#!/usr/bin/env python3 """Render the cross-domain manuscript assets from audited saved-response metrics.""" import argparse from decimal import Decimal, ROUND_HALF_UP import json from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from analyze_crossdomain_paper import MODELS, STATES NAMES = {"qwen38-flash-next": "Qwen 3.8 Flash Next", "gemma4-31b-it": "Gemma 4 31B", "qwen": "Qwen 3.8 27B", "gemma4-26b-a4b-it": "Gemma 4 26B A4B", "glm": "GLM 5.3 Flash", "molmo2-8b": "Molmo2-8B"} def percentage(n, d, places=2): return str((Decimal(int(n)) * 100 / Decimal(int(d))).quantize(Decimal(10) ** -places, rounding=ROUND_HALF_UP)) def pct(value, places=2): # Metrics are sample means; remove insignificant summation noise before formatting. return str(Decimal(str(round(value * 100, 9))).quantize(Decimal(10) ** -places, rounding=ROUND_HALF_UP)) def table(path, header, rows, columns): path.write_text("\n".join([r"\begin{tabular}{@{}" + columns + r"@{}}", r"\toprule", header + r" \\", r"\midrule", *rows, r"\bottomrule", r"\end{tabular}"]) + "\n") def model_table(data, path): """Report complete task success before its decision diagnostics.""" rows = [] for model in MODELS: cells = [] for source in STATES: domain = data["domains"][source] counts = domain["models"][model]["counts"] cells.extend([ percentage(counts["joint_groups_correct"], 1000, 1), percentage(counts["groups_correct"], 1000, 1), percentage(counts["failures"], domain["views"]), ]) rows.append(NAMES[model] + " & " + " & ".join(cells) + r" \\") rows.extend([ r"\midrule", r"Always answerable & 0.0 & 0.0 & 40.00 & 0.0 & 0.0 & 25.00 & 0.0 & 0.0 & 33.33 \\", r"Always unanswerable & 0.0 & 0.0 & 60.00 & 0.0 & 0.0 & 75.00 & 0.0 & 0.0 & 66.67 \\", ]) header = ( r"& \multicolumn{3}{c}{PlotQA} & \multicolumn{3}{c}{CLEVR} & \multicolumn{3}{c}{GQA} \\ " r"\cmidrule(lr){2-4}\cmidrule(lr){5-7}\cmidrule(l){8-10} " r"System & $J_5\uparrow$ & $B_5\uparrow$ & $E\downarrow$ " r"& $J_4\uparrow$ & $B_4\uparrow$ & $E\downarrow$ " r"& $J_3\uparrow$ & $B_3\uparrow$ & $E\downarrow$" ) table(path, header, rows, "lrrrrrrrrr") def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--analysis", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) args = parser.parse_args() data = json.loads(args.analysis.read_text()) out = args.output out.mkdir(parents=True, exist_ok=True) domains = data["domains"] model_table(data, out / "model_table.tex") rows = [] for model in MODELS: cells = [] for source in STATES: m = domains[source]["models"][model]["metrics"] cells.extend([pct(m["balanced_failure"]), pct(m["common_state_failure"])]) rows.append(NAMES[model] + " & " + " & ".join(cells) + r" \\") rows += [r"\midrule", r"Always answerable & 50.00 & 33.33 & 50.00 & 33.33 & 50.00 & 33.33 \\"] table(out / "balanced_table.tex", r"System & PlotQA Bal. & $E_{FSM}$ & CLEVR Bal. & $E_{FSM}$ & GQA Bal. & $E_{FSM}$", rows, "lrrrrrr") rows = [] for model in MODELS: cells = [] for source in STATES: c = domains[source]["models"][model]["counts"] cells.extend([percentage(c["strict_answers_correct"], c["answerable_views"]), percentage(c["joint_groups_correct"], 1000, 1)]) rows.append(NAMES[model] + " & " + " & ".join(cells) + r" \\") table(out / "answer_table.tex", r"System & PlotQA $A$ & $J_5$ & CLEVR $A$ & $J_4$ & GQA $A$ & $J_3$", rows, "lrrrrrr") rows = [] for model in MODELS: cells = [] for stratum in ["location", "other"]: c = domains["gqa"]["location_sensitivity"]["models"][model][stratum] cells.extend([percentage(c["failures"], c["views"]), percentage(c["groups_correct"], c["groups"], 1), percentage(c["missing_failures"], c["groups"])]) rows.append(NAMES[model] + " & " + " & ".join(cells) + r" \\") table(out / "location_table.tex", r"System & Loc. Fail & $B_3$ & $M$ Fail & Other Fail & $B_3$ & $M$ Fail", rows, "lrrrrrr") rows = [] for source in STATES: for model in MODELS: d = domains[source]["models"][model] c = d["counts"] lo, hi = d["intervals_95"]["failure"] rows.append(source.upper() + " / " + NAMES[model] + " & " + str(c["valid_decision_errors"]) + "/" + str(c["valid"]) + " & " + str(c["invalid"]) + " & " + pct(lo) + "--" + pct(hi) + r" \\") table(out / "denominator_table.tex", r"Domain / system & $W/V$ & Invalid & Failure 95\% interval", rows, "lrrr") plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 12, "pdf.fonttype": 42}) fig, axes = plt.subplots(1, 3, figsize=(8.3, 3.7), gridspec_kw={"width_ratios": [5, 4, 3]}) for ax, (source, states) in zip(axes, STATES.items()): values = np.array([[domains[source]["models"][m]["states"][s]["failures"] / 10 for s in states] for m in MODELS]) ax.imshow(values, cmap="YlOrRd", vmin=0, vmax=80, aspect="auto") for i in range(len(MODELS)): for j in range(len(states)): ax.text(j, i, f"{values[i, j]:.1f}", ha="center", va="center", color="white" if values[i, j] > 48 else "#222222", fontsize=11) ax.set_title({"plotqa": "A PlotQA", "clevr": "B CLEVR", "gqa": "C GQA"}[source], loc="left", fontsize=12) ax.set_xticks(range(len(states)), [{"FULL": "F", "A_SAME": "S", "A_CHANGED": "C", "U_MISSING": "M", "U_INVALID": "I"}[s] for s in states]) ax.set_yticks(range(6), [NAMES[m] for m in MODELS] if source == "plotqa" else []) ax.tick_params(length=0) fig.tight_layout(w_pad=.6, rect=(0, .06, 1, 1)) fig.text(.60, .025, "Failure (%); 1,000 views per cell", ha="center", fontsize=12) for ext in ["pdf", "png"]: fig.savefig(out / ("state_failures." + ext), dpi=220, bbox_inches="tight") plt.close(fig) summary = {"status": "passed", "model_domain_cells": 18, "rounded_percentages": "decimal half-up from integer counts; percentages are not truncated", "tables": ["model_table.tex", "balanced_table.tex", "answer_table.tex", "denominator_table.tex", "location_table.tex"], "figure": "state_failures.pdf", "sha_validation_performed": False} (out / "asset-validation.json").write_text(json.dumps(summary, indent=2) + "\n") print(json.dumps(summary)) if __name__ == "__main__": main()