Fill in tables 1-3 + generate figures 2/4/5 from existing data

Tables filled with real values:
  Table 1: 5-method audit (3-seed mean ± std for acc, headline Γ, verdict)
  Table 2: 4-condition mode 2 validation (cos and ρ values from existing
           checkpoint measurements)
  Table 3: protocol thresholds (50×, 1e-7, 0.30, 2pp)

Figures generated from existing data:
  fig2_decision_utility.pdf: 5×7 verdict heatmap from
    results/protocol_audit/ablation_decision_utility.json
  fig4_penalty_rescue.pdf: 3-panel — trajectory + cos/ρ bars + 2×2 acc
    from snapshot_evolution_v2 + dfa_residual_penalty + bp_with_penalty
  fig5_cross_arch_summary.pdf: 5×4 BP/DFA verdict matrix across
    architectures

Compiles to 8 pages with all tables/figures rendered. §1-§7 main body
still has only paragraph topic sentences (TODO: per-section prose
filling via codex). Figure numbering is wrong (codex put figures in
section order not numerical order — need fixing).

1 files changed, 56 insertions, 0 deletions

diff --git a/paper/figures/render_fig2_decision_utility.py b/paper/figures/render_fig2_decision_utility.py
new file mode 100644
index 0000000..ba1a148
--- /dev/null
+++ b/paper/figures/render_fig2_decision_utility.py
@@ -0,0 +1,56 @@
+"""Render Figure 2: decision-utility ablation as a heatmap."""
+import os
+import json
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+REPO_ROOT = "/home/yurenh2/fa"
+with open(os.path.join(REPO_ROOT, "results/protocol_audit/ablation_decision_utility.json")) as f:
+    data = json.load(f)
+
+methods = ["bp", "ep", "dfa", "credit_bridge", "state_bridge"]
+method_labels = ["BP", "EP", "DFA", "Credit Bridge", "State Bridge"]
+strategies = ["S0", "S1", "S2", "S3", "S4", "S5", "S_full"]
+strategy_labels = ["S0\nacc only", "S1\n+$\\Gamma$\n(field std)", "S2\n+(a)\nscale", "S3\n+(b)\nfloor", "S4\n+(c)\nstability", "S5\n+(d)\nfrozen", "S$_\\mathrm{full}$\nfull"]
+
+# Build a verdict matrix: 1 = walk back (red), 0 = trustworthy (blue)
+mat = np.zeros((len(methods), len(strategies)))
+for i, m in enumerate(methods):
+    for j, s in enumerate(strategies):
+        v = data["table"][m][s]
+        if "WALK" in v:
+            mat[i, j] = 1.0
+
+fig, ax = plt.subplots(figsize=(7, 3.2))
+cmap = matplotlib.colors.ListedColormap(["#4682b4", "#cc4444"])
+im = ax.imshow(mat, cmap=cmap, aspect="auto", vmin=0, vmax=1)
+
+# Annotate each cell
+for i in range(len(methods)):
+    for j in range(len(strategies)):
+        v = data["table"][methods[i]][strategies[j]]
+        txt = "WB" if "WALK" in v else "$\\checkmark$"
+        ax.text(j, i, txt, ha="center", va="center",
+                color="white" if mat[i, j] > 0.5 else "white", fontsize=10, fontweight="bold")
+
+ax.set_xticks(range(len(strategies)))
+ax.set_xticklabels(strategy_labels, fontsize=8)
+ax.set_yticks(range(len(methods)))
+ax.set_yticklabels(method_labels, fontsize=10)
+ax.set_xlabel("reporting strategy", fontsize=10)
+
+# Color legend
+from matplotlib.patches import Patch
+legend_elements = [
+    Patch(facecolor="#4682b4", label="$\\checkmark$ trustworthy"),
+    Patch(facecolor="#cc4444", label="WB walked back"),
+]
+ax.legend(handles=legend_elements, loc="upper center", bbox_to_anchor=(0.5, -0.25), ncol=2, fontsize=9, frameon=False)
+
+ax.set_title("Decision-utility ablation: 7 reporting strategies vs 5 methods\nfield-standard pair (S0, S1) walks back 0/5; full protocol walks back 3/5", fontsize=10)
+plt.tight_layout()
+out = os.path.join(REPO_ROOT, "paper/figures/fig2_decision_utility.pdf")
+plt.savefig(out, bbox_inches="tight", dpi=200)
+print(f"Saved {out}")