From fc8fe99504fe86a3636721031a7dc3d41a7909a6 Mon Sep 17 00:00:00 2001 From: YurenHao0426 Date: Wed, 22 Jul 2026 12:36:26 -0500 Subject: protocol: freeze post-failure Oral-A v2 funnel --- experiments/analyze_oral_a_v2_calibration.py | 137 +++++++++++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 experiments/analyze_oral_a_v2_calibration.py (limited to 'experiments/analyze_oral_a_v2_calibration.py') diff --git a/experiments/analyze_oral_a_v2_calibration.py b/experiments/analyze_oral_a_v2_calibration.py new file mode 100644 index 0000000..af2bd39 --- /dev/null +++ b/experiments/analyze_oral_a_v2_calibration.py @@ -0,0 +1,137 @@ +#!/usr/bin/env python3 +"""Apply the frozen Oral-A-v2 causal-capture gate.""" +import argparse +import glob +import json +import math +import os + + +MODES = ("unit_targets", "channel_subspace") +RATES = (0.01, 0.1, 1.0) +SPLIT_HASH = "8328b206a97c420e49e54e3eca4abe3274c4756b084355784ea3fb8059e4515b" + + +def load(path): + with open(path) as handle: + record = json.load(handle) + args = record["args"] + expected = { + "mode": "sdil", "depth": 20, "width": 16, "seed": 0, + "epochs": 0, "train_limit": 10000, "val_examples": 5000, + "a_warmup_steps": 400, "pert_directions": 1, "pert_every": 4, + "pert_sigma": 0.01, "perturb_seed": 1000, + "normalization": "batchnorm", "vectorizer_mode": "channel_gated", + "a_scale": 0.0, "alignment_probe": 64, + } + 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["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}") + mode = args["apical_calibration_mode"] + expected_space = ("channel_basis_moments" if mode == "channel_subspace" + else "full_hidden_field") + if record.get("calibration_metric_space") != expected_space: + raise ValueError(f"calibration metric-space drift: {path}") + diagnostics = record.get("diagnostics") + warmup = record.get("apical_warmup", {}).get("mean") + if diagnostics is None or warmup is None: + raise ValueError(f"missing diagnostics/warmup aggregate: {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), + "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.get("parameter_update_rms", 0.0), + } + finite = (record["final"]["finite"] + and all(math.isfinite(value) for value in metrics.values())) + return { + "path": path, + "source_commit": record["provenance"]["git_commit"], + "calibration_mode": mode, + "eta_A": float(args["eta_A"]), + "metric_space": expected_space, + "metrics": metrics, + "finite": finite, + } + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="results/oral_a_v2_calibration") + parser.add_argument("--out", default="results/oral_a_v2_calibration_gate.json") + args = parser.parse_args() + rows = [load(path) for path in sorted(glob.glob( + os.path.join(args.input, "*.json")))] + observed = {(row["calibration_mode"], row["eta_A"]) for row in rows} + expected = {(mode, rate) for mode in MODES for rate in RATES} + if observed != expected or len(rows) != len(expected): + raise ValueError( + f"incomplete v2 grid: missing={expected-observed}, extra={observed-expected}") + if len({row["source_commit"] for row in rows}) != 1: + raise ValueError("v2 calibration source commits differ") + + selected = {} + for mode in MODES: + candidates = [row for row in rows + if row["calibration_mode"] == mode and row["finite"]] + if candidates: + candidates.sort(key=lambda row: ( + -row["metrics"]["early_third_alignment"], + -row["metrics"]["all_layer_alignment"], row["eta_A"])) + selected[mode] = candidates[0] + checks = { + "all_six_records_finite": all(row["finite"] for row in rows), + "both_modes_selected": len(selected) == len(MODES), + } + if checks["both_modes_selected"]: + structured = selected["channel_subspace"]["metrics"] + unit = selected["unit_targets"]["metrics"] + checks.update({ + "structured_early_third_at_least_0.01": ( + structured["early_third_alignment"] >= 0.01), + "structured_all_layer_at_least_0.01": ( + structured["all_layer_alignment"] >= 0.01), + "structured_early_gain_over_unit_at_least_0.01": ( + structured["early_third_alignment"] + - unit["early_third_alignment"] >= 0.01), + }) + else: + checks.update({ + "structured_early_third_at_least_0.01": False, + "structured_all_layer_at_least_0.01": False, + "structured_early_gain_over_unit_at_least_0.01": False, + }) + passed = all(checks.values()) + output = { + "protocol": "oral_a_v2_causal_capture_v1", + "status": "passed" if passed else "failed", + "checks": checks, + "rows": rows, + "selected": selected, + "confirmation_test_seeds_touched": False, + "review_score_before": 5, + "review_score_after": 5, + "score_change_rule": "mechanics/calibration 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, + "selected": selected, + }, indent=2)) + + +if __name__ == "__main__": + main() + -- cgit v1.2.3