figure: show SDIL transfer across digital learners

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+# 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.