Other · 其他#hype-cycle#annotated-curve#phase-coloring#leader-lines
Paper-Espresso · AI research hype-cycle (Gartner-style) chart
Paper-Espresso · AI 研究 Gartner 风格 Hype-Cycle 曲线
Reproduction of arXiv:2604.04562 Figure 8. A Gartner-style hype-cycle chart derived from 35 months of arXiv topic-proportion time series. The smooth grey curve plots Visibility vs. Maturity; topics are coloured by lifecycle phase (Innovation Trigger / Peak of Inflated Expectations / Trough of Disillusionment / Slope of Enlightenment / Plateau of Productivity). Dot size encodes total paper count; thin leader lines anchor topic labels to dots; arrow-tipped axes and italic phase names sit beneath the curve.
arXiv:2604.04562 Figure 8 复现。基于 35 个月 arXiv 话题比例时间序列绘制的 Gartner 风格 Hype Cycle：浅灰平滑曲线表示 Visibility 随 Maturity 变化，话题按生命周期阶段染色（Innovation Trigger / Peak of Inflated Expectations / Trough of Disillusionment / Slope of Enlightenment / Plateau of Productivity）。点大小编码论文总数，引线连话题文字与点，下方为带箭头坐标轴与斜体阶段名。
@paper · 来自论文
Paper Espresso: From Paper Overload to Research Insight
Paper Espresso：从论文过载到研究洞见
Du et al. (Paper Espresso Authors) · arXiv 2026
// original from paper · 论文原图
// reproduced via paperespresso_hype.py · 脚本复现download png
paperespresso_hype.py
"""Reproduction of Paper-Espresso Figure 8 (AI research hype cycle).
Anonymised reproduction of arXiv:2604.04562 Figure 8:
A Gartner-style hype-cycle plot derived from 35 months of arXiv topic-
proportion time series. The smooth grey curve represents Visibility as a
function of Maturity, with topics scattered along the curve coloured by
lifecycle phase (green / red / blue / orange / grey for Innovation Trigger,
Peak of Inflated Expectations, Trough of Disillusionment, Slope of
Enlightenment, Plateau of Productivity). Phase names are italicised
along the bottom; topic labels are pinned with thin leader lines.
Dot size is proportional to total paper count. All coordinates are
inline.
"""
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({
"font.family": "serif",
"font.size": 11,
"axes.linewidth": 1.0,
})
def hype_curve(t):
rise = 1.45 * np.exp(-((t - 1.6) ** 2) / (0.55 ** 2))
trough = -0.45 * np.exp(-((t - 3.4) ** 2) / (0.35 ** 2))
plateau = 0.55 * (1.0 / (1.0 + np.exp(-1.6 * (t - 4.6))))
base = 0.05 + 0.06 * t
return rise + trough + plateau + base
T = np.linspace(0.3, 7.4, 800)
Y = hype_curve(T)
PHASES = [
("Innovation\nTrigger", 0.30, 1.45, "#3aa353"),
("Peak of Inflated\nExpectations",1.55, 2.75, "#c93b3b"),
("Trough of\nDisillusionment", 2.85, 3.95, "#3a6dc4"),
("Slope of\nEnlightenment", 4.05, 5.55, "#e08737"),
("Plateau of\nProductivity", 5.65, 7.40, "#666666"),
]
TOPICS = [
("World Models", 0.5, -0.07, 5, "#3aa353", -0.12),
("VLA Models", 0.85, 0.04, 7, "#3aa353", 0.16),
("Agentic AI", 1.15, 0.22, 9, "#3aa353", 0.20),
("Efficient Inference", 1.45, 1.05, 11, "#c93b3b", 0.20),
("Reinforcement Learning", 1.75, 1.42, 14, "#c93b3b", 0.22),
("LLM Agents", 2.00, 1.50, 16, "#c93b3b", 0.18),
("Multi-Agent Systems", 2.30, 0.90, 5, "#c93b3b", -0.50),
("Large Language Models", 3.10, 0.45, 12, "#3a6dc4", 0.32),
("Diffusion Models", 3.30, 0.25, 11, "#3a6dc4", 0.32),
("Vision-Language Models", 3.55, -0.15, 8, "#3a6dc4", -0.28),
("Multimodal LLMs", 3.85, -0.05, 9, "#3a6dc4", -0.40),
("Knowledge Distillation", 4.55, 0.42, 7, "#e08737", -0.32),
("Code Generation", 4.85, 0.65, 8, "#e08737", 0.22),
("PEFT", 5.40, 0.97, 6, "#e08737", 0.22),
("Mech. Interpretability", 6.00, 1.05, 6, "#666666", 0.18),
("Autonomous Agents", 6.85, 1.05, 6, "#666666", -0.28),
]
fig, ax = plt.subplots(figsize=(11.5, 5.6))
ax.plot(T, Y, color="#cdcdcd", linewidth=4.5, solid_capstyle="round", zorder=1)
ax.axvspan(5.65, 7.40, ymin=0.0, ymax=1.0, color="#f5f3ee", alpha=0.55, zorder=0)
for name, x0, x1, color in PHASES:
if x1 > 7.4:
x1 = 7.4
cx = (x0 + x1) / 2
ax.text(cx, -0.62, name, ha="center", va="top",
fontsize=10.0, style="italic", color=color, linespacing=1.05)
ax.axvline(x1 + 0.05, ymax=0.95, color="#dddddd", linestyle=":", linewidth=0.8)
for label, tx, dy, size, col, label_dy in TOPICS:
yc = hype_curve(np.array([tx]))[0]
ax.scatter([tx], [yc], s=20 * size, color=col,
edgecolor=col, linewidth=0.0, zorder=4, alpha=0.95)
ly = yc + label_dy
if label_dy > 0:
ax.plot([tx, tx], [yc + 0.02, ly - 0.04], color=col,
linewidth=0.8, zorder=3)
else:
ax.plot([tx, tx], [yc - 0.02, ly + 0.04], color=col,
linewidth=0.8, zorder=3)
ax.text(tx, ly, label, ha="center",
va="bottom" if label_dy > 0 else "top",
fontsize=10.0, fontweight="bold", color=col)
ax.annotate("", xy=(7.4, -0.30), xytext=(0.3, -0.30),
arrowprops=dict(arrowstyle="->", color="#222", lw=1.0),
zorder=2)
ax.text(7.45, -0.30, "Maturity", ha="left", va="center",
fontsize=11, color="#222")
ax.annotate("", xy=(0.3, 1.95), xytext=(0.3, -0.30),
arrowprops=dict(arrowstyle="->", color="#222", lw=1.0),
zorder=2)
ax.text(0.18, 0.85, "Visibility", ha="center", va="center",
fontsize=11.5, color="#222", rotation=90)
ax.set_xlim(0.0, 7.85)
ax.set_ylim(-0.95, 2.05)
for s in ["top", "right", "bottom", "left"]:
ax.spines[s].set_visible(False)
ax.set_xticks([])
ax.set_yticks([])
plt.tight_layout()
plt.savefig("paperespresso_hype_repro.png", dpi=200, bbox_inches="tight")
print("saved paperespresso_hype_repro.png")