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).

8 files changed, 214 insertions, 13 deletions

diff --git a/paper/figures/fig2_decision_utility.pdf b/paper/figures/fig2_decision_utility.pdf
index fae5c98..e37004a 100644
--- a/paper/figures/fig2_decision_utility.pdf
+++ b/paper/figures/fig2_decision_utility.pdf
diff --git a/paper/figures/fig4_penalty_rescue.pdf b/paper/figures/fig4_penalty_rescue.pdf
index 07f9b95..0dcdce7 100644
--- a/paper/figures/fig4_penalty_rescue.pdf
+++ b/paper/figures/fig4_penalty_rescue.pdf
diff --git a/paper/figures/fig5_cross_arch_summary.pdf b/paper/figures/fig5_cross_arch_summary.pdf
index c5ac3fc..93c0676 100644
--- a/paper/figures/fig5_cross_arch_summary.pdf
+++ b/paper/figures/fig5_cross_arch_summary.pdf
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}")
diff --git a/paper/figures/render_fig4_penalty_rescue.py b/paper/figures/render_fig4_penalty_rescue.py
new file mode 100644
index 0000000..b7089ec
--- /dev/null
+++ b/paper/figures/render_fig4_penalty_rescue.py
@@ -0,0 +1,92 @@
+"""Render Figure 4: penalty rescue + capacity-cost control."""
+import os
+import json
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+REPO_ROOT = "/home/yurenh2/fa"
+
+# Panel A: penalty rescue trajectory
+with open(os.path.join(REPO_ROOT, "results/snapshot_evolution_v2/snapshot_evolution_s42.json")) as f:
+    snap = json.load(f)
+vanilla = snap["dfa_log"]
+ep_vanilla = [e["epoch"] for e in vanilla]
+hL_vanilla = [e["hidden_norms"][-1] for e in vanilla]
+g_vanilla = [e["bp_grad_norms_per_sample_med"][-1] for e in vanilla]
+
+with open(os.path.join(REPO_ROOT, "results/dfa_residual_penalty/dfa_pen_lam0.01_s42.json")) as f:
+    pen = json.load(f)
+ep_pen = [e["epoch"] for e in pen["log"]]
+hL_pen = [e["h_L_norm"] for e in pen["log"]]
+g_pen = [e["g_2_norm"] for e in pen["log"]]
+
+# Panel B: cosine + rho across vanilla / penalized / fresh-B / penalty lam=1e-4
+# Read from existing results
+conditions = ["vanilla\nDFA\n(early)", "penalized\n$\\lambda{=}10^{-4}$", "penalized\n$\\lambda{=}10^{-2}$", "fresh-$B$\nnull", "BP grad\n(positive)"]
+deep_cos = [-0.008, -0.022, +0.155, +0.002, +1.000]
+deep_rho = [-0.003, -0.004, +0.080, +0.006, +0.997]
+cos_err = [0.013, 0.0, 0.025, 0.022, 0.0]
+rho_err = [0.005, 0.0, 0.011, 0.0, 0.0]
+
+# Panel C: 2x2 capacity-cost control
+methods = ["BP", "DFA"]
+no_pen = [0.609, 0.308]
+with_pen = [0.530, 0.363]
+shallow = 0.349
+
+fig, axes = plt.subplots(1, 3, figsize=(13, 3.5))
+
+# Panel A: trajectory
+ax = axes[0]
+ax.plot(ep_vanilla, hL_vanilla, label="vanilla DFA $\\|h_L\\|$", color="C3", lw=1.5, marker="o", markersize=3)
+ax.plot(ep_pen, hL_pen, label="penalized DFA $\\|h_L\\|$ ($\\lambda{=}10^{-2}$)", color="C2", lw=1.5, marker="s", markersize=3)
+ax.set_yscale("log")
+ax.set_xlabel("epoch", fontsize=10)
+ax.set_ylabel("$\\|h_L\\|$ (log)", fontsize=10)
+ax.set_title("(a) penalty contains residual stream\n(4 OOM rescue)", fontsize=10)
+ax.legend(loc="lower right", fontsize=8)
+ax.grid(True, alpha=0.3, which="both")
+ax2 = ax.twinx()
+ax2.plot(ep_vanilla, g_vanilla, label="vanilla $\\|g\\|$", color="C3", lw=1, ls=":", marker="^", markersize=3)
+ax2.plot(ep_pen, g_pen, label="penalized $\\|g\\|$", color="C2", lw=1, ls=":", marker="v", markersize=3)
+ax2.axhline(1e-7, color="black", ls="--", lw=0.8, label="$10^{-7}$ floor")
+ax2.set_yscale("log")
+ax2.set_ylabel("$\\|g_L\\|$ (log)", fontsize=9, color="gray")
+ax2.tick_params(axis="y", labelcolor="gray")
+
+# Panel B: cosine + rho
+ax = axes[1]
+xpos = np.arange(len(conditions))
+w = 0.35
+b1 = ax.bar(xpos - w/2, deep_cos, w, yerr=cos_err, label="deep cos", color="#4682b4", capsize=3)
+b2 = ax.bar(xpos + w/2, deep_rho, w, yerr=rho_err, label="deep $\\rho$", color="#7da76f", capsize=3)
+ax.axhline(0, color="black", lw=0.5)
+ax.set_xticks(xpos)
+ax.set_xticklabels(conditions, fontsize=8)
+ax.set_ylabel("deep-layer alignment", fontsize=10)
+ax.set_title("(b) two metrics agree across conditions\n(measurement vs random feedback)", fontsize=10)
+ax.legend(loc="upper left", fontsize=8)
+ax.grid(True, axis="y", alpha=0.3)
+ax.set_ylim(-0.1, 1.1)
+
+# Panel C: 2x2 capacity-cost
+ax = axes[2]
+xpos = np.arange(len(methods))
+w = 0.35
+ax.bar(xpos - w/2, no_pen, w, label="no penalty", color="#4682b4")
+ax.bar(xpos + w/2, with_pen, w, label="with penalty $\\lambda{=}10^{-2}$", color="#cc4444")
+ax.axhline(shallow, color="black", ls="--", lw=1, label=f"frozen baseline {shallow}")
+ax.set_xticks(xpos)
+ax.set_xticklabels(methods, fontsize=10)
+ax.set_ylabel("test accuracy", fontsize=10)
+ax.set_title("(c) BP+penalty 2$\\times$2 control\n(BP-pen-cost $-8$pp; gap $17$pp $=$ credit quality)", fontsize=10)
+ax.legend(loc="upper right", fontsize=8)
+ax.grid(True, axis="y", alpha=0.3)
+ax.set_ylim(0, 0.7)
+
+plt.tight_layout()
+out = os.path.join(REPO_ROOT, "paper/figures/fig4_penalty_rescue.pdf")
+plt.savefig(out, bbox_inches="tight", dpi=200)
+print(f"Saved {out}")
diff --git a/paper/figures/render_fig5_cross_arch.py b/paper/figures/render_fig5_cross_arch.py
new file mode 100644
index 0000000..e163956
--- /dev/null
+++ b/paper/figures/render_fig5_cross_arch.py
@@ -0,0 +1,53 @@
+"""Render Figure 5: cross-architecture verdict matrix."""
+import os
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+REPO_ROOT = "/home/yurenh2/fa"
+
+# Verdict matrix: arch x diagnostic
+# 0 = ok (BP), 1 = ok-non-LN-arch, 2 = walk-back
+# Columns: (a) per-block growth, (b) ||g_L|| floor, (c) drift stability, (d) frozen baseline
+# Rows: ResMLP-d256, ResMLP-d512, ViT-Mini, StudentNet (no LN), CNN (BN, no LN)
+arches = ["ResMLP $d{=}256$\n(terminal LN)", "ResMLP $d{=}512$\n(terminal LN)", "ViT-Mini\n(cls + LN)", "StudentNet\n(no terminal LN)", "CNN BatchNorm\n(no terminal LN)"]
+diags = ["(a) scale", "(b) ${\\|g\\|}$ floor", "(c) drift", "(d) frozen"]
+
+# DFA verdicts on each
+# 1 = fires (walked back); 0 = passes
+dfa = np.array([
+    [1, 1, 0, 1],   # ResMLP d256: (a) fires, (b) fires, (c) noise sub-mode, (d) fires
+    [1, 1, 0, 1],   # ResMLP d512: same pattern
+    [1, 1, 0, 1],   # ViT-Mini: same pattern
+    [1, 0, 0, 0],   # StudentNet: only (a) fires; (b) NEVER
+    [1, 0, 0, 0],   # CNN BN: only (a) fires; (b) NEVER (the killer (b)-is-LN-specific finding)
+])
+
+bp = np.zeros_like(dfa)  # BP: passes everywhere
+
+fig, axes = plt.subplots(1, 2, figsize=(11, 3.5))
+
+for ax, mat, title in [(axes[0], bp, "BP-trained: protocol passes"),
+                       (axes[1], dfa, "DFA-trained: protocol verdict by architecture")]:
+    cmap = matplotlib.colors.ListedColormap(["#4682b4", "#cc4444"])
+    im = ax.imshow(mat, cmap=cmap, aspect="auto", vmin=0, vmax=1)
+    for i in range(mat.shape[0]):
+        for j in range(mat.shape[1]):
+            txt = "WB" if mat[i, j] == 1 else "$\\checkmark$"
+            ax.text(j, i, txt, ha="center", va="center", color="white", fontsize=11, fontweight="bold")
+    ax.set_xticks(range(len(diags)))
+    ax.set_xticklabels(diags, fontsize=9)
+    ax.set_yticks(range(len(arches)))
+    ax.set_yticklabels(arches, fontsize=9)
+    ax.set_title(title, fontsize=10)
+
+# Highlight the key finding
+axes[1].text(0.5, -1.0, "Key finding: diagnostic (b) BP-grad-floor fires only on terminal-LN architectures.\n"
+             "Across the 5 architecture cases tested, (b) is restricted to the with-terminal-LN family.",
+             ha="center", fontsize=9, style="italic", transform=axes[1].transAxes)
+
+plt.tight_layout()
+out = os.path.join(REPO_ROOT, "paper/figures/fig5_cross_arch_summary.pdf")
+plt.savefig(out, bbox_inches="tight", dpi=200)
+print(f"Saved {out}")
diff --git a/paper/main.pdf b/paper/main.pdf
index 99ae08b..c737bb3 100644
--- a/paper/main.pdf
+++ b/paper/main.pdf
diff --git a/paper/main.tex b/paper/main.tex
index 2cae264..b0a7787 100644
--- a/paper/main.tex
+++ b/paper/main.tex
@@ -50,11 +50,11 @@ On the 4-block pre-LayerNorm ResMLP at $d{=}256$ on CIFAR-10, the field-standard
 \toprule
 Method & Test acc. & Headline $\Gamma$ & Status-quo verdict & Protocol verdict \\
 \midrule
-BP & \textit{TODO} & \textit{TODO} & trustworthy & trustworthy \\
-EP & \textit{TODO} & \textit{TODO} & trustworthy & trustworthy \\
-DFA & \textit{TODO} & \textit{TODO} & trustworthy & walked back \\
-State Bridge & \textit{TODO} & \textit{TODO} & trustworthy & walked back \\
-Credit Bridge & \textit{TODO} & \textit{TODO} & trustworthy & walked back \\
+BP            & $0.615 \pm 0.003$ & $\approx 1.0$ & trustworthy & trustworthy \\
+EP            & $0.316 \pm 0.030$ & $0.008$       & trustworthy & trustworthy \\
+DFA           & $0.306 \pm 0.006$ & $0.10$        & trustworthy & walked back \\
+State Bridge  & $0.205 \pm 0.032$ & $0.005$       & trustworthy & walked back \\
+Credit Bridge & $0.289 \pm 0.026$ & $0.07$        & trustworthy & walked back \\
 \bottomrule
 \end{tabular}
 \end{table}
@@ -109,10 +109,10 @@ Temporal replay shows that the protocol fires early enough to change experimenta
 \toprule
 Condition & Deep-layer alignment signal & Measurement regime & Interpretation \\
 \midrule
-Vanilla DFA, early epoch & \textit{TODO} & \textit{TODO} & mode 2 present without mode 1 \\
-Vanilla DFA, converged & \textit{TODO} & \textit{TODO} & mode 1 obscures mode 2 \\
-Penalized DFA & \textit{TODO} & \textit{TODO} & partial alleviation of both modes \\
-Fresh-$B$ null control & \textit{TODO} & meaningful & training-specific adaptation check \\
+Vanilla DFA, early epoch & $\overline{\cos}_{deep}{=}{-}0.008{\pm}0.013$, $\overline{\rho}_{deep}{=}{-}0.003{\pm}0.005$ & meaningful ($\|g\|{\sim}10^{-6}$) & mode 2 present without mode 1 \\
+Vanilla DFA, converged & $\overline{\cos}_{deep}{=}{-}0.022$, $\overline{\rho}_{deep}{=}+0.002$ & degenerate ($\|g\|{\sim}10^{-9}$) & mode 1 obscures mode 2 \\
+Penalized DFA, $\lambda{=}10^{-2}$ & $\overline{\cos}_{deep}{=}+0.155{\pm}0.025$, $\overline{\rho}_{deep}{=}+0.080{\pm}0.011$ & meaningful ($\|g\|{\sim}10^{-6}$) & partial alleviation of both modes \\
+Fresh-$B$ null control & $\overline{\cos}_{deep}{=}+0.002{\pm}0.022$ ($n{=}20$ draws) & meaningful & training-specific adaptation check \\
 \bottomrule
 \end{tabular}
 \end{table}
@@ -161,10 +161,10 @@ The protocol has four diagnostics because the evaluation failure is not visible
 \toprule
 Diag. & Measurement & Default threshold & Role \\
 \midrule
-(a) & Per-layer activation scale via max-per-block growth $\max_l \|h_{l+1}\|/\|h_l\|$ & \textit{TODO} & binary detector \\
-(b) & Deepest hidden-layer BP gradient norm $\|g_L\|$ & \textit{TODO} & binary detector \\
-(c) & Cross-batch direction stability of normalized BP gradients & \textit{TODO} & sub-mode discriminator \\
-(d) & Frozen-blocks baseline margin for trained blocks over random blocks & \textit{TODO} & depth-utilization check \\
+(a) & Per-layer activation scale via max-per-block growth $\max_l \|h_{l+1}\|/\|h_l\|$ & $> 50\times$ & binary detector \\
+(b) & Deepest hidden-layer BP gradient norm $\|g_L\|$ & $< 10^{-7}$ & binary detector \\
+(c) & Cross-batch direction stability of normalized BP gradients & $> 0.30$ & sub-mode discriminator \\
+(d) & Frozen-blocks baseline margin for trained blocks over random blocks & $< 2$pp & depth-utilization check \\
 \bottomrule
 \end{tabular}
 \end{table}