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6c5f29f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 | """Generate MemAudit paper figures from canonical artifacts.
The script is intentionally dependency-light: matplotlib plus the Python
standard library. It reads existing run summaries and writes vector PDF/SVG
figures to the figures/ directory.
"""
from __future__ import annotations
import argparse
import json
from pathlib import Path
import matplotlib.pyplot as plt
from matplotlib.patches import FancyArrowPatch, FancyBboxPatch, Rectangle
ROOT = Path(__file__).resolve().parents[1]
FIG_DIR = ROOT / "figures"
PALETTE = {
"oracle": "#0072B2",
"opt": "#111111",
"full_raw": "#D55E00",
"density": "#E69F00",
"no_tombstone": "#CC79A7",
"fact": "#009E73",
"summary": "#B58900",
"recency": "#7A7A7A",
"fifo": "#7A7A7A",
"light": "#F7F7F7",
"mid": "#9ECAE1",
"dark": "#08519C",
}
METHOD_LABELS = {
"oracle_gvt": "MemAudit-GVT",
"density_only": "Density-only",
"no_tombstone_opt": "No-tombstone OPT",
"fact_only": "Fact-only",
"summary_only": "Summary-only",
"recency_raw": "Recency raw",
"dense_budgeted_bsc": "MemAudit + dense",
"dense_rag_e5": "Full raw dense",
"dense_budgeted_replay": "Budgeted raw replay",
"fifo_replay": "FIFO",
}
def load_json(path: str | Path):
return json.loads((ROOT / path).read_text(encoding="utf-8"))
def save(fig, name: str):
FIG_DIR.mkdir(exist_ok=True)
for ext in ("pdf", "svg"):
fig.savefig(FIG_DIR / f"{name}.{ext}", bbox_inches="tight")
plt.close(fig)
def style_axes(ax):
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.grid(axis="y", color="#E5E5E5", linewidth=0.8)
ax.set_axisbelow(True)
def add_box(ax, xy, w, h, text, color, fontsize=9):
box = FancyBboxPatch(
xy,
w,
h,
boxstyle="round,pad=0.02,rounding_size=0.03",
linewidth=1.2,
edgecolor=color,
facecolor=color,
alpha=0.12,
)
ax.add_patch(box)
ax.text(xy[0] + w / 2, xy[1] + h / 2, text, ha="center", va="center", fontsize=fontsize)
def arrow(ax, p1, p2, color="#444444"):
ax.add_patch(FancyArrowPatch(p1, p2, arrowstyle="->", mutation_scale=12, lw=1.2, color=color))
def pipeline_schematic():
fig, ax = plt.subplots(figsize=(10, 3.1))
ax.set_xlim(0, 10)
ax.set_ylim(0, 3)
ax.axis("off")
ax.add_patch(Rectangle((0.2, 0.15), 9.6, 0.95, facecolor="#FCE8C8", edgecolor="none", alpha=0.7))
ax.text(5, 0.9, "Hidden event graph -> evidence units -> future query requirements", ha="center", fontsize=9)
xs = [0.35, 1.75, 3.15, 4.55, 5.95, 7.35, 8.75]
labels = [
"Experience\nstream",
"Candidate\nrepresentations",
"Budgeted\nwriter",
"Memory\nstore X",
"Exact oracle\nF(X) / OPT",
"Retriever",
"Reader\nanswers",
]
colors = ["#7A7A7A", "#CC79A7", "#0072B2", "#0072B2", "#111111", "#009E73", "#009E73"]
for x, label, color in zip(xs, labels, colors):
add_box(ax, (x, 1.65), 1.0, 0.7, label, color)
for x1, x2 in zip(xs[:-1], xs[1:]):
arrow(ax, (x1 + 1.02, 2.0), (x2 - 0.05, 2.0))
ax.text(1.75, 1.38, "discard / raw / fact / summary / tombstone / compound", ha="center", fontsize=7)
ax.text(5.95, 1.38, "computed before retrieval and reader reasoning", ha="center", fontsize=7)
save(fig, "pipeline_schematic")
def tombstone_timeline():
fig, ax = plt.subplots(figsize=(8, 2.8))
ax.set_xlim(0, 10)
ax.set_ylim(0, 3)
ax.axis("off")
ax.plot([1, 9], [2.35, 2.35], color="#444444", lw=1.5)
for x, label in [(2, "t1"), (6, "t2"), (8.7, "future query")]:
ax.plot([x, x], [2.25, 2.45], color="#444444", lw=1)
ax.text(x, 2.58, label, ha="center", fontsize=8)
ax.text(2, 2.0, '"I prefer vegetarian\nmeals for travel."', ha="center", fontsize=8)
ax.text(6, 2.0, '"Actually, I am\npescatarian now."', ha="center", fontsize=8)
ax.text(8.7, 2.0, "What meals\nshould we book?", ha="center", fontsize=8)
add_box(ax, (0.7, 0.85), 2.0, 0.55, "Old fact:\ntravel = vegetarian", "#D55E00", fontsize=8)
ax.plot([0.9, 2.5], [1.12, 1.12], color="#D55E00", lw=2)
add_box(ax, (3.4, 0.85), 2.0, 0.55, "Current fact:\ntravel = pescatarian", "#009E73", fontsize=8)
add_box(ax, (6.1, 0.85), 2.4, 0.55, "Tombstone:\nvegetarian invalid after t2", "#CC79A7", fontsize=8)
arrow(ax, (5.45, 1.12), (6.05, 1.12), "#444444")
ax.text(5, 0.35, "Compound update stores the new current fact plus the invalidation record.", ha="center", fontsize=9)
save(fig, "tombstone_timeline")
def exact_budget_sweep():
data = load_json("oraclemem_runs/exact_500/summary.json")
rows = data["by_budget_method"]
budgets = [2, 4, 8, 16]
methods = ["oracle_gvt", "density_only", "no_tombstone_opt", "fact_only", "summary_only", "recency_raw"]
colors = {
"oracle_gvt": PALETTE["oracle"],
"density_only": PALETTE["density"],
"no_tombstone_opt": PALETTE["no_tombstone"],
"fact_only": PALETTE["fact"],
"summary_only": PALETTE["summary"],
"recency_raw": PALETTE["recency"],
}
lookup = {(r["budget"], r["method"]): r for r in rows}
fig, ax = plt.subplots(figsize=(6.5, 3.6))
for method in methods:
ys = [lookup[(b, method)]["mean_ratio_to_opt"] for b in budgets]
lows = [lookup[(b, method)]["bootstrap95_ratio_to_opt_low"] for b in budgets]
highs = [lookup[(b, method)]["bootstrap95_ratio_to_opt_high"] for b in budgets]
ax.plot(budgets, ys, marker="o", lw=2, label=METHOD_LABELS[method], color=colors[method])
ax.fill_between(budgets, lows, highs, color=colors[method], alpha=0.12, linewidth=0)
ax.axhline(1.0, color=PALETTE["opt"], lw=1, ls="--", label="Exact OPT")
ax.set_xlabel("Storage budget B")
ax.set_ylabel("Ratio to exact OPT")
ax.set_ylim(-0.02, 1.05)
ax.set_xticks(budgets)
style_axes(ax)
ax.legend(ncol=2, fontsize=8, frameon=False)
save(fig, "exact_budget_sweep")
def stress_heatmap_and_gap():
data = load_json("oraclemem_runs/stress_exact_500/summary.json")
rows = data["by_distribution_budget_method"]
dists = ["base", "update_chain", "temporal_interval"]
methods = ["oracle_gvt", "density_only", "no_tombstone_opt"]
lookup = {(r["distribution"], r["budget"], r["method"]): r for r in rows}
budget = 6
vals = [[lookup[(d, budget, m)]["mean_ratio_to_opt"] for d in dists] for m in methods]
fig, ax = plt.subplots(figsize=(5.8, 3.0))
im = ax.imshow(vals, cmap="Blues", vmin=0, vmax=1)
ax.set_xticks(range(len(dists)), ["Base", "Update\nchain", "Temporal\ninterval"])
ax.set_yticks(range(len(methods)), [METHOD_LABELS[m] for m in methods])
for i, row in enumerate(vals):
for j, val in enumerate(row):
ax.text(j, i, f"{val:.3f}", ha="center", va="center", fontsize=9, color="#111111")
ax.set_title("Validity-heavy stress suite (B=6)", fontsize=10)
fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04, label="Ratio to OPT")
save(fig, "stress_heatmap")
gaps = [1.0 - lookup[(d, budget, "no_tombstone_opt")]["mean_ratio_to_opt"] for d in dists]
fig, ax = plt.subplots(figsize=(5.2, 3.0))
ax.bar(["Base", "Update\nchain", "Temporal\ninterval"], gaps, color=PALETTE["no_tombstone"], alpha=0.85)
ax.set_ylabel("Full OPT - no-tombstone OPT")
ax.set_ylim(0, max(gaps) * 1.25)
style_axes(ax)
for i, val in enumerate(gaps):
ax.text(i, val + 0.015, f"{val:.3f}", ha="center", fontsize=9)
save(fig, "validity_frontier_gap")
def longmemeval_retrieval():
data = load_json("llm_memory_validation/longmemeval_focus_report_core4/summary.json")
methods = ["dense_budgeted_bsc", "dense_rag_e5", "dense_budgeted_replay", "fifo_replay"]
colors = {
"dense_budgeted_bsc": PALETTE["oracle"],
"dense_rag_e5": PALETTE["full_raw"],
"dense_budgeted_replay": PALETTE["density"],
"fifo_replay": PALETTE["fifo"],
}
ks = [1, 3, 5]
fig, ax = plt.subplots(figsize=(6.0, 3.4))
for method in methods:
metrics = data["metrics"][method]
ys = [metrics[f"focus_recall_at_{k}"] for k in ks]
ax.plot(ks, ys, marker="o", lw=2, label=METHOD_LABELS[method], color=colors[method])
ax.set_xlabel("k")
ax.set_ylabel("Focus recall@k")
ax.set_ylim(0, 1.02)
ax.set_xticks(ks)
style_axes(ax)
ax.legend(fontsize=8, frameon=False, loc="lower right")
save(fig, "longmemeval_retrieval_rk")
def gpt55_reader_bars():
data = load_json("llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/summary.json")
methods = ["dense_budgeted_bsc", "dense_rag_e5", "dense_budgeted_replay", "fifo_replay"]
labels = [METHOD_LABELS[m] for m in methods]
metrics = [
("token_f1", "Token F1", True),
("evidence_use", "Evidence use", True),
("insufficient_evidence_rate", "Insufficient", False),
]
colors = [PALETTE["oracle"], PALETTE["full_raw"], PALETTE["density"], PALETTE["fifo"]]
fig, axes = plt.subplots(1, 3, figsize=(9.8, 3.2), sharey=True)
for ax, (key, title, _) in zip(axes, metrics):
vals = [data["metrics"][m]["focus"][key] for m in methods]
ax.bar(range(len(methods)), vals, color=colors, alpha=0.9)
ax.set_title(title, fontsize=10)
ax.set_xticks(range(len(methods)), labels, rotation=35, ha="right", fontsize=7)
ax.set_ylim(0, 1.0)
style_axes(ax)
axes[0].set_ylabel("Rate")
save(fig, "gpt55_reader_bars")
def conditional_failure_audit():
data = load_json("llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/failure_bucket_counts.json")
methods = ["dense_budgeted_bsc", "dense_rag_e5", "dense_budgeted_replay", "fifo_replay"]
buckets = [
("missing_gold_evidence", "Missing evidence", "#D55E00"),
("abstained_despite_gold", "Abstained despite support", "#E69F00"),
("scoring_mismatch_possible", "Scoring mismatch possible", "#9ECAE1"),
("used_gold_but_wrong", "Wrong extraction", "#CC79A7"),
("unsupported_answer", "Unsupported", "#7A7A7A"),
("parse_failure", "Parse", "#111111"),
]
fig, ax = plt.subplots(figsize=(7.2, 3.8))
bottoms = [0.0] * len(methods)
for bucket, label, color in buckets:
vals = []
for m in methods:
row = data["by_method"][m]
vals.append(row["outcome_counts"].get(bucket, 0) / row["n"])
ax.bar(range(len(methods)), vals, bottom=bottoms, label=label, color=color, alpha=0.9)
bottoms = [b + v for b, v in zip(bottoms, vals)]
ax.set_xticks(range(len(methods)), [METHOD_LABELS[m] for m in methods], rotation=25, ha="right", fontsize=8)
ax.set_ylabel("Share of focus questions")
ax.set_ylim(0, 1.0)
style_axes(ax)
ax.legend(fontsize=7, frameon=False, ncol=2, loc="upper right")
save(fig, "conditional_failure_audit")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--dry-run", action="store_true", help="Print inputs/outputs without generating figures.")
args = parser.parse_args()
inputs = [
"oraclemem_runs/exact_500/summary.json",
"oraclemem_runs/stress_exact_500/summary.json",
"llm_memory_validation/longmemeval_focus_report_core4/summary.json",
"llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/summary.json",
"llm_memory_validation/longmemeval_reader_api_gpt55_answer_supported_focus_full/failure_bucket_counts.json",
]
outputs = [
"pipeline_schematic",
"tombstone_timeline",
"exact_budget_sweep",
"stress_heatmap",
"validity_frontier_gap",
"longmemeval_retrieval_rk",
"gpt55_reader_bars",
"conditional_failure_audit",
]
if args.dry_run:
print("Inputs:")
for item in inputs:
print(f" {item}")
print("Outputs:")
for item in outputs:
print(f" figures/{item}.pdf")
print(f" figures/{item}.svg")
return
pipeline_schematic()
tombstone_timeline()
exact_budget_sweep()
stress_heatmap_and_gap()
longmemeval_retrieval()
gpt55_reader_bars()
conditional_failure_audit()
print(f"Wrote {len(outputs) * 2} vector figures to {FIG_DIR}")
if __name__ == "__main__":
main()
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