Add Gate anchor-calibration audit, split-half validation and geometric sensitivity
Browse files- rebuttal-metric-validation-v1/gate_calibration_audit/code_audit.json +28 -0
- rebuttal-metric-validation-v1/gate_calibration_audit/geometric_comparison.json +393 -0
- rebuttal-metric-validation-v1/gate_calibration_audit/protocol.json +16 -0
- rebuttal-metric-validation-v1/gate_calibration_audit/reproducibility/analyze_gate_calibration_audit.py +200 -0
- rebuttal-metric-validation-v1/gate_calibration_audit/reviewer_response_original.py +237 -0
- rebuttal-metric-validation-v1/gate_calibration_audit/split_half.json +4056 -0
rebuttal-metric-validation-v1/gate_calibration_audit/code_audit.json
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{
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"source_sha256": "28e31984a2ffad67db4e01e0effffc561528a0a43d89ecd4bf9c00a0d9daa8d0",
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"source_commit": [
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"07c13f889afaf4b3625ffa4dc67d31395d88e362",
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"2026-07-25T15:12:06+00:00",
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"2026-07-25T15:12:06+00:00",
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"Add offline reviewer-response analysis (no model inference)"
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],
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"constant_assignments": {
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"_id": "_id = per_task_arrays(load(\"anchor_identity\"))",
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"_gt": "_gt = per_task_arrays(load(\"anchor_gt\"))",
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"ID": "ID = {k: nanmean(v) for k, v in _id.items()}",
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"GT": "GT = {k: nanmean(v) for k, v in _gt.items()}"
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},
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"normalization_lines": [
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"denom = GT[\"material\"] - ID[\"material\"]",
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"smat_norm = np.clip((A[\"material\"] - ID[\"material\"]) / denom, 0, 1)"
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],
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"conclusion": "The constant-computation code reads only the two benchmark anchor score files; it does not use competing submission scores or human labels as calibration inputs and has no numerical optimization.",
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"mathematics": "mu_id = mean_i S_mat(identity_i); mu_gt = mean_i S_mat(exemplar_i); offset=mu_id and scale=mu_gt-mu_id. For fixed anchors, changing competitor scores or human labels cannot change these two constants.",
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"paper_rounded_means": {
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"identity": 0.242,
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"exemplar": 0.622,
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"difference": 0.38
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},
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"scope_of_proof": "Functional dependence of constants, not a proof of when the formula was conceived or what results the authors had seen. Clipping, multiplicative gating and equal weighting remain design choices. Exemplars are generated benchmark solutions, not solely task inputs.",
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"chronology": "This code commit precedes the current reviews and rebuttal, but processes already stored per-task scores. Its date cannot establish that the formula was fixed before the original results. The manuscript states that aggregates were pre-specified; do not infer preregistration from a code timestamp alone."
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}
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rebuttal-metric-validation-v1/gate_calibration_audit/geometric_comparison.json
ADDED
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| 1 |
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{
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"sources": [
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{
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| 4 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/bagel__real_photo__Cplus.json",
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| 5 |
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"sha256": "93a4c367726655c008bc2a83577f53933b92caa9710cbc26b9b25e81f29986c9"
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| 6 |
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},
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{
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| 8 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/gpt__real_photo__Cplus.json",
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| 9 |
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"sha256": "ba2563053b6d6aa7095b14441a24ce3f463ed1032066bec12672eeace4e09464"
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| 10 |
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| 11 |
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{
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| 12 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/identity__real_photo__anchor.json",
|
| 13 |
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"sha256": "174222441daa787898fca80d93f02e765407bbd4b93f64e89369bc3b47018291"
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| 14 |
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| 15 |
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{
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| 16 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/materialfusion__real_photo__C.json",
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| 17 |
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"sha256": "c6f94365ff8b2f9b26a18e146683e29eeba1c4cd998c0d0e8afafc8068bfea43"
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| 18 |
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| 19 |
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{
|
| 20 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/materialfusion__synthetic__C.json",
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| 21 |
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"sha256": "3ffbe819442ab573a8223d6559fcb9c11ec4d7b5fd5850501e53814b11c628d7"
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| 22 |
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{
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| 24 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/nano__real_photo__Cplus.json",
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| 25 |
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"sha256": "258c42a2d46d5594ccbd459c412a664e706a0d406bf3e0da939f2820d53107f6"
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| 26 |
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| 27 |
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{
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| 28 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_rebuttal/reports/omnigen2__real_photo__C.json",
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| 29 |
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| 32 |
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| 48 |
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| 49 |
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| 50 |
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| 52 |
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{
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| 72 |
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| 79 |
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{
|
| 80 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_real_photo_a/reports/bagel.json",
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| 81 |
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| 82 |
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| 83 |
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{
|
| 84 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_real_photo_a/reports/gpt.json",
|
| 85 |
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| 86 |
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| 87 |
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{
|
| 88 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_real_photo_a/reports/nano.json",
|
| 89 |
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| 90 |
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| 91 |
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{
|
| 92 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_real_photo_a/reports/omnigen2.json",
|
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|
rebuttal-metric-validation-v1/gate_calibration_audit/protocol.json
ADDED
|
@@ -0,0 +1,16 @@
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|
| 1 |
+
{
|
| 2 |
+
"scope": "Existing rebuttal subsets: 100 synthetic tasks and a separate 100 real-source tasks; not the original full 23-entry leaderboard.",
|
| 3 |
+
"fixed_gate": {
|
| 4 |
+
"identity": 0.242,
|
| 5 |
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"exemplar": 0.622,
|
| 6 |
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"scale": 0.38
|
| 7 |
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},
|
| 8 |
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"split_half": "Synthetic100: five tasks per material category in each half; calibrate only from identity/exemplar in the other half; swap halves and average all 100 held-out scores.",
|
| 9 |
+
"seed": 43096,
|
| 10 |
+
"repeated_split_count": 1000,
|
| 11 |
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"repeated_split_interpretation": "Sensitivity to the partition of these fixed 100 tasks; not a population confidence interval.",
|
| 12 |
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"rankings": "Kendall tau-b within each split/condition; anchors excluded; no correlation for one model.",
|
| 13 |
+
"geometric": "mean over tasks of (material * preservation * structure * harmony) ** (1/4); no anchor calibration",
|
| 14 |
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"source_code_commit": "07c13f889afaf4b3625ffa4dc67d31395d88e362",
|
| 15 |
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"script_sha256": "6fc3a14bf70fb532b27d4b58d34d18a8a63b4a4b6dd7cd92f11a3223baabe7f8"
|
| 16 |
+
}
|
rebuttal-metric-validation-v1/gate_calibration_audit/reproducibility/analyze_gate_calibration_audit.py
ADDED
|
@@ -0,0 +1,200 @@
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
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|
|
|
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|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""Audit Gate calibration dependencies and reaggregate existing, paired scores.
|
| 2 |
+
|
| 3 |
+
No inference or fitting to model rankings/human labels. The original 23-entry,
|
| 4 |
+
1,421-task score files are not present locally; empirical analyses below use
|
| 5 |
+
the completed 100-task rebuttal subsets and explicitly identify that scope.
|
| 6 |
+
"""
|
| 7 |
+
from __future__ import annotations
|
| 8 |
+
|
| 9 |
+
import ast
|
| 10 |
+
import hashlib
|
| 11 |
+
import json
|
| 12 |
+
import math
|
| 13 |
+
import subprocess
|
| 14 |
+
from collections import defaultdict
|
| 15 |
+
from pathlib import Path
|
| 16 |
+
|
| 17 |
+
import numpy as np
|
| 18 |
+
|
| 19 |
+
ROOT = Path(__file__).resolve().parent
|
| 20 |
+
WORKSPACE = ROOT.parent
|
| 21 |
+
OUT = ROOT / "analysis/gate_calibration_audit"
|
| 22 |
+
DIMS = ["material", "preservation", "structure", "harmony"]
|
| 23 |
+
SEED = 43096
|
| 24 |
+
REPEATS = 1000
|
| 25 |
+
ORIGINAL_COMMIT = "07c13f889afaf4b3625ffa4dc67d31395d88e362"
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def write_json(path, value):
|
| 29 |
+
path.write_text(json.dumps(value, indent=2, allow_nan=False) + "\n")
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
def tau_b(a, b):
|
| 33 |
+
"""Kendall tau-b, including ties, for the small model sets here."""
|
| 34 |
+
a, b = np.asarray(a), np.asarray(b)
|
| 35 |
+
i, j = np.triu_indices(len(a), 1)
|
| 36 |
+
sa, sb = np.sign(a[i] - a[j]), np.sign(b[i] - b[j])
|
| 37 |
+
denominator = math.sqrt(np.count_nonzero(sa) * np.count_nonzero(sb))
|
| 38 |
+
return float(np.sum(sa * sb) / denominator) if denominator else None
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def gate(v, lo=.242, hi=.622):
|
| 42 |
+
assert hi > lo
|
| 43 |
+
return np.clip((v[..., 0] - lo) / (hi - lo), 0, 1) * np.prod(v[..., 1:], axis=-1) ** (1 / 3)
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
def load_reports():
|
| 47 |
+
entries, sources = {}, []
|
| 48 |
+
for folder in ["matreplace_rebuttal", "matreplace_real_photo_a", "matreplace_gpt_real_bc"]:
|
| 49 |
+
for path in sorted((WORKSPACE / folder / "reports").glob("*.json")):
|
| 50 |
+
report = json.loads(path.read_text())
|
| 51 |
+
if not isinstance(report, dict) or not {"summary", "samples"} <= report.keys():
|
| 52 |
+
continue
|
| 53 |
+
meta = report["summary"]
|
| 54 |
+
# This folder also carries copies of the A/C+ comparison reports.
|
| 55 |
+
if folder == "matreplace_gpt_real_bc" and meta["track"] not in {"B", "C"}:
|
| 56 |
+
continue
|
| 57 |
+
key = (meta["split"], meta["track"], meta["model"])
|
| 58 |
+
assert key not in entries, key
|
| 59 |
+
rows = sorted(report["samples"], key=lambda r: r["sample_uid"])
|
| 60 |
+
assert len(rows) == len({r["sample_uid"] for r in rows}) == 100
|
| 61 |
+
entries[key] = rows
|
| 62 |
+
sources.append({"path": str(path), "sha256": hashlib.sha256(path.read_bytes()).hexdigest()})
|
| 63 |
+
assert len(entries) == 26
|
| 64 |
+
return entries, sources
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
def matrix(rows):
|
| 68 |
+
v = np.array([[r["scores"][d] for d in DIMS] for r in rows])
|
| 69 |
+
assert np.isfinite(v).all() and ((v >= 0) & (v <= 1)).all()
|
| 70 |
+
assert np.allclose(gate(v), [r["gate"] for r in rows], atol=1e-12, rtol=0)
|
| 71 |
+
assert np.allclose(np.prod(v, axis=1) ** .25, [r["geometric_overall"] for r in rows], atol=1e-12, rtol=0)
|
| 72 |
+
return v
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
def main():
|
| 76 |
+
OUT.mkdir(exist_ok=True)
|
| 77 |
+
protocol = {
|
| 78 |
+
"scope": "Existing rebuttal subsets: 100 synthetic tasks and a separate 100 real-source tasks; not the original full 23-entry leaderboard.",
|
| 79 |
+
"fixed_gate": {"identity": .242, "exemplar": .622, "scale": .380},
|
| 80 |
+
"split_half": "Synthetic100: five tasks per material category in each half; calibrate only from identity/exemplar in the other half; swap halves and average all 100 held-out scores.",
|
| 81 |
+
"seed": SEED, "repeated_split_count": REPEATS,
|
| 82 |
+
"repeated_split_interpretation": "Sensitivity to the partition of these fixed 100 tasks; not a population confidence interval.",
|
| 83 |
+
"rankings": "Kendall tau-b within each split/condition; anchors excluded; no correlation for one model.",
|
| 84 |
+
"geometric": "mean over tasks of (material * preservation * structure * harmony) ** (1/4); no anchor calibration",
|
| 85 |
+
"source_code_commit": ORIGINAL_COMMIT,
|
| 86 |
+
"script_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),
|
| 87 |
+
}
|
| 88 |
+
write_json(OUT / "protocol.json", protocol)
|
| 89 |
+
|
| 90 |
+
original = subprocess.check_output([
|
| 91 |
+
"git", "-C", str(WORKSPACE / "image_factory"), "show",
|
| 92 |
+
f"{ORIGINAL_COMMIT}:scripts/reviewer_response.py",
|
| 93 |
+
], text=True)
|
| 94 |
+
(OUT / "reviewer_response_original.py").write_text(original)
|
| 95 |
+
parsed = ast.parse(original)
|
| 96 |
+
bound = {}
|
| 97 |
+
for node in parsed.body:
|
| 98 |
+
if isinstance(node, ast.Assign):
|
| 99 |
+
for target in node.targets:
|
| 100 |
+
if isinstance(target, ast.Name) and target.id in {"_id", "_gt", "ID", "GT"}:
|
| 101 |
+
bound[target.id] = ast.get_source_segment(original, node)
|
| 102 |
+
assert bound["_id"] == '_id = per_task_arrays(load("anchor_identity"))'
|
| 103 |
+
assert bound["_gt"] == '_gt = per_task_arrays(load("anchor_gt"))'
|
| 104 |
+
metadata = subprocess.check_output([
|
| 105 |
+
"git", "-C", str(WORKSPACE / "image_factory"), "show", "-s",
|
| 106 |
+
"--format=%H%n%aI%n%cI%n%s", ORIGINAL_COMMIT,
|
| 107 |
+
], text=True).splitlines()
|
| 108 |
+
audit = {
|
| 109 |
+
"source_sha256": hashlib.sha256(original.encode()).hexdigest(),
|
| 110 |
+
"source_commit": metadata,
|
| 111 |
+
"constant_assignments": bound,
|
| 112 |
+
"normalization_lines": [s.strip() for s in original.splitlines() if s.strip().startswith(("denom =", "smat_norm ="))],
|
| 113 |
+
"conclusion": "The constant-computation code reads only the two benchmark anchor score files; it does not use competing submission scores or human labels as calibration inputs and has no numerical optimization.",
|
| 114 |
+
"mathematics": "mu_id = mean_i S_mat(identity_i); mu_gt = mean_i S_mat(exemplar_i); offset=mu_id and scale=mu_gt-mu_id. For fixed anchors, changing competitor scores or human labels cannot change these two constants.",
|
| 115 |
+
"paper_rounded_means": {"identity": .242, "exemplar": .622, "difference": .380},
|
| 116 |
+
"scope_of_proof": "Functional dependence of constants, not a proof of when the formula was conceived or what results the authors had seen. Clipping, multiplicative gating and equal weighting remain design choices. Exemplars are generated benchmark solutions, not solely task inputs.",
|
| 117 |
+
"chronology": "This code commit precedes the current reviews and rebuttal, but processes already stored per-task scores. Its date cannot establish that the formula was fixed before the original results. The manuscript states that aggregates were pre-specified; do not infer preregistration from a code timestamp alone.",
|
| 118 |
+
}
|
| 119 |
+
write_json(OUT / "code_audit.json", audit)
|
| 120 |
+
|
| 121 |
+
entries, sources = load_reports()
|
| 122 |
+
matrices = {key: matrix(rows) for key, rows in entries.items()}
|
| 123 |
+
groups = defaultdict(list)
|
| 124 |
+
for key in entries:
|
| 125 |
+
if key[1] != "anchor":
|
| 126 |
+
groups[key[:2]].append(key)
|
| 127 |
+
geometric = []
|
| 128 |
+
for group, keys in sorted(groups.items()):
|
| 129 |
+
keys.sort()
|
| 130 |
+
assert len({tuple(r["sample_uid"] for r in entries[k]) for k in keys}) == 1
|
| 131 |
+
fixed = np.array([gate(matrices[k]).mean() for k in keys])
|
| 132 |
+
geo = np.array([(np.prod(matrices[k], axis=1) ** .25).mean() for k in keys])
|
| 133 |
+
raw = np.array([matrices[k].mean() for k in keys])
|
| 134 |
+
geometric.append({
|
| 135 |
+
"group": "/".join(group), "n_tasks": 100, "n_models": len(keys),
|
| 136 |
+
"tau_geometric_vs_gate": tau_b(geo, fixed), "tau_geometric_vs_raw": tau_b(geo, raw),
|
| 137 |
+
"gate_order": ["/".join(keys[i]) for i in np.argsort(-fixed)],
|
| 138 |
+
"geometric_order": ["/".join(keys[i]) for i in np.argsort(-geo)],
|
| 139 |
+
"means": [{"entry": "/".join(k), "gate": float(fixed[i]), "geometric": float(geo[i]), "raw": float(raw[i])} for i, k in enumerate(keys)],
|
| 140 |
+
})
|
| 141 |
+
write_json(OUT / "geometric_comparison.json", {"sources": sources, "groups": geometric})
|
| 142 |
+
|
| 143 |
+
jobs = sorted([json.loads(s) for s in (WORKSPACE / "matreplace_rebuttal/inputs/synthetic/tasks.jsonl").read_text().splitlines()], key=lambda r: r["sample_uid"])
|
| 144 |
+
uids = [r["sample_uid"] for r in jobs]
|
| 145 |
+
strata = defaultdict(list)
|
| 146 |
+
for i, job in enumerate(jobs):
|
| 147 |
+
strata[job["material_category"]].append(i)
|
| 148 |
+
assert len(strata) == 10 and all(len(s) == 10 for s in strata.values())
|
| 149 |
+
anchors = {}
|
| 150 |
+
anchor_sources = []
|
| 151 |
+
for model in ["identity", "exemplar"]:
|
| 152 |
+
anchors[model] = []
|
| 153 |
+
for uid in uids:
|
| 154 |
+
path = ROOT / "scores/anchors" / model / f"{uid}.json"
|
| 155 |
+
row = json.loads(path.read_text())
|
| 156 |
+
assert row["sample_uid"] == uid
|
| 157 |
+
anchors[model].append(row["scores"]["material"])
|
| 158 |
+
anchor_sources.append({"path": str(path), "sha256": hashlib.sha256(path.read_bytes()).hexdigest()})
|
| 159 |
+
anchors[model] = np.array(anchors[model])
|
| 160 |
+
keys = sorted(k for k in entries if k[0] == "synthetic")
|
| 161 |
+
assert len(keys) == 8
|
| 162 |
+
assert all([r["sample_uid"] for r in entries[k]] == uids for k in keys)
|
| 163 |
+
v = np.stack([matrices[k] for k in keys])
|
| 164 |
+
fixed = gate(v)
|
| 165 |
+
grouped_indices = {track: [i for i,k in enumerate(keys) if k[1] == track] for track in ["B", "C", "Cplus"]}
|
| 166 |
+
rng = np.random.default_rng(SEED)
|
| 167 |
+
repeated = defaultdict(list)
|
| 168 |
+
first = None
|
| 169 |
+
for repeat in range(REPEATS):
|
| 170 |
+
half = np.ones(100, dtype=int)
|
| 171 |
+
for stratum in strata.values():
|
| 172 |
+
half[rng.permutation(stratum)[:5]] = 0
|
| 173 |
+
crossfit = np.zeros_like(fixed)
|
| 174 |
+
folds = []
|
| 175 |
+
for f in [0, 1]:
|
| 176 |
+
train, test = half != f, half == f
|
| 177 |
+
lo, hi = anchors["identity"][train].mean(), anchors["exemplar"][train].mean()
|
| 178 |
+
assert train.sum() == test.sum() == 50 and not np.any(train & test)
|
| 179 |
+
crossfit[:, test] = gate(v[:, test], lo, hi)
|
| 180 |
+
folds.append({"test_half": f, "calibration_n": 50, "test_n": 50, "identity_mean": float(lo), "exemplar_mean": float(hi), "scale": float(hi-lo), "heldout_uids": [uids[i] for i in np.where(test)[0]]})
|
| 181 |
+
rankings = []
|
| 182 |
+
for track, indices in grouped_indices.items():
|
| 183 |
+
base, current = fixed[indices].mean(1), crossfit[indices].mean(1)
|
| 184 |
+
tau = tau_b(base, current)
|
| 185 |
+
repeated[track].append(tau)
|
| 186 |
+
rankings.append({"track": track, "models": len(indices), "tau": tau, "fixed_order": ["/".join(keys[indices[i]]) for i in np.argsort(-base)], "crossfit_order": ["/".join(keys[indices[i]]) for i in np.argsort(-current)]})
|
| 187 |
+
if repeat == 0:
|
| 188 |
+
first = {"folds": folds, "rankings": rankings, "means": [{"entry": "/".join(k), "fixed": float(fixed[i].mean()), "crossfit": float(crossfit[i].mean())} for i,k in enumerate(keys)]}
|
| 189 |
+
result = {
|
| 190 |
+
"n_tasks": 100, "n_entries": 8, "seed": SEED, "first_split": first,
|
| 191 |
+
"repeated_splits": [{"track": t, "splits": REPEATS, "tau_min": float(min(vals)), "tau_median": float(np.median(vals)), "tau_max": float(max(vals)), "fraction_tau_one": float(np.mean(np.isclose(vals,1))), "tau_values": vals} for t,vals in repeated.items()],
|
| 192 |
+
"anchor_sources": anchor_sources,
|
| 193 |
+
"limitations": ["A diagnostic balanced sample of 100 synthetic tasks, not the original full leaderboard.", "Two-model rankings in B/C+ are coarse diagnostics; C includes four models.", "Cross-fitting separates calibration tasks from scoring tasks; it does not eliminate generator dependence of the exemplar anchors.", "Split repetition quantifies partition sensitivity conditional on these tasks and scores; it is not a population confidence interval."],
|
| 194 |
+
}
|
| 195 |
+
write_json(OUT / "split_half.json", result)
|
| 196 |
+
print(json.dumps({"code_audit": audit["constant_assignments"], "split_half": first["rankings"], "repeated_splits": [{k:v for k,v in r.items() if k!='tau_values'} for r in result['repeated_splits']], "geometric": [{k:r[k] for k in ['group','n_models','tau_geometric_vs_gate']} for r in geometric]}, indent=2))
|
| 197 |
+
|
| 198 |
+
|
| 199 |
+
if __name__ == "__main__":
|
| 200 |
+
main()
|
rebuttal-metric-validation-v1/gate_calibration_audit/reviewer_response_original.py
ADDED
|
@@ -0,0 +1,237 @@
|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""Offline reviewer-response analyses, recomputed from stored per-task scores.
|
| 3 |
+
|
| 4 |
+
No model inference: every number here is a re-aggregation of the per-task
|
| 5 |
+
component scores already in ``results/*_full.json`` (material clip/dino,
|
| 6 |
+
preservation lpips, structure depth, harmony ncc/seam, plus the taxonomy
|
| 7 |
+
labels). Produces four artifacts requested in review:
|
| 8 |
+
|
| 9 |
+
1. DINOv2-ablation leaderboard -- S_mat with the DINOv2 cosine term removed
|
| 10 |
+
(S_mat' = 1{family correct}), to show the ranking does not depend on it.
|
| 11 |
+
2. Identity-robust main metrics -- two candidates that de-saturate the
|
| 12 |
+
``do-nothing'' anchor:
|
| 13 |
+
(G) gate : overall_G = Smat_norm * geomean(pres, struct, harm)
|
| 14 |
+
(N) linear : overall_N = (overall_raw - id) / (gt - id) [rank-preserving]
|
| 15 |
+
reported with where identity / paste / gt land under each.
|
| 16 |
+
3. Stratified tables -- overall + dims per material category, surface type,
|
| 17 |
+
and coverage bucket, for the ranked systems.
|
| 18 |
+
4. Significance audit -- flags cross-track / near-tie deltas with |Δ|<0.02
|
| 19 |
+
that are marked significant but sit inside plausible single-seed noise.
|
| 20 |
+
|
| 21 |
+
Writes results/reviewer_response.json and prints readable tables.
|
| 22 |
+
"""
|
| 23 |
+
from __future__ import annotations
|
| 24 |
+
|
| 25 |
+
import json
|
| 26 |
+
from pathlib import Path
|
| 27 |
+
|
| 28 |
+
import numpy as np
|
| 29 |
+
|
| 30 |
+
REPO = Path(__file__).resolve().parents[1]
|
| 31 |
+
RESULTS = REPO / "results"
|
| 32 |
+
DIMS = ("material", "preservation", "structure", "harmony")
|
| 33 |
+
|
| 34 |
+
# 23 paper entries: (submission stem, display, track)
|
| 35 |
+
ENTRIES = [
|
| 36 |
+
("gpt-image-2-A", "GPT-Image-2", "A"), ("nanobanana2lite-A", "Nano-Banana-2-Lite", "A"),
|
| 37 |
+
("qwen-image-edit", "Qwen-Image-Edit", "A"), ("bagel-A", "BAGEL-7B", "A"),
|
| 38 |
+
("flux-kontext", "FLUX-Kontext", "A"), ("omnigen2-A", "OmniGen2", "A"),
|
| 39 |
+
("instruct-pix2pix", "InstructPix2Pix", "A"),
|
| 40 |
+
("gpt-image-2-B", "GPT-Image-2", "B"), ("nanobanana2lite-B", "Nano-Banana-2-Lite", "B"),
|
| 41 |
+
("flux-kontext-inpaint", "FLUX-Kontext", "B"), ("qwen-image-edit-inpaint", "Qwen-Image-Edit", "B"),
|
| 42 |
+
("flux-fill", "FLUX.1-Fill", "B"), ("bagel-B", "BAGEL-7B", "B"),
|
| 43 |
+
("sdxl-inpaint", "SDXL-inpaint", "B"), ("sd35-inpaint", "SD3.5-inpaint", "B"),
|
| 44 |
+
("omnigen2-B", "OmniGen2", "B"),
|
| 45 |
+
("gpt-image-2-C", "GPT-Image-2", "C"), ("nanobanana2lite-C", "Nano-Banana-2-Lite", "C"),
|
| 46 |
+
("paint-by-example", "Paint-by-Example", "C"), ("flux-kontext-ref", "FLUX-Kontext", "C"),
|
| 47 |
+
("qwen-image-edit-ref", "Qwen-Image-Edit", "C"), ("omnigen2-C", "OmniGen2", "C"),
|
| 48 |
+
("bagel-C", "BAGEL-7B", "C"),
|
| 49 |
+
]
|
| 50 |
+
ANCHORS = [("anchor_gt", "gt"), ("anchor_identity", "identity"), ("anchor_paste", "paste")]
|
| 51 |
+
|
| 52 |
+
# systems shown in the stratified tables (one strong closed, spread of open)
|
| 53 |
+
STRATA_SYSTEMS = ["gpt-image-2-A", "nanobanana2lite-A", "qwen-image-edit",
|
| 54 |
+
"flux-kontext", "bagel-A"]
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
def load(stem: str) -> dict:
|
| 58 |
+
return json.loads((RESULTS / f"{stem}_full.json").read_text())
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
def smat_family_only(sample: dict) -> float:
|
| 62 |
+
"""S_mat with the DINOv2 term dropped: 1{family correct}, or dino if the
|
| 63 |
+
target category is outside the vocabulary (family_correct is None)."""
|
| 64 |
+
m = sample["material"]
|
| 65 |
+
fam = m.get("family_correct")
|
| 66 |
+
if fam is None:
|
| 67 |
+
return max(0.0, m.get("dino_ref_cos") or 0.0)
|
| 68 |
+
return 1.0 if fam else 0.0
|
| 69 |
+
|
| 70 |
+
|
| 71 |
+
def per_task_arrays(d: dict):
|
| 72 |
+
"""Return dict of arrays over tasks: the 4 stored dim scores + family-only mat."""
|
| 73 |
+
mat, pres, struct, harm, mat_fam = [], [], [], [], []
|
| 74 |
+
for s in d["samples"]:
|
| 75 |
+
sc = s.get("scores")
|
| 76 |
+
if not sc:
|
| 77 |
+
continue
|
| 78 |
+
mat.append(sc["material"]); pres.append(sc["preservation"])
|
| 79 |
+
struct.append(sc["structure"]); harm.append(sc["harmony"])
|
| 80 |
+
mat_fam.append(smat_family_only(s))
|
| 81 |
+
A = {k: np.array(v, float) for k, v in
|
| 82 |
+
dict(material=mat, preservation=pres, structure=struct, harmony=harm,
|
| 83 |
+
material_fam=mat_fam).items()}
|
| 84 |
+
return A
|
| 85 |
+
|
| 86 |
+
|
| 87 |
+
def nanmean(x):
|
| 88 |
+
return float(np.nanmean(x))
|
| 89 |
+
|
| 90 |
+
|
| 91 |
+
# ---------------------------------------------------------------------------
|
| 92 |
+
# global anchor constants (identity floor, gt ceiling) for normalization
|
| 93 |
+
# ---------------------------------------------------------------------------
|
| 94 |
+
_id = per_task_arrays(load("anchor_identity"))
|
| 95 |
+
_gt = per_task_arrays(load("anchor_gt"))
|
| 96 |
+
ID = {k: nanmean(v) for k, v in _id.items()}
|
| 97 |
+
GT = {k: nanmean(v) for k, v in _gt.items()}
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
def geomean3(pres, struct, harm):
|
| 101 |
+
p = np.clip(pres, 0, 1) * np.clip(struct, 0, 1) * np.clip(harm, 0, 1)
|
| 102 |
+
return np.cbrt(np.clip(p, 0, 1))
|
| 103 |
+
|
| 104 |
+
|
| 105 |
+
def metrics_for(A: dict) -> dict:
|
| 106 |
+
"""All aggregate metrics for one submission's per-task arrays."""
|
| 107 |
+
raw_dims = {k: nanmean(A[k]) for k in DIMS}
|
| 108 |
+
overall_raw = float(np.mean(list(raw_dims.values())))
|
| 109 |
+
|
| 110 |
+
# DINOv2-ablation overall (material -> family-only)
|
| 111 |
+
over_nodino = float(np.mean([nanmean(A["material_fam"]),
|
| 112 |
+
raw_dims["preservation"], raw_dims["structure"],
|
| 113 |
+
raw_dims["harmony"]]))
|
| 114 |
+
|
| 115 |
+
# Candidate G: gate. Smat_norm maps identity material->0, gt material->1.
|
| 116 |
+
denom = GT["material"] - ID["material"]
|
| 117 |
+
smat_norm = np.clip((A["material"] - ID["material"]) / denom, 0, 1)
|
| 118 |
+
g = geomean3(A["preservation"], A["structure"], A["harmony"])
|
| 119 |
+
over_gate = nanmean(smat_norm * g)
|
| 120 |
+
# gate variant using family-only material
|
| 121 |
+
denom_f = GT["material_fam"] - ID["material_fam"]
|
| 122 |
+
smat_norm_f = np.clip((A["material_fam"] - ID["material_fam"]) / denom_f, 0, 1)
|
| 123 |
+
over_gate_fam = nanmean(smat_norm_f * g)
|
| 124 |
+
|
| 125 |
+
# Candidate N: linear identity-normalization of the raw overall (rank-preserving)
|
| 126 |
+
over_lin = (overall_raw - ID["overall"]) / (GT["overall"] - ID["overall"])
|
| 127 |
+
|
| 128 |
+
return dict(**raw_dims, overall_raw=overall_raw, mat_fam=nanmean(A["material_fam"]),
|
| 129 |
+
over_nodino=over_nodino, over_gate=over_gate,
|
| 130 |
+
over_gate_fam=over_gate_fam, over_lin=over_lin)
|
| 131 |
+
|
| 132 |
+
|
| 133 |
+
# identity/gt overall for the linear metric
|
| 134 |
+
ID["overall"] = float(np.mean([ID[k] for k in DIMS]))
|
| 135 |
+
GT["overall"] = float(np.mean([GT[k] for k in DIMS]))
|
| 136 |
+
|
| 137 |
+
|
| 138 |
+
def main():
|
| 139 |
+
out = {"entries": {}, "anchors": {}, "strata": {}, "constants": {"ID": ID, "GT": GT}}
|
| 140 |
+
|
| 141 |
+
rows = []
|
| 142 |
+
for stem, disp, tr in ENTRIES:
|
| 143 |
+
A = per_task_arrays(load(stem))
|
| 144 |
+
m = metrics_for(A)
|
| 145 |
+
m.update(display=disp, track=tr, stem=stem)
|
| 146 |
+
out["entries"][stem] = m
|
| 147 |
+
rows.append(m)
|
| 148 |
+
for stem, name in ANCHORS:
|
| 149 |
+
A = per_task_arrays(load(stem))
|
| 150 |
+
m = metrics_for(A)
|
| 151 |
+
out["anchors"][name] = m
|
| 152 |
+
|
| 153 |
+
id_raw = out["anchors"]["identity"]["overall_raw"]
|
| 154 |
+
|
| 155 |
+
# ---- Table: raw vs no-DINOv2 vs gate vs linear ----
|
| 156 |
+
print("=" * 92)
|
| 157 |
+
print("LEADERBOARD: raw overall vs DINOv2-ablation vs candidate identity-robust metrics")
|
| 158 |
+
print("=" * 92)
|
| 159 |
+
hdr = f"{'model':22s}{'tr':>3s} {'Smat':>6s}{'Smat*':>6s} {'raw':>7s}{'noDINO':>7s} {'gate':>7s}{'gateF':>7s}{'lin':>7s}"
|
| 160 |
+
print(hdr)
|
| 161 |
+
for m in rows:
|
| 162 |
+
print(f"{m['display']:22s}{m['track']:>3s} {m['material']:6.3f}{m['mat_fam']:6.3f} "
|
| 163 |
+
f"{m['overall_raw']:7.3f}{m['over_nodino']:7.3f} {m['over_gate']:7.3f}"
|
| 164 |
+
f"{m['over_gate_fam']:7.3f}{m['over_lin']:7.3f}")
|
| 165 |
+
print("-" * 92)
|
| 166 |
+
for name in ("gt", "identity", "paste"):
|
| 167 |
+
m = out["anchors"][name]
|
| 168 |
+
print(f"{'anchor:'+name:22s}{'--':>3s} {m['material']:6.3f}{m['mat_fam']:6.3f} "
|
| 169 |
+
f"{m['overall_raw']:7.3f}{m['over_nodino']:7.3f} {m['over_gate']:7.3f}"
|
| 170 |
+
f"{m['over_gate_fam']:7.3f}{m['over_lin']:7.3f}")
|
| 171 |
+
print("(* = family-only material, DINOv2 term dropped; gateF = gate with family-only material)")
|
| 172 |
+
|
| 173 |
+
# ---- how many entries clear the identity floor, per metric ----
|
| 174 |
+
def above(key, floor):
|
| 175 |
+
return sum(1 for m in rows if m[key] > floor)
|
| 176 |
+
print(f"\nentries strictly above the identity anchor (n=23):")
|
| 177 |
+
print(f" raw overall (floor {id_raw:.3f}): {above('overall_raw', id_raw)}")
|
| 178 |
+
print(f" no-DINOv2 (floor {out['anchors']['identity']['over_nodino']:.3f}): {above('over_nodino', out['anchors']['identity']['over_nodino'])}")
|
| 179 |
+
print(f" gate (floor {out['anchors']['identity']['over_gate']:.3f}): {above('over_gate', out['anchors']['identity']['over_gate'])}")
|
| 180 |
+
print(f" gate-family (floor {out['anchors']['identity']['over_gate_fam']:.3f}): {above('over_gate_fam', out['anchors']['identity']['over_gate_fam'])}")
|
| 181 |
+
|
| 182 |
+
# ---- Kendall tau: does re-ranking change under each metric? ----
|
| 183 |
+
import itertools
|
| 184 |
+
def tau(a, b):
|
| 185 |
+
c = disc = 0
|
| 186 |
+
for i, j in itertools.combinations(range(len(a)), 2):
|
| 187 |
+
s = np.sign(a[i] - a[j]) * np.sign(b[i] - b[j])
|
| 188 |
+
if s > 0: c += 1
|
| 189 |
+
elif s < 0: disc += 1
|
| 190 |
+
return (c - disc) / (c + disc)
|
| 191 |
+
raw = [m["overall_raw"] for m in rows]
|
| 192 |
+
for key, lab in [("over_nodino", "no-DINOv2"), ("over_gate", "gate"),
|
| 193 |
+
("over_gate_fam", "gate-fam"), ("over_lin", "linear")]:
|
| 194 |
+
v = [m[key] for m in rows]
|
| 195 |
+
print(f" Kendall tau(raw vs {lab:9s}) over 23 entries = {tau(raw, v):.3f}")
|
| 196 |
+
out.setdefault("tau_vs_raw", {})[lab] = tau(raw, v)
|
| 197 |
+
|
| 198 |
+
# ---- Stratified tables ----
|
| 199 |
+
print("\n" + "=" * 92)
|
| 200 |
+
print("STRATIFIED analysis (overall, per stratum) -- addresses 'claimed but absent'")
|
| 201 |
+
print("=" * 92)
|
| 202 |
+
for key, title in [("material_category", "material category"),
|
| 203 |
+
("surface_type", "surface type"),
|
| 204 |
+
("coverage_bucket", "mask-coverage bucket")]:
|
| 205 |
+
# collect strata and counts from gpt-A (labels identical across submissions)
|
| 206 |
+
base = load("gpt-image-2-A")["samples"]
|
| 207 |
+
counts = {}
|
| 208 |
+
for s in base:
|
| 209 |
+
counts[s.get(key, "?")] = counts.get(s.get(key, "?"), 0) + 1
|
| 210 |
+
strata = [k for k, _ in sorted(counts.items(), key=lambda kv: -kv[1])]
|
| 211 |
+
print(f"\n-- by {title} --")
|
| 212 |
+
print(f"{'system':22s}" + "".join(f"{k[:8]:>9s}" for k in strata))
|
| 213 |
+
print(f"{'(n)':22s}" + "".join(f"{counts[k]:>9d}" for k in strata))
|
| 214 |
+
tbl = {}
|
| 215 |
+
for stem in STRATA_SYSTEMS:
|
| 216 |
+
d = load(stem)
|
| 217 |
+
per = {k: [] for k in strata}
|
| 218 |
+
for s in d["samples"]:
|
| 219 |
+
sc = s.get("scores")
|
| 220 |
+
if not sc:
|
| 221 |
+
continue
|
| 222 |
+
vals = [sc[dd] for dd in DIMS if np.isfinite(sc[dd])]
|
| 223 |
+
if vals:
|
| 224 |
+
per[s.get(key, "?")].append(float(np.mean(vals)))
|
| 225 |
+
row = {k: (round(float(np.mean(v)), 3) if v else None) for k, v in per.items()}
|
| 226 |
+
tbl[stem] = row
|
| 227 |
+
disp = next(d2 for st, d2, _ in ENTRIES if st == stem)
|
| 228 |
+
print(f"{disp:22s}" + "".join(
|
| 229 |
+
f"{row[k]:>9.3f}" if row[k] is not None else f"{'-':>9s}" for k in strata))
|
| 230 |
+
out["strata"][key] = {"counts": counts, "table": tbl}
|
| 231 |
+
|
| 232 |
+
(RESULTS / "reviewer_response.json").write_text(json.dumps(out, indent=1, default=float))
|
| 233 |
+
print("\nwrote results/reviewer_response.json")
|
| 234 |
+
|
| 235 |
+
|
| 236 |
+
if __name__ == "__main__":
|
| 237 |
+
main()
|
rebuttal-metric-validation-v1/gate_calibration_audit/split_half.json
ADDED
|
@@ -0,0 +1,4056 @@
|
|
|
|
|
|
|
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|
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| 1 |
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| 2 |
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| 3 |
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"path": "/mlx_devbox/users/mz.du/playground/matreplace_remaining/scores/anchors/exemplar/fddb1eef.json",
|
| 4047 |
+
"sha256": "9d36fed2b3983b76fe6e2c96b35b84f072e896afd0232be594b0fb9ba9c21456"
|
| 4048 |
+
}
|
| 4049 |
+
],
|
| 4050 |
+
"limitations": [
|
| 4051 |
+
"A diagnostic balanced sample of 100 synthetic tasks, not the original full leaderboard.",
|
| 4052 |
+
"Two-model rankings in B/C+ are coarse diagnostics; C includes four models.",
|
| 4053 |
+
"Cross-fitting separates calibration tasks from scoring tasks; it does not eliminate generator dependence of the exemplar anchors.",
|
| 4054 |
+
"Split repetition quantifies partition sensitivity conditional on these tasks and scores; it is not a population confidence interval."
|
| 4055 |
+
]
|
| 4056 |
+
}
|