count_at_most_1.40x_bp": all(
value <= 1.40 * BP_EPOCH20_MACS for value in total_macs.values()),
"elementwise_cost_reported": all(value > 0 for value in elementwise.values()),
}
status = "passed" if all(checks.values()) else "failed"
output = {
"protocol": "kp_mixed_traffic_short_v1", "status": status,
"checks": checks,
"metrics": {
"accuracy": accuracies,
"clean_kp_accuracy": KP_SHORT_ACCURACY,
"innovation_gain_over_raw": (
accuracies["innovation"] - accuracies["raw"]),
"innovation_gain_over_matched": (
accuracies["innovation"] - accuracies["matched"]),
"innovation_early_third_alignment": innovation_early,
"same_innovation_network_raw_early_third_alignment": innovation_raw_early,
"final_feedback_forward_cosine": final_feedback_cosine,
"epoch11_to20_feedback_forward_cosine": late_feedback_cosine,
"max_initial_layer_ratio_error": max(initial_ratio_errors),
"max_post_warmup_predictor_residual_rms_ratio": (
max(predictor_residual_ratios)),
"matched_norm_relative_error": matched_norm_error,
"total_macs": total_macs,
"mac_ratio_to_bp": {rule: value / BP_EPOCH20_MACS
for rule, value in total_macs.items()},
"elementwise_operations_estimate": elementwise,
"logical_batch_loss_queries": queries,
"source_commit": next(iter(source_commits)),
},
"full_validation_opened": status == "passed",
"confirmation_test_seeds_touched": False,
"review_score_before": 5, "review_score_after": 5,
"score_change_rule": (
"a one-seed short mechanism screen cannot raise the review 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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