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"""2x2 quantization-regime study analysis (QAT Q4_0 vs PTQ Q4_K_M
across Gemma 4 12B dense and 26B MoE, governance routing tasks).
Reads operate-fr-bench row-level results + labels + suite datasets
(authoritative per-task family and smoke-stem ids); writes CSVs and a JSON
summary into ../data/. All routing statistics are recomputed from row
level. Exception: the safety-rate metrics (stale_commitment,
unsupported_current_claim, over_verification) quoted in the paper come
from the harness scorer's *_summary.json outputs, not from this script.
"""
import csv
import json
import random
from math import comb
from pathlib import Path
import os
R = Path(os.environ.get("OPFR_BENCH_ROOT", ".")) # root of a local operate-fr-bench checkout with reports/
OUT = Path(os.environ.get("STUDY_DATA_OUT", Path(__file__).resolve().parent.parent / "data"))
OUT.mkdir(exist_ok=True)
CELLS = {
# (size, regime) -> {suite: results file}
("12B", "PTQ"): {
"smoke": "gemma4_12b_route_transformer_plus_validator_v3_1_rerun_2026-08-10.jsonl",
"core500": "gemma4_12b_core500_candidate_rerun_2026-08-10.jsonl",
},
("12B", "QAT"): {
"smoke": "gemma4_12b_qat_route_transformer_plus_validator_v3_1.jsonl",
"core500": "gemma4_12b_qat_core500_candidate.jsonl",
},
("26B", "PTQ"): {
"smoke": "gemma4_26b_route_transformer_plus_validator_v3_1_rerun_2026-08-10.jsonl",
"core500": "gemma4_26b_core500_candidate_rerun_2026-08-10.jsonl",
},
("26B", "QAT"): {
"smoke": "gemma4_26b_a4b_qat_route_transformer_plus_validator_v3_1.jsonl",
"core500": "gemma4_26b_a4b_qat_core500_candidate.jsonl",
},
}
LABELS = {
"smoke": "data/labels/smoke100_route_labels.jsonl",
"core500": "data/labels/core500_route_labels.jsonl",
}
DATASETS = { # authoritative per-task family + stem ids
"smoke": "data/smoke100.jsonl",
"core500": "data/core500.jsonl",
}
REPEATS = { # same-day second runs for ordinary-nondeterminism baseline
("12B", "PTQ"): "gemma4_12b_route_transformer_plus_validator_v3_1_repeat2.jsonl",
("12B", "QAT"): "gemma4_12b_qat_route_transformer_plus_validator_v3_1_repeat2.jsonl",
("26B", "QAT"): "gemma4_26b_a4b_qat_route_transformer_plus_validator_v3_1_repeat2.jsonl",
("26B", "PTQ"): "gemma4_26b_route_transformer_plus_validator_v3_1_repeat2.jsonl",
}
def load_labels(suite):
out = {}
for ln in open(R / LABELS[suite], encoding="utf-8"):
d = json.loads(ln)
out[d["task_id"]] = d
return out
def load_dataset_meta(suite):
"""Authoritative task metadata: family + smoke stem id (for clustering)."""
meta = {}
for ln in open(R / DATASETS[suite], encoding="utf-8"):
d = json.loads(ln)
meta[d["id"]] = {
"family": d["family"],
"stem": d.get("source_smoke100_id") or d["id"],
}
return meta
def load_cell(fname, suite):
rows = {}
p = R / "reports" / fname
for ln in open(p, encoding="utf-8"):
d = json.loads(ln)
rows[d["task_id"]] = d
return rows
def correct(row, lab):
return row["classified_route"] in lab["allowed_routes"]
def mcnemar_exact(n01, n10):
n = n01 + n10
if n == 0:
return 1.0
k = min(n01, n10)
p = sum(comb(n, i) for i in range(0, k + 1)) * 2 / 2 ** n
return min(1.0, p)
def main():
labels = {s: load_labels(s) for s in ("smoke", "core500")}
metas = {s: load_dataset_meta(s) for s in ("smoke", "core500")}
cells = {}
for key, files in CELLS.items():
cells[key] = {s: load_cell(f, s) for s, f in files.items()}
summary = {"cells": {}, "paired": {}, "interaction": {}, "divergence": {},
"repeats": {}}
# ── per-cell metrics ──
cell_csv = [["size", "regime", "suite", "n", "errored",
"route_correct", "rate", "preferred_match",
"mean_latency_ms", "mean_resp_chars"]]
fam_csv = [["size", "regime", "suite", "family", "n", "correct", "rate"]]
for (size, regime), suites in cells.items():
for suite, rows in suites.items():
lab = labels[suite]
ids = sorted(lab)
nerr = sum(1 for t in ids if rows[t].get("error"))
ncor = sum(1 for t in ids if correct(rows[t], lab[t]))
npref = sum(1 for t in ids
if rows[t]["classified_route"] == lab[t].get("preferred_route"))
lat = [rows[t].get("latency_ms") or 0 for t in ids]
rl = [len(rows[t].get("response_text") or "") for t in ids]
cell_csv.append([size, regime, suite, len(ids), nerr, ncor,
round(ncor / len(ids), 4), round(npref / len(ids), 4),
round(sum(lat) / len(lat), 1), round(sum(rl) / len(rl), 1)])
summary["cells"][f"{size}_{regime}_{suite}"] = {
"n": len(ids), "errored": nerr,
"route_correctness": round(ncor / len(ids), 4),
"preferred_match": round(npref / len(ids), 4),
"mean_latency_ms": round(sum(lat) / len(lat), 1),
}
meta = metas[suite]
fams = {}
for t in ids:
fams.setdefault(meta[t]["family"], []).append(t)
for f, tt in sorted(fams.items()):
c = sum(1 for t in tt if correct(rows[t], lab[t]))
fam_csv.append([size, regime, suite, f, len(tt), c,
round(c / len(tt), 4)])
# ── paired quant effect within size (core500 primary; smoke secondary) ──
paired_csv = [["suite", "size", "n_pairs", "qat_only_correct",
"ptq_only_correct", "delta_rate", "mcnemar_p"]]
deltas = {}
for suite in ("core500", "smoke"):
lab = labels[suite]
ids = sorted(lab)
for size in ("12B", "26B"):
q, p_ = cells[(size, "QAT")][suite], cells[(size, "PTQ")][suite]
n10 = sum(1 for t in ids if correct(q[t], lab[t]) and not correct(p_[t], lab[t]))
n01 = sum(1 for t in ids if correct(p_[t], lab[t]) and not correct(q[t], lab[t]))
delta = (n10 - n01) / len(ids)
pv = mcnemar_exact(n01, n10)
paired_csv.append([suite, size, len(ids), n10, n01,
round(delta, 4), round(pv, 4)])
summary["paired"][f"{suite}_{size}"] = {
"qat_only": n10, "ptq_only": n01,
"delta_rate": round(delta, 4), "mcnemar_p": round(pv, 4)}
if suite == "core500":
deltas[size] = [
(1 if correct(q[t], lab[t]) else 0) -
(1 if correct(p_[t], lab[t]) else 0) for t in ids]
# ── interaction (delta-of-deltas, task bootstrap, core500) ──
rng = random.Random(20260810)
obs = sum(deltas["26B"]) / len(deltas["26B"]) - sum(deltas["12B"]) / len(deltas["12B"])
n = len(deltas["12B"])
ids_c = sorted(labels["core500"])
boots = []
for _ in range(10000):
idx = [rng.randrange(n) for _ in range(n)]
boots.append((sum(deltas["26B"][i] for i in idx)
- sum(deltas["12B"][i] for i in idx)) / n)
boots.sort()
# stem-clustered bootstrap: Core-500 is 100 smoke stems x 5 paraphrase
# variants, so tasks are NOT independent. Resample the 100 stems.
meta_c = metas["core500"]
stems = {}
for i, t_ in enumerate(ids_c):
stems.setdefault(meta_c[t_]["stem"], []).append(i)
stem_keys = sorted(stems)
rng2 = random.Random(20260810)
cboots = []
for _ in range(10000):
picks = [stem_keys[rng2.randrange(len(stem_keys))] for _ in stem_keys]
tot26 = tot12 = cnt = 0
for s in picks:
for i in stems[s]:
tot26 += deltas["26B"][i]
tot12 += deltas["12B"][i]
cnt += 1
cboots.append((tot26 - tot12) / cnt)
cboots.sort()
summary["interaction"]["core500"] = {
"delta_of_deltas": round(obs, 4),
"ci95_task_iid": [round(boots[249], 4), round(boots[9749], 4)],
"ci95_stem_clustered": [round(cboots[249], 4), round(cboots[9749], 4)],
"n_stems": len(stem_keys),
"note": ("positive = QAT helps 26B more than 12B; stem-clustered CI "
"is primary (5x paraphrase dependence)"),
}
# ── exact-output divergence between regimes (same size, same task) ──
div_csv = [["suite", "size", "n", "exact_match", "rate",
"median_first_divergence_char"]]
for suite in ("core500", "smoke"):
lab = labels[suite]
ids = sorted(lab)
for size in ("12B", "26B"):
q, p_ = cells[(size, "QAT")][suite], cells[(size, "PTQ")][suite]
same = 0
first = []
for t in ids:
a = q[t].get("response_text") or ""
b = p_[t].get("response_text") or ""
if a == b:
same += 1
else:
k = next((i for i, (x, y) in enumerate(zip(a, b)) if x != y),
min(len(a), len(b)))
first.append(k)
first.sort()
med = first[len(first) // 2] if first else None
div_csv.append([suite, size, len(ids), same,
round(same / len(ids), 4), med])
summary["divergence"][f"{suite}_{size}"] = {
"exact_match": same, "rate": round(same / len(ids), 4),
"median_first_divergence_char": med}
# ── ordinary-nondeterminism baseline (same profile, same day, rerun) ──
for (size, regime), fname in REPEATS.items():
p = R / "reports" / fname
if not p.exists():
continue
rep = load_cell(fname, "smoke")
base = cells[(size, regime)]["smoke"]
lab = labels["smoke"]
same = sum(1 for t in sorted(lab)
if (rep[t].get("response_text") or "") ==
(base[t].get("response_text") or ""))
summary["repeats"][f"{size}_{regime}_smoke"] = {
"exact_match": same, "n": len(lab)}
for name, rows in [("cell_metrics.csv", cell_csv),
("family_rates.csv", fam_csv),
("paired_quant_effect.csv", paired_csv),
("regime_output_divergence.csv", div_csv)]:
with open(OUT / name, "w", newline="", encoding="utf-8") as fh:
csv.writer(fh).writerows(rows)
(OUT / "analysis_summary.json").write_text(
json.dumps(summary, indent=2, ensure_ascii=False), encoding="utf-8")
print(json.dumps(summary, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
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