#!/usr/bin/env python3 """Replay the 72,000 saved decisions and build descriptive, paired diagnostics. Inputs are explicit field projections of final responses, not new model calls. Existing label and group identifiers are treated as opaque keys. No hashes run. """ from __future__ import annotations import argparse from collections import Counter, defaultdict import gzip import json from pathlib import Path import numpy as np from analyze_crossdomain_visual import strict_answer_match from analyze_visual_judges import number, valid_parsed MODELS = ["qwen38-flash-next", "gemma4-31b-it", "qwen", "gemma4-26b-a4b-it", "glm", "molmo2-8b"] CROSS_KEYS = {m: {"qwen": "qwen3.8-27b-fp8", "glm": "GLM-5.3-Flash"}.get(m, m) for m in MODELS} STATES = {"plotqa": ["FULL", "A_SAME", "A_CHANGED", "U_MISSING", "U_INVALID"], "clevr": ["FULL", "A_SAME", "A_CHANGED", "U_MISSING"], "gqa": ["FULL", "A_SAME", "U_MISSING"]} COMMON = ["FULL", "A_SAME", "U_MISSING"] METRICS = ["failure", "group_success", "joint_success", "answer_accuracy_strict", "balanced_failure", "common_state_failure", "common_state_group_success", "missing_failure", "answerable_failure", "unanswerable_failure"] def read(path): return json.loads(Path(path).read_text()) def rows(path): with gzip.open(path, "rt") as handle: return [json.loads(line) for line in handle if line.strip()] def interval(samples): return np.quantile(samples, [.025, .975], axis=0).tolist() def analyze(inputs: Path, expected: Path, draws: int = 10000): if draws < 10000: raise ValueError("The manuscript analysis requires at least 10,000 bootstrap draws") plot_labels = read(inputs / "plotqa/labels.json") cross_labels = read(inputs / "crossdomain/labels.json") manifest = [json.loads(line) for line in (inputs / "crossdomain/manifest.jsonl").read_text().splitlines()] blind = {r["item_id"]: r for r in manifest} if len(blind) != len(manifest) or len(blind) != 7000: raise ValueError("Cross-domain manifest must contain 7,000 unique IDs") plot_manifest = [json.loads(line) for line in (inputs / "plotqa/manifest.jsonl").read_text().splitlines()] plot_blind = {r["item_id"]: r for r in plot_manifest} if len(plot_blind) != len(plot_manifest) or len(plot_blind) != 5000: raise ValueError("PlotQA manifest must contain 5,000 unique IDs") group_meta = read(inputs / "crossdomain/group-metadata.json") meta = {r["group_id"]: r for r in group_meta} if len(meta) != len(group_meta) or len(meta) != 2000: raise ValueError("Expected 2,000 distinct frozen group metadata records") label_sets = {"plotqa": plot_labels, **{s: [r for r in cross_labels if r["source"] == s] for s in ["clevr", "gqa"]}} old_plot = read(expected / "plotqa-analysis.json")["models"] old_joint = read(expected / "plotqa-joint.json")["models"] old_cross = read(expected / "crossdomain-analysis.json")["models"] outputs = {} output_rows = 0 for domain in ["plotqa", "crossdomain"]: expected_ids = {r["item_id"] for r in (plot_labels if domain == "plotqa" else cross_labels)} questions = plot_blind if domain == "plotqa" else blind if expected_ids != set(questions): raise ValueError("Label IDs and frozen question IDs differ") outputs[domain] = {} for model in MODELS: key = model if domain == "plotqa" else CROSS_KEYS[model] loaded = rows(inputs / domain / (key + ".jsonl.gz")) records = {r["item_id"]: r for r in loaded} if len(records) != len(loaded) or set(records) != expected_ids: raise ValueError(f"{domain}/{model}: duplicate, missing or unexpected IDs") for r in loaded: if r["status"] != "completed" and not ( r["status"] == "invalid_schema" and r.get("terminal_invalid") is True): raise ValueError(f"{domain}/{model}: unfinished response") if r["question"] != questions[r["item_id"]]["question"]: raise ValueError("Question differs from frozen blind input") output_rows += len(loaded) outputs[domain][model] = records result = {"schema_version": 1, "models": MODELS, "domains": {}, "provenance": {}, "protocol": {"draws": draws, "seed": 20260915, "unit": "source-question group", "interval": "descriptive percentile bootstrap; paired within domain; no multiplicity adjustment", "common_states": COMMON, "inference_calls": 0, "cross_domain_interpretation": "different questions, states and evidence guarantees; no causal domain effect", "balanced_failure": "one half answerable failure plus one half unanswerable failure; invalids fail in their truth class", "new_metrics": "post-hoc descriptive additions; not preregistered", "source_image_clustering": "one question per source image is the release builder contract; portable labels cannot re-audit original image identities for CLEVR/GQA", "sha_validation_performed": False}} total_invalid = 0 for source, labels in label_sets.items(): states = STATES[source] gold = {r["item_id"]: r for r in labels} if len(gold) != len(labels) or len(labels) != 1000 * len(states): raise ValueError(f"{source}: invalid number of unique labels") grouped = defaultdict(dict) for r in labels: if r["state"] not in states or type(r["answerable"]) is not bool: raise ValueError("Malformed state or target") if r["answerable"] != (not r["state"].startswith("U_")): raise ValueError("Inconsistent answerability label") if r["state"] in grouped[r["group_id"]]: raise ValueError("Repeated group state") if source != "plotqa": g = meta[r["group_id"]] if g["source"] != source or g["question"] != blind[r["item_id"]]["question"]: raise ValueError("Release group/question join failed") grouped[r["group_id"]][r["state"]] = r if len(grouped) != 1000 or any(set(g) != set(states) for g in grouped.values()): raise ValueError(f"{source}: incomplete group coverage") group_ids = sorted(grouped) ordered = [[grouped[g][s] for s in states] for g in group_ids] positive = np.array([not s.startswith("U_") for s in states]) common = [states.index(s) for s in COMMON] vectors = [] domain = {"groups": 1000, "views": len(labels), "states": states, "models": {}} if source == "gqa": location = np.array([grouped[g]["FULL"]["target"] in {"left", "right", "top", "bottom"} for g in group_ids]) domain["location_sensitivity"] = { "selection": "FULL canonical target in {left, right, top, bottom}; all sibling states retained", "location_groups": int(location.sum()), "other_groups": int((~location).sum()), "location_group_ids": [g for g, selected in zip(group_ids, location) if selected], "interpretation": "post-hoc vulnerability stratum, not adjudicated label errors; retained complement is not certified clean", "models": {}, "intervals": "point estimates only"} if source != "plotqa": mm = [meta[g] for g in group_ids] domain["program_steps"] = dict(sorted(Counter(g["dependency_depth"] for g in mm).items())) domain["program_steps_note"] = "Stored dependency_depth is len(program steps), not tree depth" domain["answer_types"] = dict(Counter(g["answer_type"] for g in mm)) for model in MODELS: records = outputs["plotqa" if source == "plotqa" else "crossdomain"][model] invalid = np.zeros((1000, len(states)), dtype=bool) wrong = invalid.copy() correct_answer = invalid.copy() for i, gg in enumerate(ordered): for j, r in enumerate(gg): prediction = records[r["item_id"]] if source == "plotqa" and prediction["question"] != records[gg[0]["item_id"]]["question"]: raise ValueError("PlotQA sibling questions differ") parsed = valid_parsed(prediction) invalid[i, j] = parsed is None wrong[i, j] = parsed is not None and parsed["answerable"] != r["answerable"] if parsed and parsed["answerable"] and r["answerable"]: if source == "plotqa": value, target = number(parsed["answer"]), number(r["target"]) correct_answer[i, j] = value is not None and target is not None and value == target else: correct_answer[i, j] = strict_answer_match(parsed["answer"], r["target"]) fail = invalid | wrong success = ~fail.any(axis=1) joint = success & correct_answer[:, positive].all(axis=1) if joint.sum() > success.sum(): raise ValueError("Joint answer success cannot exceed decision success") pos = fail[:, positive].mean(axis=1) neg = fail[:, ~positive].mean(axis=1) matrix = np.column_stack([fail.mean(axis=1), success, joint, correct_answer[:, positive].mean(axis=1), (pos + neg) / 2, fail[:, common].mean(axis=1), ~fail[:, common].any(axis=1), fail[:, states.index("U_MISSING")], pos, neg]) vectors.append(matrix) counts = {"failures": int(fail.sum()), "invalid": int(invalid.sum()), "valid_decision_errors": int(wrong.sum()), "valid": int((~invalid).sum()), "groups_correct": int(success.sum()), "joint_groups_correct": int(joint.sum()), "strict_answers_correct": int(correct_answer[:, positive].sum()), "answerable_views": int(1000 * positive.sum()), "unanswerable_views": int(1000 * (~positive).sum())} state_counts = {s: {"failures": int(fail[:, j].sum()), "invalid": int(invalid[:, j].sum()), "valid_decision_errors": int(wrong[:, j].sum()), "views": 1000} for j, s in enumerate(states)} if source == "plotqa": old = old_plot[model] checks = [(counts["failures"], old["error_fraction_if_invalid_count_wrong"]["numerator"]), (counts["invalid"], old["invalid_or_missing"]), (counts["valid_decision_errors"], old["answerability_errors"]["numerator"]), (counts["groups_correct"], old["five_view_groups_all_correct"]["numerator"]), (counts["joint_groups_correct"], old_joint[model]["strict_joint_all_five_groups"]["numerator"])] for s in states: checks.append((state_counts[s]["failures"], old["state_counts"][s]["failures_including_invalid"]["numerator"])) else: old = old_cross[CROSS_KEYS[model]]["by_source"][source] checks = [(counts["failures"], old["failures_full_denominator"]["numerator"]), (counts["invalid"], old["invalid_outputs"]), (counts["valid_decision_errors"], old["answerability_errors"]["numerator"]), (counts["groups_correct"], old["groups_all_states_correct"]["numerator"]), (counts["strict_answers_correct"], old["answer_correct_on_all_answerable_strict_lower_bound"]["numerator"])] for s in states: checks.append((state_counts[s]["failures"], old["states"][s]["failures_full_denominator"]["numerator"])) if any(a != b for a, b in checks): raise ValueError(f"Saved aggregate mismatch: {source}/{model}: {checks}") domain["models"][model] = {"counts": counts, "states": state_counts, "metrics": dict(zip(METRICS, matrix.mean(axis=0).tolist())), "saved_aggregate_checks": len(checks), "invalid_item_ids": [r["item_id"] for i, gg in enumerate(ordered) for j, r in enumerate(gg) if invalid[i, j]]} if source == "gqa": domain["location_sensitivity"]["models"][model] = {} for stratum, mask in [("location", location), ("other", ~location)]: domain["location_sensitivity"]["models"][model][stratum] = { "groups": int(mask.sum()), "views": int(mask.sum() * len(states)), "failures": int(fail[mask].sum()), "groups_correct": int(success[mask].sum()), "missing_failures": int(fail[mask, states.index("U_MISSING")].sum()), "invalid": int(invalid[mask].sum())} total_invalid += counts["invalid"] if model == "qwen" and source != "plotqa": backend = {"subsets": {}, "group_composition": dict(Counter( "mixed" if len({records[r["item_id"]]["saved_backend"] for r in gg}) > 1 else records[gg[0]["item_id"]]["saved_backend"] for gg in ordered))} for name in ["bridge", "simflow"]: mask = np.array([[records[r["item_id"]]["saved_backend"] == name for r in gg] for gg in ordered]) backend["subsets"][name] = {"views": int(mask.sum()), "failures": int(fail[mask].sum()), "invalid": int(invalid[mask].sum()), "state_counts": {s: int(mask[:, j].sum()) for j, s in enumerate(states)}} backend["interpretation"] = "Availability-selected disjoint subsets; not a matched backend comparison" result["provenance"][source] = backend values = np.stack(vectors, axis=1) rng = np.random.default_rng(20260915) boot = np.empty((draws, len(MODELS), len(METRICS))) for start in range(0, draws, 100): end = min(start + 100, draws) selected = rng.integers(0, 1000, size=(end - start, 1000)) boot[start:end] = values[selected].mean(axis=1) for i, model in enumerate(MODELS): domain["models"][model]["intervals_95"] = {m: interval(boot[:, i, k]) for k, m in enumerate(METRICS)} contrasts = {} for i, left in enumerate(MODELS): for j in range(i + 1, len(MODELS)): right = MODELS[j] contrasts[left + "__" + right] = {"left": left, "right": right, **{metric: {"left_minus_right_pp": float((values[:, i, k] - values[:, j, k]).mean() * 100), "interval_95_pp": interval((boot[:, i, k] - boot[:, j, k]) * 100)} for k, metric in enumerate(METRICS)}} domain["paired_contrasts"] = contrasts domain["analytic_baselines"] = {"always_answerable_failure": float((~positive).mean()), "always_unanswerable_failure": float(positive.mean()), "constant_decision_group_success": 0, "constant_decision_balanced_failure": .5} result["domains"][source] = domain result["validation"] = {"status": "passed", "selected_model_responses": output_rows, "unique_input_views": 12000, "source_question_groups": 3000, "model_domain_cells": 18, "terminal_invalid_responses": total_invalid, "valid_responses": output_rows - total_invalid, "missing_or_duplicate_rows": 0, "original_aggregate_counts_reproduced": True, "all_model_frozen_question_joins": True, "output_projection_scope": "parsed response fields, not independent raw-output/token validation", "sha_validation_performed": False} return result def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--inputs", type=Path, required=True) parser.add_argument("--expected", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) args = parser.parse_args() result = analyze(args.inputs, args.expected) args.output.parent.mkdir(parents=True, exist_ok=True) args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2, allow_nan=False) + "\n") print(json.dumps(result["validation"])) if __name__ == "__main__": main()