#!/usr/bin/env python3 """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()