From e9f1342fc8e233a4841b7eb3c1363324e90ecda9 Mon Sep 17 00:00:00 2001
From: YurenHao0426 <Blackhao0426@gmail.com>
Date: Sat, 29 Aug 2026 18:43:05 -0500
Subject: figure: show SDIL transfer across digital learners

---
 experiments/plot_digital_additive_transfer.py     | 325 ++++++++
 results/digital_additive_transfer.csv             |  19 +
 results/digital_additive_transfer.json            | 194 +++++
 results/figs/figure_digital_additive_transfer.pdf | Bin 0 -> 17919 bytes
 results/figs/figure_digital_additive_transfer.png | Bin 0 -> 157768 bytes
 results/figs/figure_digital_additive_transfer.svg | 964 ++++++++++++++++++++++
 visual-composer/digital-additive-transfer.md      |  33 +
 visual-composer/qa-ledger.md                      |  14 +
 8 files changed, 1549 insertions(+)
 create mode 100644 experiments/plot_digital_additive_transfer.py
 create mode 100644 results/digital_additive_transfer.csv
 create mode 100644 results/digital_additive_transfer.json
 create mode 100644 results/figs/figure_digital_additive_transfer.pdf
 create mode 100644 results/figs/figure_digital_additive_transfer.png
 create mode 100644 results/figs/figure_digital_additive_transfer.svg
 create mode 100644 visual-composer/digital-additive-transfer.md

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
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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
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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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+    <dc:type rdf:resource="http://purl.org/dc/dcmitype/StillImage"/>
+    <dc:date>2026-08-29T18:42:31.692423</dc:date>
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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.
-- 
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