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-rw-r--r--experiments/plot_digital_additive_transfer.py325
-rw-r--r--results/digital_additive_transfer.csv19
-rw-r--r--results/digital_additive_transfer.json194
-rw-r--r--results/figs/figure_digital_additive_transfer.pdfbin0 -> 17919 bytes
-rw-r--r--results/figs/figure_digital_additive_transfer.pngbin0 -> 157768 bytes
-rw-r--r--results/figs/figure_digital_additive_transfer.svg964
-rw-r--r--visual-composer/digital-additive-transfer.md33
-rw-r--r--visual-composer/qa-ledger.md14
8 files changed, 1549 insertions, 0 deletions
diff --git a/experiments/plot_digital_additive_transfer.py b/experiments/plot_digital_additive_transfer.py
new file mode 100644
index 0000000..5e12a11
--- /dev/null
+++ b/experiments/plot_digital_additive_transfer.py
@@ -0,0 +1,325 @@
+#!/usr/bin/env python3
+"""Plot SDIL as an additive correction across four digital learners."""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+from pathlib import Path
+
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+COLORS = {
+ "clean": "#222222",
+ "noise": "#CC79A7",
+ "raw": "#D55E00",
+ "constant": "#777777",
+ "sdil": "#0072B2",
+ "oracle": "#009E73",
+}
+
+
+def parse_args() -> argparse.Namespace:
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--dualprop", type=Path,
+ default=Path("results/contrastive_bias/c1_gate.json"))
+ parser.add_argument(
+ "--ep", type=Path,
+ default=Path("results/ep_bias/c1_gate.json"))
+ parser.add_argument(
+ "--clln", type=Path,
+ default=Path("results/coupled_ladder/p2_confirm_side4.json"))
+ parser.add_argument(
+ "--overclamp", type=Path,
+ default=Path("results/coupled_ladder/p3_overclamp_side4.json"))
+ parser.add_argument(
+ "--output-analysis", type=Path,
+ default=Path("results/digital_additive_transfer.json"))
+ parser.add_argument(
+ "--output-csv", type=Path,
+ default=Path("results/digital_additive_transfer.csv"))
+ parser.add_argument(
+ "--output-figure", type=Path,
+ default=Path("results/figs/figure_digital_additive_transfer"))
+ parser.add_argument("--bootstrap-replicates", type=int, default=20000)
+ parser.add_argument("--bootstrap-seed", type=int, default=20260829)
+ return parser.parse_args()
+
+
+def summarize_values(
+ values: np.ndarray,
+ rng: np.random.Generator,
+ replicates: int,
+) -> dict:
+ values = np.asarray(values, dtype=float)
+ samples = rng.integers(0, len(values), size=(replicates, len(values)))
+ means = np.mean(values[samples], axis=1)
+ return {
+ "mean_accuracy_percent": float(np.mean(values)),
+ "bootstrap_95ci_percent": [
+ float(value) for value in np.percentile(means, (2.5, 97.5))
+ ],
+ "independent_units": len(values),
+ }
+
+
+def task_cluster_accuracies(report: dict, method: str) -> np.ndarray:
+ tasks = sorted({record["task_index"] for record in report["records"]})
+ return np.asarray([
+ 100.0 * np.mean([
+ 1.0 - record["methods"][method]["classification_error"]
+ for record in report["records"]
+ if record["task_index"] == task
+ ])
+ for task in tasks
+ ])
+
+
+def gap_recovered(methods: dict) -> float:
+ clean = methods["clean"]["mean_accuracy_percent"]
+ raw = methods["raw"]["mean_accuracy_percent"]
+ sdil = methods["sdil"]["mean_accuracy_percent"]
+ return float(100.0 * (sdil - raw) / (clean - raw))
+
+
+def build_analysis(
+ dualprop: dict,
+ ep: dict,
+ clln: dict,
+ overclamp: dict,
+ *,
+ replicates: int,
+ seed: int,
+) -> dict:
+ rng = np.random.default_rng(seed)
+ dp_rows = dualprop["rows"]
+ ep_rows = ep["rows"]
+ panels = {
+ "dualprop": {
+ "title": "Dual Propagation · CIFAR-10",
+ "unit": "seed",
+ "methods": {
+ "clean": summarize_values(np.asarray([
+ row["same_path_clean"] for row in dp_rows]), rng, replicates),
+ "raw": summarize_values(np.asarray([
+ row["raw"] for row in dp_rows]), rng, replicates),
+ "sdil": summarize_values(np.asarray([
+ row["innovation"] for row in dp_rows]), rng, replicates),
+ "oracle": summarize_values(np.asarray([
+ row["oracle"] for row in dp_rows]), rng, replicates),
+ },
+ },
+ "ep": {
+ "title": "Equilibrium Propagation · FashionMNIST",
+ "unit": "seed",
+ "methods": {
+ "clean": summarize_values(100.0 * np.asarray([
+ row["clean"] for row in ep_rows]), rng, replicates),
+ "noise": summarize_values(100.0 * np.asarray([
+ row["same_rms_noise"] for row in ep_rows]), rng, replicates),
+ "raw": summarize_values(100.0 * np.asarray([
+ row["raw"] for row in ep_rows]), rng, replicates),
+ "constant": summarize_values(100.0 * np.asarray([
+ row["constant"] for row in ep_rows]), rng, replicates),
+ "sdil": summarize_values(100.0 * np.asarray([
+ row["innovation"] for row in ep_rows]), rng, replicates),
+ "oracle": summarize_values(100.0 * np.asarray([
+ row["oracle"] for row in ep_rows]), rng, replicates),
+ },
+ },
+ "clln": {
+ "title": "Coupled learning · 32 edges",
+ "unit": "task; device draws averaged within task",
+ "methods": {
+ method: summarize_values(
+ task_cluster_accuracies(clln, source), rng, replicates)
+ for method, source in (
+ ("clean", "clean"),
+ ("noise", "matched_noise"),
+ ("raw", "raw"),
+ ("constant", "constant"),
+ ("sdil", "sdil"),
+ )
+ },
+ },
+ "overclamp": {
+ "title": "Overclamped coupled learning · 32 edges",
+ "unit": "task; device draws averaged within task",
+ "methods": {
+ method: summarize_values(
+ task_cluster_accuracies(overclamp, source), rng, replicates)
+ for method, source in (
+ ("clean", "overclamp_clean"),
+ ("raw", "overclamp"),
+ ("sdil", "overclamp_sdil"),
+ )
+ },
+ },
+ }
+ for panel in panels.values():
+ panel["raw_to_clean_gap_recovered_percent"] = gap_recovered(
+ panel["methods"])
+ return {
+ "analysis": "digital_additive_sdil_transfer",
+ "confirmatory_sources": {
+ "dualprop": dualprop["gate"] == "pass",
+ "ep": ep["gate"] == "pass",
+ "clln": bool(clln["confirmatory"]),
+ "overclamp": bool(overclamp["confirmatory"]),
+ },
+ "bootstrap": {
+ "replicates": replicates,
+ "seed": seed,
+ "interval": "percentile 95%",
+ },
+ "panels": panels,
+ }
+
+
+def write_csv(path: Path, analysis: dict) -> None:
+ path.parent.mkdir(parents=True, exist_ok=True)
+ with path.open("w", newline="") as stream:
+ writer = csv.writer(stream)
+ writer.writerow((
+ "panel", "method", "mean_accuracy_percent", "ci_low", "ci_high",
+ "independent_unit", "independent_units", "gap_recovered_percent",
+ ))
+ for panel_name, panel in analysis["panels"].items():
+ for method, summary in panel["methods"].items():
+ writer.writerow((
+ panel_name,
+ method,
+ summary["mean_accuracy_percent"],
+ *summary["bootstrap_95ci_percent"],
+ panel["unit"],
+ summary["independent_units"],
+ panel["raw_to_clean_gap_recovered_percent"],
+ ))
+
+
+def plot(path: Path, analysis: dict) -> None:
+ mpl.rcParams.update({
+ "font.family": "DejaVu Sans",
+ "font.size": 8.5,
+ "axes.labelsize": 9,
+ "axes.titlesize": 9.5,
+ "xtick.labelsize": 7.8,
+ "ytick.labelsize": 8,
+ "axes.spines.top": False,
+ "axes.spines.right": False,
+ "svg.fonttype": "none",
+ "pdf.fonttype": 42,
+ "figure.facecolor": "white",
+ "axes.facecolor": "white",
+ })
+ order = ("dualprop", "ep", "clln", "overclamp")
+ figure, axes = plt.subplots(2, 2, figsize=(8.0, 5.8), sharey=True)
+ for index, (axis, panel_name) in enumerate(zip(axes.ravel(), order)):
+ panel = analysis["panels"][panel_name]
+ methods = list(panel["methods"])
+ summaries = [panel["methods"][method] for method in methods]
+ means = np.asarray([
+ summary["mean_accuracy_percent"] for summary in summaries])
+ intervals = np.asarray([
+ summary["bootstrap_95ci_percent"] for summary in summaries])
+ positions = np.arange(len(methods))
+ axis.bar(
+ positions,
+ means,
+ color=[COLORS[method] for method in methods],
+ width=0.72,
+ edgecolor="white",
+ linewidth=0.4,
+ )
+ axis.errorbar(
+ positions,
+ means,
+ yerr=np.vstack((
+ means - intervals[:, 0], intervals[:, 1] - means,
+ )),
+ fmt="none",
+ ecolor="#222222",
+ elinewidth=0.8,
+ capsize=2.2,
+ )
+ labels = {
+ "clean": "Clean", "noise": "Same-RMS\nnoise", "raw": "Raw",
+ "constant": "Static\ncalibration", "sdil": "SDIL",
+ "oracle": "Oracle",
+ }
+ axis.set_xticks(positions, [labels[method] for method in methods])
+ axis.set_ylim(0.0, 106.0)
+ axis.grid(axis="y", color="#D9D9D9", linewidth=0.55, alpha=0.8)
+ axis.tick_params(length=3)
+ letter = chr(ord("a") + index)
+ recovery = panel["raw_to_clean_gap_recovered_percent"]
+ axis.set_title(
+ f"({letter}) {panel['title']}\n{recovery:.0f}% of clean gap recovered")
+ if index % 2 == 0:
+ axis.set_ylabel("Task accuracy (%)")
+ figure.text(
+ 0.995,
+ 0.005,
+ "Dual Prop/EP: 5 seeds; coupled learners: 40 tasks × 3 device draws",
+ ha="right",
+ va="bottom",
+ fontsize=7,
+ color="#666666",
+ )
+ figure.tight_layout(rect=(0.0, 0.035, 1.0, 1.0), h_pad=2.1, w_pad=1.8)
+ path.parent.mkdir(parents=True, exist_ok=True)
+ figure.savefig(path.with_suffix(".svg"), bbox_inches="tight")
+ figure.savefig(path.with_suffix(".pdf"), bbox_inches="tight")
+ figure.savefig(path.with_suffix(".png"), dpi=240, bbox_inches="tight")
+ plt.close(figure)
+
+
+def main() -> None:
+ args = parse_args()
+ reports = {
+ name: json.loads(path.read_text())
+ for name, path in (
+ ("dualprop", args.dualprop),
+ ("ep", args.ep),
+ ("clln", args.clln),
+ ("overclamp", args.overclamp),
+ )
+ }
+ analysis = build_analysis(
+ **reports,
+ replicates=args.bootstrap_replicates,
+ seed=args.bootstrap_seed,
+ )
+ analysis["sources"] = {
+ "dualprop": str(args.dualprop),
+ "ep": str(args.ep),
+ "clln": str(args.clln),
+ "overclamp": str(args.overclamp),
+ }
+ args.output_analysis.parent.mkdir(parents=True, exist_ok=True)
+ args.output_analysis.write_text(json.dumps(analysis, indent=2) + "\n")
+ write_csv(args.output_csv, analysis)
+ plot(args.output_figure, analysis)
+ print(json.dumps({
+ name: {
+ "gap_recovered_percent": panel[
+ "raw_to_clean_gap_recovered_percent"],
+ "methods": {
+ method: summary["mean_accuracy_percent"]
+ for method, summary in panel["methods"].items()
+ },
+ }
+ for name, panel in analysis["panels"].items()
+ }, indent=2))
+ print(f"wrote {args.output_analysis}")
+ print(f"wrote {args.output_csv}")
+ print(f"wrote {args.output_figure}.svg/.pdf/.png")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/results/digital_additive_transfer.csv b/results/digital_additive_transfer.csv
new file mode 100644
index 0000000..92b019c
--- /dev/null
+++ b/results/digital_additive_transfer.csv
@@ -0,0 +1,19 @@
+panel,method,mean_accuracy_percent,ci_low,ci_high,independent_unit,independent_units,gap_recovered_percent
+dualprop,clean,82.85999908447266,82.56399993896484,83.07999725341797,seed,5,100.08168001179783
+dualprop,raw,9.399999618530273,9.399999618530273,9.399999618530273,seed,5,100.08168001179783
+dualprop,sdil,82.92000122070313,82.6280014038086,83.23600158691406,seed,5,100.08168001179783
+dualprop,oracle,82.96000061035156,82.52799682617187,83.34400329589843,seed,5,100.08168001179783
+ep,clean,76.26,73.55,78.53,seed,5,96.12299465240643
+ep,noise,75.44,71.01,78.76,seed,5,96.12299465240643
+ep,raw,31.379999999999995,18.059999999999995,46.08,seed,5,96.12299465240643
+ep,constant,67.9,60.42,74.46674999999942,seed,5,96.12299465240643
+ep,sdil,74.52000000000001,72.51,76.25999999999999,seed,5,96.12299465240643
+ep,oracle,75.55999999999999,70.6,78.66999999999999,seed,5,96.12299465240643
+clln,clean,100.0,100.0,100.0,task; device draws averaged within task,40,100.0
+clln,noise,99.27083333333334,98.4375,99.89583333333334,task; device draws averaged within task,40,100.0
+clln,raw,65.3125,61.14583333333333,69.375,task; device draws averaged within task,40,100.0
+clln,constant,93.85416666666666,90.41666666666667,96.875,task; device draws averaged within task,40,100.0
+clln,sdil,100.0,100.0,100.0,task; device draws averaged within task,40,100.0
+overclamp,clean,91.875,87.8125,95.3125,task; device draws averaged within task,40,100.41666666666663
+overclamp,raw,66.875,61.25,72.70833333333333,task; device draws averaged within task,40,100.41666666666663
+overclamp,sdil,91.97916666666666,88.125,95.3125,task; device draws averaged within task,40,100.41666666666663
diff --git a/results/digital_additive_transfer.json b/results/digital_additive_transfer.json
new file mode 100644
index 0000000..ccb06f8
--- /dev/null
+++ b/results/digital_additive_transfer.json
@@ -0,0 +1,194 @@
+{
+ "analysis": "digital_additive_sdil_transfer",
+ "confirmatory_sources": {
+ "dualprop": true,
+ "ep": false,
+ "clln": true,
+ "overclamp": true
+ },
+ "bootstrap": {
+ "replicates": 20000,
+ "seed": 20260829,
+ "interval": "percentile 95%"
+ },
+ "panels": {
+ "dualprop": {
+ "title": "Dual Propagation \u00b7 CIFAR-10",
+ "unit": "seed",
+ "methods": {
+ "clean": {
+ "mean_accuracy_percent": 82.85999908447266,
+ "bootstrap_95ci_percent": [
+ 82.56399993896484,
+ 83.07999725341797
+ ],
+ "independent_units": 5
+ },
+ "raw": {
+ "mean_accuracy_percent": 9.399999618530273,
+ "bootstrap_95ci_percent": [
+ 9.399999618530273,
+ 9.399999618530273
+ ],
+ "independent_units": 5
+ },
+ "sdil": {
+ "mean_accuracy_percent": 82.92000122070313,
+ "bootstrap_95ci_percent": [
+ 82.6280014038086,
+ 83.23600158691406
+ ],
+ "independent_units": 5
+ },
+ "oracle": {
+ "mean_accuracy_percent": 82.96000061035156,
+ "bootstrap_95ci_percent": [
+ 82.52799682617187,
+ 83.34400329589843
+ ],
+ "independent_units": 5
+ }
+ },
+ "raw_to_clean_gap_recovered_percent": 100.08168001179783
+ },
+ "ep": {
+ "title": "Equilibrium Propagation \u00b7 FashionMNIST",
+ "unit": "seed",
+ "methods": {
+ "clean": {
+ "mean_accuracy_percent": 76.26,
+ "bootstrap_95ci_percent": [
+ 73.55,
+ 78.53
+ ],
+ "independent_units": 5
+ },
+ "noise": {
+ "mean_accuracy_percent": 75.44,
+ "bootstrap_95ci_percent": [
+ 71.01,
+ 78.76
+ ],
+ "independent_units": 5
+ },
+ "raw": {
+ "mean_accuracy_percent": 31.379999999999995,
+ "bootstrap_95ci_percent": [
+ 18.059999999999995,
+ 46.08
+ ],
+ "independent_units": 5
+ },
+ "constant": {
+ "mean_accuracy_percent": 67.9,
+ "bootstrap_95ci_percent": [
+ 60.42,
+ 74.46674999999942
+ ],
+ "independent_units": 5
+ },
+ "sdil": {
+ "mean_accuracy_percent": 74.52000000000001,
+ "bootstrap_95ci_percent": [
+ 72.51,
+ 76.25999999999999
+ ],
+ "independent_units": 5
+ },
+ "oracle": {
+ "mean_accuracy_percent": 75.55999999999999,
+ "bootstrap_95ci_percent": [
+ 70.6,
+ 78.66999999999999
+ ],
+ "independent_units": 5
+ }
+ },
+ "raw_to_clean_gap_recovered_percent": 96.12299465240643
+ },
+ "clln": {
+ "title": "Coupled learning \u00b7 32 edges",
+ "unit": "task; device draws averaged within task",
+ "methods": {
+ "clean": {
+ "mean_accuracy_percent": 100.0,
+ "bootstrap_95ci_percent": [
+ 100.0,
+ 100.0
+ ],
+ "independent_units": 40
+ },
+ "noise": {
+ "mean_accuracy_percent": 99.27083333333334,
+ "bootstrap_95ci_percent": [
+ 98.4375,
+ 99.89583333333334
+ ],
+ "independent_units": 40
+ },
+ "raw": {
+ "mean_accuracy_percent": 65.3125,
+ "bootstrap_95ci_percent": [
+ 61.14583333333333,
+ 69.375
+ ],
+ "independent_units": 40
+ },
+ "constant": {
+ "mean_accuracy_percent": 93.85416666666666,
+ "bootstrap_95ci_percent": [
+ 90.41666666666667,
+ 96.875
+ ],
+ "independent_units": 40
+ },
+ "sdil": {
+ "mean_accuracy_percent": 100.0,
+ "bootstrap_95ci_percent": [
+ 100.0,
+ 100.0
+ ],
+ "independent_units": 40
+ }
+ },
+ "raw_to_clean_gap_recovered_percent": 100.0
+ },
+ "overclamp": {
+ "title": "Overclamped coupled learning \u00b7 32 edges",
+ "unit": "task; device draws averaged within task",
+ "methods": {
+ "clean": {
+ "mean_accuracy_percent": 91.875,
+ "bootstrap_95ci_percent": [
+ 87.8125,
+ 95.3125
+ ],
+ "independent_units": 40
+ },
+ "raw": {
+ "mean_accuracy_percent": 66.875,
+ "bootstrap_95ci_percent": [
+ 61.25,
+ 72.70833333333333
+ ],
+ "independent_units": 40
+ },
+ "sdil": {
+ "mean_accuracy_percent": 91.97916666666666,
+ "bootstrap_95ci_percent": [
+ 88.125,
+ 95.3125
+ ],
+ "independent_units": 40
+ }
+ },
+ "raw_to_clean_gap_recovered_percent": 100.41666666666663
+ }
+ },
+ "sources": {
+ "dualprop": "results/contrastive_bias/c1_gate.json",
+ "ep": "results/ep_bias/c1_gate.json",
+ "clln": "results/coupled_ladder/p2_confirm_side4.json",
+ "overclamp": "results/coupled_ladder/p3_overclamp_side4.json"
+ }
+}
diff --git a/results/figs/figure_digital_additive_transfer.pdf b/results/figs/figure_digital_additive_transfer.pdf
new file mode 100644
index 0000000..4070813
--- /dev/null
+++ b/results/figs/figure_digital_additive_transfer.pdf
Binary files differ
diff --git a/results/figs/figure_digital_additive_transfer.png b/results/figs/figure_digital_additive_transfer.png
new file mode 100644
index 0000000..78046ca
--- /dev/null
+++ b/results/figs/figure_digital_additive_transfer.png
Binary files differ
diff --git a/results/figs/figure_digital_additive_transfer.svg b/results/figs/figure_digital_additive_transfer.svg
new file mode 100644
index 0000000..014aeb9
--- /dev/null
+++ b/results/figs/figure_digital_additive_transfer.svg
@@ -0,0 +1,964 @@
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+ <text style="font-size: 9.5px; font-family: 'DejaVu Sans'" transform="translate(330.643516 215.655516)">(d) Overclamped coupled learning · 32 edges</text>
+ <text style="font-size: 9.5px; font-family: 'DejaVu Sans'" transform="translate(368.531445 226.293437)">100% of clean gap recovered</text>
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diff --git a/visual-composer/digital-additive-transfer.md b/visual-composer/digital-additive-transfer.md
new file mode 100644
index 0000000..74867f8
--- /dev/null
+++ b/visual-composer/digital-additive-transfer.md
@@ -0,0 +1,33 @@
+# Visual contract: additive digital transfer
+
+- **Artifact:** four-panel quantitative result figure.
+- **Target venue / format:** ICLR two-column paper, full-width figure.
+- **Core claim:** the same local instruction-off subtraction improves four
+ digital local-learning backbones under matched teaching-channel
+ imperfection.
+- **Reviewer question:** is SDIL a correction that transfers across learning
+ rules, or does it work only in one custom network?
+- **Evidence layer:** main digital transfer result.
+- **Source data:**
+ `results/contrastive_bias/c1_gate.json`,
+ `results/ep_bias/c1_gate.json`,
+ `results/coupled_ladder/p2_confirm_side4.json`, and
+ `results/coupled_ladder/p3_overclamp_side4.json`.
+- **Statistics / uncertainty:** Dual Propagation and EP resample five seeds;
+ CLLN panels average three device draws within each of 40 tasks and resample
+ tasks. Error bars are percentile 95% bootstrap intervals.
+- **Figure prototype:** coordinated 2-by-2 bar-chart small multiples.
+- **Panel map:** author-code Dual Propagation, author-code EP, digital coupled
+ learning, and digital overclamped coupled learning.
+- **Exact label inventory:** clean, same-RMS noise, raw, static calibration,
+ SDIL, oracle, task accuracy, clean gap recovered.
+- **Caption role:** define raw-to-clean gap recovery within each panel and
+ state that datasets and absolute clean levels differ across panels.
+- **Manuscript placement:** Part 1, after the algorithm definition.
+- **Output formats:** editable SVG, vector PDF, PNG preview, analysis JSON, and
+ source CSV.
+- **Traceability:** every bar is regenerated by
+ `experiments/plot_digital_additive_transfer.py` from the four JSON sources.
+- **Constraint:** the EP five-seed result beats raw in every seed and recovers
+ 96% of the mean clean gap, while its stricter preregistered gate failed. The
+ manuscript and caption must retain that distinction.
diff --git a/visual-composer/qa-ledger.md b/visual-composer/qa-ledger.md
index c9e66e5..f601f93 100644
--- a/visual-composer/qa-ledger.md
+++ b/visual-composer/qa-ledger.md
@@ -26,3 +26,17 @@ changing the plot grammar.
Rendered inspection: no clipped labels, tick-label collision, legend overlap,
or panel overlap at 2,560-by-765 PNG resolution. SVG text remains editable and
the PDF uses embedded TrueType fonts.
+
+## Additive digital transfer figure
+
+| Issue | Artifact | Severity | Fix | Status |
+|:--|:--|:--|:--|:--|
+| Different datasets invite invalid absolute comparisons | All panels | High | Used separate titled panels and defined recovery relative to each panel's own raw and clean endpoints | Resolved |
+| CLLN device draws are not independent tasks | Panels (c) and (d) | High | Averaged device draws within task before task-level bootstrap | Resolved |
+| EP result could be mistaken for a passed strict gate | Panel (b) | High | Stored gate status in analysis and required the caption/manifest to report the partial-gate boundary | Resolved |
+| Long calibration labels could collide | Panels (b) and (c) | Medium | Used two-line labels and inspected the full-resolution render | Resolved |
+| Color-only distinctions could fail in print | All panels | Low | Every bar has a direct method label | Resolved |
+
+Rendered inspection: no clipped labels, title collision, error-bar clipping,
+or panel overlap at 1,920-by-1,392 PNG resolution. SVG text remains editable
+and the PDF uses embedded TrueType fonts.
generated by cgit v1.2.3 (git 2.25.1) at 2026-09-15 08:12:41 +0000