254 lines
10 KiB
Python
254 lines
10 KiB
Python
"""Final paper figures (CPU only), regenerated from consolidated results.
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fig_placement.png : band-entrance cliff + structural nulls
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fig_ladder.png : MBPP-E2B attribution ladder, hard bucket, Wilson CIs
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fig_scale.png : cross-scale / cross-task attribution grid
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fig_transfer.png : substrate transfer panel (HumanEval / Rust / BW)
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"""
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import json
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import math
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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ROOT = Path(__file__).resolve().parent.parent
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OUT = ROOT / "results-loop"
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BLUE, GREEN, ORANGE, GRAY, RED = ("#2b6cb0", "#2f855a", "#dd6b20",
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"#8a8f98", "#c53030")
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def wilson(c, n, z=1.96):
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p = c / n
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d = 1 + z * z / n
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ctr = (p + z * z / (2 * n)) / d
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hw = z * math.sqrt(p * (1 - p) / n + z * z / (4 * n * n)) / d
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return ctr - hw, ctr + hw
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def ev(path, k):
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d = json.load(open(path))
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v = d["ks"][str(k)]
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return v["acc"], v["by_label"].get("hard", float("nan"))
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def best_hard(path, exclude0=True):
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d = json.load(open(path))
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items = [(int(k), v) for k, v in d["ks"].items()
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if not (exclude0 and k == "0")]
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k, v = max(items, key=lambda kv: kv[1]["by_label"].get("hard", 0))
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return k, v["acc"], v["by_label"].get("hard", 0)
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def style(ax):
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ax.grid(True, color="#e8e8e8", lw=0.7)
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ax.set_axisbelow(True)
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for s in ("top", "right"):
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ax.spines[s].set_visible(False)
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# ---------------- fig 1: placement ----------------
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def fig_placement():
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entr = [] # (entrance_layer, overall@bestk, hard@bestk)
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for lo in (9, 11, 12, 13, 14):
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f = ROOT / f"results-band-{lo}_30" / f"eval_code_band_{lo}_30.json"
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if lo == 14:
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f = OUT / "eval_code_code_s0_full.json"
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k, a, h = best_hard(f)
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entr.append((lo, a, h))
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base_a, base_h = ev(OUT / "eval_code_code_s0_full.json", 0)
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fig, ax = plt.subplots(figsize=(6.4, 4.2))
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style(ax)
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xs = [e[0] for e in entr]
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ax.plot(xs, [e[2] for e in entr], "-s", color=BLUE, lw=2, ms=6,
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label="hard bucket (best k)")
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ax.plot(xs, [e[1] for e in entr], "-o", color=GRAY, lw=2, ms=5,
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label="overall (same k)")
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ax.axhline(base_h, color=BLUE, lw=1, ls=":", alpha=0.6)
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ax.axhline(base_a, color=GRAY, lw=1, ls=":", alpha=0.6)
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ax.annotate("base hard", (9.1, base_h + 0.012), fontsize=8, color=BLUE)
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ax.annotate("base overall", (9.1, base_a + 0.012), fontsize=8, color=GRAY)
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ax.axvspan(13.5, 14.5, color="#ebf4ff", zorder=0)
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ax.annotate("lens-identified\nworkspace entrance", (13.55, 0.60),
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fontsize=8, color=BLUE)
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ax.set_xticks(xs)
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ax.set_xlabel("loop entrance layer (exit fixed at L30)")
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ax.set_ylabel("MBPP pass@1")
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ax.set_title("Placement cliff: the retrofit works only at the "
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"lens boundary (L14)", fontsize=11)
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ax.legend(fontsize=8, frameon=False, loc="center left")
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fig.text(0.13, 0.005,
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"Entrances 17/24 (not shown): structurally null — KV sharing "
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"makes k>0 bit-identical to k=0.", fontsize=7.5, color="#666")
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fig.tight_layout(rect=(0, 0.03, 1, 1))
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fig.savefig(OUT / "fig_placement.png", dpi=140, facecolor="white",
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bbox_inches="tight")
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print("wrote fig_placement.png")
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# ---------------- fig 2: attribution ladder ----------------
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def fig_ladder():
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NH = 55
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rows = [] # (label, hard_acc, n, color, note)
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def add(label, h, n, color, note=""):
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rows.append((label, h, n, color, note))
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_, h0 = ev(OUT / "eval_code_code_s0_full.json", 0)
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add("base (k=0, exact)", h0, NH, GRAY)
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_, hu = ev(OUT / "eval_code_untrained_full.json", 4)
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add("untrained loop (perturbation floor)", hu, NH, GRAY)
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_, hf = ev(OUT / "eval_code_code_ff_full.json", 1)
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add("trained FF (no recurrence)", hf, NH, ORANGE)
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_, hp = ev(OUT / "eval_code_pause16.json", 1)
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add("pause-16 registers (width)", hp, NH, ORANGE)
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_, hl = ev(OUT / "eval_code_code_s0_full.json", 4)
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add("loop k=4 (depth)", hl, NH, BLUE, "seed mean 0.375 ± 0.055")
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d = json.load(open(OUT / "eval_rung2_s1.json"))
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add("rung-2: + band LoRA (k=4)", d["4"]["by_label"]["hard"], 28, BLUE)
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_, hd = ev(OUT / "eval_code_distill_s1.json", 1)
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add("plan-distilled FF", hd, NH, GREEN, "8-run mean 0.457 ± 0.046")
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d = json.load(open(OUT / "eval_budgetcot.json"))
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add("budget-CoT (50 visible tok)", d["by_label"]["hard"], NH, "#805ad5")
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d = json.load(open(OUT / "eval_bestof3.json"))
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add("best-of-3 sampling (~matched FLOPs)", d["by_label"]["hard"], NH,
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"#805ad5")
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d = json.load(open(OUT / "eval_plan_baseline.json"))
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add("explicit plan in context (ceiling)", d["by_label"]["hard"], NH,
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"#1a202c")
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fig, ax = plt.subplots(figsize=(7.4, 4.8))
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style(ax)
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ys = range(len(rows))[::-1]
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for y, (label, h, n, color, note) in zip(ys, rows):
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lo, hi = wilson(round(h * n), n)
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ax.barh(y, h, color=color, height=0.62, alpha=0.88)
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ax.plot([lo, hi], [y, y], color="#333", lw=1.2)
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txt = f"{h:.2f}"
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if note:
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txt += f" ({note})"
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ax.text(hi + 0.015, y, txt, va="center", fontsize=8)
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ax.set_yticks(list(ys))
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ax.set_yticklabels([r[0] for r in rows], fontsize=9)
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ax.set_xlim(0, 1.02)
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ax.set_xlabel("pass@1, MBPP hard bucket (plan-dependent items)")
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ax.set_title("Attribution ladder: what closes the plan gap "
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"(bars: point estimate, whiskers: Wilson 95%)", fontsize=11)
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fig.tight_layout()
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fig.savefig(OUT / "fig_ladder.png", dpi=140, facecolor="white",
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bbox_inches="tight")
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print("wrote fig_ladder.png")
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# ---------------- fig 3: cross-scale grid ----------------
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def fig_scale():
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N2 = ROOT / "results-node2-final/results-12b"
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N1 = ROOT / "results-node-final/results-12b"
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panels = {
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("MBPP", "E2B"): [
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("base", *ev(OUT / "eval_code_code_s0_full.json", 0)),
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("loop k=4", *ev(OUT / "eval_code_code_s0_full.json", 4)),
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("adaptive k=4", *ev(OUT / "eval_code_e2b_adaptive.json", 4)),
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("distill", *ev(OUT / "eval_code_distill_s1.json", 1)),
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],
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("MBPP", "12B"): [
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("base", *ev(N1 / "eval_code_12b_trained.json", 0)),
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("loop k=4 (α=.3)", *ev(N1 / "eval_code_12b_trained.json", 4)),
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("adaptive k=4", *ev(N2 / "eval_code_12b_adaptive.json", 4)),
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("distill", *ev(OUT / "eval_code_12b_distill.json", 1)),
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],
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("GSM8K", "E2B"): [
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("base", *ev(OUT / "eval_uni.json", 0)),
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("loop k=2", *ev(OUT / "eval_uni.json", 2)),
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("adaptive k=2", *ev(OUT / "eval_gsm_e2b_adaptive.json", 2)),
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("distill", *ev(OUT / "eval_gsm_distill_retry.json", 1)),
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],
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("GSM8K", "12B"): [
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("base", *ev(N1 / "eval_12b_gsm_loop.json", 0)),
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("loop k=2 (α=.3)", *ev(N1 / "eval_12b_gsm_loop.json", 2)),
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("adaptive k=2", *ev(OUT / "eval_12b_gsm_adaptive.json", 2)),
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("distill*", *ev(OUT / "eval_12b_gsm_distill.json", 1)),
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],
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}
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fig, axes = plt.subplots(2, 2, figsize=(9.6, 6.6))
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for ax, ((task, scale), arms) in zip(axes.flat, panels.items()):
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if (task, scale) == ("GSM8K", "12B"):
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ax.annotate("*training collapse (0.00)", (3, 0.03), fontsize=7.5,
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ha="center", color=RED)
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style(ax)
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x = range(len(arms))
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ax.bar([i - 0.19 for i in x], [a[1] for a in arms], width=0.36,
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color=GRAY, alpha=0.85, label="overall")
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ax.bar([i + 0.19 for i in x], [a[2] for a in arms], width=0.36,
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color=BLUE, alpha=0.85, label="hard")
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ax.axhline(arms[0][1], color=GRAY, lw=1, ls=":")
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ax.set_xticks(list(x))
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ax.set_xticklabels([a[0] for a in arms], fontsize=8)
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ax.set_title(f"{task} · {scale}", fontsize=10)
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ax.set_ylim(0, 1.0)
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if ax is axes.flat[0]:
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ax.legend(fontsize=8, frameon=False)
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fig.suptitle("Cross-scale attribution: constant-α destroys the 12B "
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"substrate; state-dependent α restores it (MBPP) but not "
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"everywhere", fontsize=11.5)
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fig.tight_layout(rect=(0, 0, 1, 0.96))
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fig.savefig(OUT / "fig_scale.png", dpi=140, facecolor="white",
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bbox_inches="tight")
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print("wrote fig_scale.png")
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# ---------------- fig 4: transfer panel ----------------
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def fig_transfer():
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def he_hard(path, k):
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d = json.load(open(OUT / path))
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return d["ks"][str(k)]["acc"], d["ks"][str(k)]["hard_acc"]
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groups = [
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("HumanEval\n(MBPP-trained)", [
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("base", *he_hard("eval_humaneval_trained.json", 0)),
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("loop k=4", *he_hard("eval_humaneval_trained.json", 4)),
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("distill", *he_hard("eval_humaneval_distill_transfer.json", 1)),
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]),
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("Rust / MultiPL-E\n(Python-trained)", [
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("base", *ev(OUT / "eval_rust_py_transfer.json", 0)),
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("loop k=4", *ev(OUT / "eval_rust_py_transfer.json", 4)),
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]),
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("LiveCodeBench (far)\nuntrained wins", [
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("base", *he_hard("eval_lcb_untrained.json", 0)),
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("untrained k=4", *he_hard("eval_lcb_untrained.json", 4)),
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("loop k=4", *he_hard("eval_lcb_loop.json", 4)),
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("distill", *he_hard("eval_lcb_distill.json", 1)),
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]),
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]
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fig, axes = plt.subplots(1, 3, figsize=(10.8, 3.8))
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for ax, (title, arms) in zip(axes, groups):
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style(ax)
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x = range(len(arms))
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ax.bar([i - 0.19 for i in x], [a[1] for a in arms], width=0.36,
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color=GRAY, alpha=0.85, label="overall")
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ax.bar([i + 0.19 for i in x],
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[a[2] if a[2] is not None else 0 for a in arms],
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width=0.36, color=BLUE, alpha=0.85, label="hard")
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ax.set_xticks(list(x))
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ax.set_xticklabels([a[0] for a in arms], fontsize=8.5)
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ax.set_title(title, fontsize=9.5)
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ax.set_ylim(0, 1.0)
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axes[0].legend(fontsize=8, frameon=False)
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fig.suptitle("Transfer: the implant moves with the substrate, "
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"not the task", fontsize=11.5)
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fig.tight_layout(rect=(0, 0, 1, 0.94))
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fig.savefig(OUT / "fig_transfer.png", dpi=140, facecolor="white",
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bbox_inches="tight")
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print("wrote fig_transfer.png")
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if __name__ == "__main__":
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fig_placement()
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fig_ladder()
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fig_scale()
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fig_transfer()
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