J-lens workspace reproduction + loop retrofit: lens, band looping, adapters, controls, multi-task evals

Reproduction of the 2026 workspace/J-lens paper on gemma-4 (E2B/12B/26B),
plus the workspace-loop retrofit line: merge adapter, prompt-only latent
planning (MBPP), carry variant, attribution controls (FF/pause/untrained),
band-location ablation, Blocksworld harness, 12B replication scripts.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

scripts/plot_code_eval.py

@@ -0,0 +1,51 @@
+"""MBPP latent-planning results: pass@1 vs loop depth, trained vs untrained."""
+import json
+from pathlib import Path
+
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+OUT = Path(__file__).resolve().parent.parent / "results-loop"
+tr = json.load(open(OUT / "eval_code_trained.json"))
+un = json.load(open(OUT / "eval_code_untrained.json"))
+ks = sorted(int(k) for k in tr["ks"])
+BLUE, GRAY = "#2b6cb0", "#8a8f98"
+
+def curve(res, sel):
+    return [res["ks"][str(k)]["acc"] if sel == "all"
+            else res["ks"][str(k)]["by_label"].get(sel, 0.0) for k in ks]
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4.2))
+for ax in (ax1, ax2):
+    ax.grid(True, color="#e5e5e5", lw=0.7)
+    ax.set_axisbelow(True)
+    for s in ("top", "right"):
+        ax.spines[s].set_visible(False)
+    ax.set_xticks(ks)
+    ax.set_xlabel("loop depth k (prompt-only)")
+
+ax1.plot(ks, curve(tr, "all"), "-o", color=BLUE, lw=2, ms=5, label="trained · all")
+ax1.plot(ks, curve(un, "all"), "-o", color=GRAY, lw=2, ms=5, label="untrained · all")
+ax1.axhline(curve(tr, "all")[0], color="#bbb", lw=1, ls=":")
+ax1.set_ylabel("pass@1 (250 MBPP test items)")
+ax1.set_title("Overall: trained k=2 beats no-loop baseline", fontsize=11)
+ax1.legend(fontsize=8, frameon=False)
+
+ax2.plot(ks, curve(tr, "hard"), "-s", color=BLUE, lw=2, ms=5,
+         label="trained · hard (plan-only)")
+ax2.plot(ks, curve(un, "hard"), "-s", color=GRAY, lw=2, ms=5,
+         label="untrained · hard")
+ax2.set_ylabel("pass@1, hard bucket (~56 items)")
+ax2.set_title("Planning-dependent problems: 3.6% → 46.4%", fontsize=11)
+ax2.legend(fontsize=8, frameon=False)
+ax2.annotate("silent loops recover ~46%\nof explicit-planning gap",
+             xy=(4, curve(tr, "hard")[-1]), xytext=(1.8, 0.30), fontsize=8,
+             color=BLUE, arrowprops=dict(arrowstyle="-", color=BLUE, lw=0.8))
+
+fig.suptitle("MBPP latent planning: loop the workspace over the prompt, "
+             "then write code normally", fontsize=12, y=1.02)
+fig.tight_layout()
+fig.savefig(OUT / "loop_eval_code.png", dpi=140, bbox_inches="tight",
+            facecolor="white")
+print("wrote", OUT / "loop_eval_code.png")