AOS/skills/literature-survey/references/02-survey-figures.md

Survey Figures Playbook

Why this exists

A research paper's figures tend to be experimental: tables of numbers, training curves, confusion matrices. A survey's figures are organisational: how is the field structured, when did things happen, who works on what. They are how a reader navigates the survey. Without good figures a survey is a long list of references; with them it becomes a map.

Hard targets: 6–10 figures spanning these families:

• At least 1 taxonomy / classification diagram (TikZ tree or hierarchy)
• At least 1 chronological timeline of major works
• At least 1 area / capability matrix (coverage heatmap)
• 1–2 representative architecture / mechanism diagrams (TikZ)
• 1–2 quantitative trend plots (matplotlib publication style)
• Optional: paradigm comparison, citation-co-occurrence map, geographic / institutional map

Design figures for THIS research — aim at the Nature/Science bar

Two real failures this playbook used to cause. Read both before you draw anything.

1. Template leakage (the worst over-fitting). The worked examples below were written about time-series Transformers. A real "open-source LLM" survey produced from this playbook shipped a taxonomy whose leaves were Informer, Autoformer, PatchTST, iTransformer, Moirai — time-series forecasting models, completely off-topic — because the agent copied the sample's content, not just its shape. Every code block here is a worked example of a principle, never content to reuse. Replace 100% of the labels, branches, palette, and composition with what your topic and your gathered papers actually require. If a reader could guess which sample you started from, you over-templated. The figure should look like it was designed for this survey and no other.

2. Generic look. Default LaTeX/matplotlib output (Computer Modern serif, boxed axes, rainbow colours, one chart per figure) reads as a class assignment, not a journal. The target is Nature / Science: figures that are designed, not decorated.

What "Nature/Science" concretely means (not vibes)

• A figure is a composition, not a lone chart. Top journals compose multi-panel figures (a, b, c …), each panel one sub-claim, together telling one story. Use subfigure / matplotlib subplots, with bold lowercase panel labels in the top-left corner of each panel.
• Sans-serif type. Helvetica/Arial (both installed here), ~7 pt floor — never Computer Modern serif. matplotlib: font.family:"sans-serif". TikZ/LaTeX diagrams: \usepackage[scaled]{helvet}\renewcommand{\familydefault}{\sfdefault} (or set the figure's nodes to \sffamily).
• Restrained, colour-blind-safe colour that encodes something. Use the Wong palette (the one Nature recommends): #000000 #E69F00 #56B4E9 #009E73 #F0E442 #0072B2 #D55E00 #CC79A7. For ordinal data use a single-hue sequential map (Blues), never rainbow/jet. ≤ ~5 hues per figure; colour must mean something, not decorate.
• Maximise data-ink. Despine top + right; ticks short and outward; axis lines ~0.6 pt; no gridlines (or very faint); direct-label lines instead of a legend box where possible. Banned: 3-D, pie charts, drop shadows, gradients, raster screenshots.
• Exact sizing. Nature column width = 89 mm single (≈ 3.5 in), 183 mm double (≈ 7.2 in). Set matplotlib figsize to these in inches; in this single-column A4 template include at width=\linewidth.
• The caption states a finding. Bold "Figure N." + one declarative sentence giving the takeaway, then detail. Not "Taxonomy of methods." but "Open-source LLM research splits into four lineages that diverge after the 2024 MoE shift."

Nature-style matplotlib preamble — use this everywhere instead of the old serif block

import matplotlib as mpl
WONG = ["#000000","#E69F00","#56B4E9","#009E73","#F0E442","#0072B2","#D55E00","#CC79A7"]
mpl.rcParams.update({
    "font.family":"sans-serif", "font.sans-serif":["Helvetica","Arial","DejaVu Sans"],
    "font.size":7, "axes.labelsize":7, "axes.titlesize":7,
    "xtick.labelsize":6.5, "ytick.labelsize":6.5, "legend.fontsize":6.5,
    "axes.linewidth":0.6, "axes.spines.top":False, "axes.spines.right":False,
    "xtick.direction":"out", "ytick.direction":"out",
    "xtick.major.size":2.5, "ytick.major.size":2.5,
    "xtick.major.width":0.6, "ytick.major.width":0.6,
    "axes.grid":False, "lines.linewidth":1.2, "lines.markersize":3.5,
    "savefig.dpi":400, "savefig.bbox":"tight", "pdf.fonttype":42, "ps.fonttype":42,
    "axes.prop_cycle": mpl.cycler(color=WONG[1:]),
})
# figsize: single column = (89/25.4, h); double column / 2-panel = (183/25.4, h)

The design process — this is where flexibility lives

Templates are rigid because they answer "what figure exists?" Design answers "what does this survey need to show?" For each figure, decide from scratch:

1. What claim in this section must the reader see to believe it? No claim → no figure (cut it; decoration fails the Nature bar).
2. What is the shape of that evidence? hierarchy → tree (forest); change over time → timeline / line; coverage or comparison → matrix / small-multiples; mechanism → schematic; distribution → strip/box. Let the data shape pick the form — don't force every survey into taxonomy+timeline+matrix.
3. What is the most reductive form that still makes the point? One well-composed panel beats a busy one; merge weak figures, split overloaded ones.
4. Compose with the Nature language above, then apply the mechanical floor below (vector, forest/\resizebox, zero overflow/overlap).

Steps 1–3 change with every topic — that is the flexible craft. The mechanical floor (next section) never changes.

Fitting & overflow — the mechanical floor (read BEFORE drawing any figure)

Most "ugly survey PDF" problems are not bad taste, they are figures that overrun the column or overlap themselves because the source used hand-tuned absolute sizes (sibling distance=42mm, figsize=(7,…)) that happen not to fit. The fix is to never hand-tune for fit — let the figure adapt to the available width.

Know your column width first. This template is single-column article on A4 with 1-inch margins → text width ≈ 159 mm (≈ 6.2 in). (\documentclass{article} is single-column; figure* does not widen anything here — it only spans columns in a genuinely two-column class such as IEEEtran. Don't reach for figure* to "get more room"; you don't have a second column.)

There are two different failure modes, and they need two different fixes. Confusing them is why the old taxonomy sample both ran off the page and overlapped.

Failure A — figure wider than the column (runs off the right margin)

Fix: wrap the picture so its rendered width is clamped.

• TikZ: wrap in \resizebox{\linewidth}{!}{ … }.
• Includes: always \includegraphics[width=\linewidth]{…} (or 0.85–0.95\linewidth), never a bare \includegraphics{file}. matplotlib figsize then only sets aspect ratio; keep its width ≤ 6.2 in so in-figure fonts aren't double-shrunk.

\begin{figure}[!t]
  \centering
  \resizebox{\linewidth}{!}{%
    \begin{tikzpicture}[ ... ] ... \end{tikzpicture}%
  }
  \caption{...}\label{fig:...}
\end{figure}

Failure B — nodes overlapping each other (the real "ugly" problem)

\resizebox does NOT fix overlap. It scales the whole picture uniformly, so two boxes that overlap at natural size still overlap after shrinking — you just get a smaller tangle. Overlap comes from hand-picked spacing (sibling distance=42mm) where adjacent sub-trees are wider than the gap between them. The fixes, in order of preference:

1. Use a layout engine that auto-spaces — \usepackage{forest}. Forest computes sibling positions so children never collide, for any branch/leaf count. This is the default for taxonomies now (see Family 1). It removes the whole class of "magic mm that happens to overlap".
2. Pick orientation by size. Few leaves (≲ 12) → top-down reads well. Many leaves → grow the tree horizontally (grow'=east) so leaves stack down the page (vertical space is plentiful, horizontal isn't).
3. Fold. If one figure still can't hold it legibly, split a branch into its own sub-figure.

Then apply Failure-A's \resizebox on top as a width backstop. Order matters: forest (or real spacing) removes overlap; resizebox only then trims width.

The quality gate (05-quality-gate.md G1 + S5) treats residual Overfull \hbox as a remediation loop, and reminds you that a figure shrunk to illegibility is not a pass — fold it instead.

File layout

All figures land under output/literature-survey/<slug>/survey_paper/figures/. Use vector PDF for LaTeX (.pdf); keep a PNG sibling only for previewing. TikZ figures live inline inside the relevant sections/*.tex (no separate file needed). matplotlib / seaborn figures get a make_fig_NN_<slug>.py script alongside the PDF.

Naming: fig_<NN>_<short-slug>.pdf. Numbers correspond to citation order.

Family 1 — Taxonomy / classification diagrams (TikZ)

The single most important survey figure. A taxonomy figure says: here is how I cut the field. Readers will refer back to it constantly.

Setup

In survey_paper/main.tex:

\usepackage{tikz}
\usetikzlibrary{positioning, shapes.geometric, arrows.meta, fit, backgrounds, calc, trees}
\usepackage{forest}   % auto-spaced trees — taxonomies use this, NOT hand-built child{} trees

Tree-style taxonomy — use forest, not hand-tuned child{} distances

Do not build the taxonomy from a tikzpicture with level/.style={sibling distance=42mm} and nested child{} (the old sample here did exactly that). With more than ~3 leaves per branch, adjacent sub-trees are wider than the fixed gap and the leaves overlap — and because \resizebox scales the overlap too, you can't shrink your way out of it. forest computes sibling spacing automatically, so children never collide regardless of count.

Flat, sans-serif, journal-style top-down (default; good up to ~12 leaves). Define two colours once in main.tex (\definecolor{nblue}{HTML}{0072B2} and \definecolor{nfill}{HTML}{EAF2FB}, \definecolor{ngrey}{HTML}{4D4D4D}); thin uniform grey strokes + one accent + sans labels is the Nature diagram look — not the heavy blue/orange boxes the old sample used. Replace every label with your own taxonomy.

\begin{figure}[!t]
  \centering
  \resizebox{\linewidth}{!}{%
  \begin{forest}
    for tree={font=\sffamily\small, draw=ngrey, line width=0.4pt, rounded corners=1pt,
              inner sep=3pt, align=center, edge={draw=ngrey, line width=0.4pt},
              l sep=7mm, s sep=3mm, parent anchor=south, child anchor=north}
    [Open-source LLMs, draw=nblue, line width=0.7pt, text=nblue
      [Architecture, fill=nfill, draw=nblue [Llama 3][Mistral][Qwen2]]
      [Sparse / MoE, fill=nfill, draw=nblue [DeepSeek][Mixtral][OLMoE]]
      [Alignment,    fill=nfill, draw=nblue [DPO][ORPO][KTO]]
      [Efficiency,   fill=nfill, draw=nblue [GPTQ][AWQ][QLoRA]]
    ]
  \end{forest}%
  }
  \caption{\textbf{Figure 1.} Open-source LLM research organises into four lineages. Each leaf cites its primary source; full coverage in Section~\ref{sec:methods}.}
  \label{fig:taxonomy}
\end{figure}

(\sffamily gives Helvetica when main.tex loads \usepackage[scaled]{helvet} — which the template now does — while body text stays serif, the standard journal split.)

Horizontal / folder (use when there are many leaves — leaves stack down the page where there is room): add grow'=east, parent anchor=east, child anchor=west, anchor=west to the for tree={…} options and switch align=center→align=left. This keeps labels full-size instead of shrinking them to fit one row. (Verified: a 5-branch / 20-leaf tree renders with zero overlap and zero overfull in both orientations; the same content as a hand-built child{} tree overflowed the page by 270 pt and merged half its leaves.)

Layered / hierarchy diagram (when tree doesn't fit)

When the field has a layered structure (e.g., infrastructure → models → applications), use horizontal layers with \node[fit=...] to draw boundary boxes.

Taste

• Color: one family per branch; orange for the row that contains your contribution if you have one.
• Leaves: 8–15 reads best top-down. With forest there is no hard cap (it won't overlap), but past ~15 switch to the horizontal orientation so labels stay full-size rather than shrinking; past ~24 fold into sub-figures.
• Always cite each leaf in the prose; the leaf names alone are not the survey, the citations are.

Family 2 — Timeline of major works (matplotlib)

A survey timeline shows how the field evolved. Place key papers on a horizontal time axis with publication-year ticks.

"""Generate fig_02_timeline.pdf — chronology of major works."""
import os
import matplotlib.pyplot as plt

# Apply the Nature-style preamble from the top of this reference (sans-serif,
# Wong palette, despined, thin axes). Do NOT use font.family:"serif".

events = [
    # (year, lane, label, color)
    (2017, 0, "Transformer\n(Vaswani)", "#1f77b4"),
    (2019, 0, "LogSparse",              "#1f77b4"),
    (2020, 0, "Reformer",                "#1f77b4"),
    (2021, 1, "Informer\nAutoformer",   "#ff7f0e"),
    (2022, 1, "FEDformer\nPyraformer",  "#ff7f0e"),
    (2022, 2, "RevIN",                   "#2ca02c"),
    (2023, 1, "PatchTST\nDLinear",       "#ff7f0e"),
    (2023, 3, "TimesNet\nTSMixer",       "#9467bd"),
    (2024, 1, "iTransformer\nTimeMixer", "#ff7f0e"),
    (2024, 4, "MOMENT, Moirai\nTimeGPT, Chronos", "#d62728"),
]

LANE_LABELS = {
    0: "Foundations",
    1: "Time-domain Trans.",
    2: "Normalisation",
    3: "MLP / 2D",
    4: "Foundation models",
}

fig, ax = plt.subplots(figsize=(6.0, 3.4))   # width ≤ column (6.2in); include with width=\linewidth
years = [e[0] for e in events]
ax.set_xlim(min(years) - 0.5, max(years) + 0.5)
ax.set_ylim(-0.7, max(LANE_LABELS) + 0.7)

for year, lane, label, color in events:
    ax.scatter([year], [lane], s=120, color=color, edgecolor="black", linewidth=0.5, zorder=3)
    ax.annotate(label, (year, lane), xytext=(0, 12), textcoords="offset points",
                ha="center", fontsize=7.5, color="black")

ax.set_yticks(list(LANE_LABELS.keys()))
ax.set_yticklabels([LANE_LABELS[k] for k in sorted(LANE_LABELS)])
ax.set_xlabel("Year")
ax.grid(True, axis="x", alpha=0.25)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)

plt.tight_layout(pad=0.4)
out = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fig_02_timeline.pdf")
plt.savefig(out)
plt.close(fig)
print(f"Wrote {out}")

Taste

• Lanes carve the timeline into sub-areas (rows of the figure) — the same sub-areas as the taxonomy (Fig~\ref{fig:taxonomy}). Consistency helps readers connect the two.
• Annotate ~10–15 key works; more is clutter. Pick the works that defined the year, not every paper.
• Color matches taxonomy color so the reader can cross-reference.

Family 3 — Coverage matrix / capability heatmap (seaborn)

A survey often makes claims like "method X covers capabilities A, B but not C". A binary or three-level matrix makes that scannable.

import os
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

methods = ["DLinear", "Informer", "Autoformer", "FEDformer",
           "PatchTST", "iTransformer", "TimeMixer",
           "MOMENT", "Moirai", "Chronos"]
caps = ["Long horizon", "Multivariate", "Probabilistic",
        "Zero-shot", "Efficient", "Interpretable"]

# 0 = no, 1 = partial, 2 = yes (illustrative — derive from your survey reading)
cov = np.array([
    [2, 1, 0, 0, 2, 1],   # DLinear
    [2, 1, 1, 0, 1, 0],   # Informer
    [2, 2, 1, 0, 1, 1],   # Autoformer
    [2, 2, 1, 0, 1, 1],   # FEDformer
    [2, 2, 0, 0, 2, 1],   # PatchTST
    [2, 2, 0, 0, 2, 0],   # iTransformer
    [2, 2, 0, 0, 2, 0],   # TimeMixer
    [2, 2, 1, 2, 1, 0],   # MOMENT
    [2, 2, 2, 2, 1, 0],   # Moirai
    [2, 2, 2, 2, 1, 0],   # Chronos
])

# Apply the Nature-style preamble from the top of this reference (sans-serif, despined).

fig, ax = plt.subplots(figsize=(5.6, 3.6))
sns.heatmap(cov, annot=False, cmap="Blues", vmin=0, vmax=2,
            xticklabels=caps, yticklabels=methods,
            linewidths=0.4, linecolor="white",
            cbar_kws={"label": "Coverage", "ticks": [0, 1, 2], "shrink": 0.6},
            ax=ax)
ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha="right")
ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
plt.tight_layout(pad=0.4)
plt.savefig("fig_03_capability_matrix.pdf")
plt.close(fig)

Taste

• 0/1/2 (no/partial/yes) is more readable than continuous numbers when the underlying judgement is qualitative.
• Annotate cells only if there are ≤ 60.
• Cmap: greens for "more is better" (coverage); avoid traffic-light reds in surveys.

Family 4 — Architecture / mechanism diagrams (TikZ)

When a sub-area shares a common architectural pattern (e.g., "all decomposition Transformers do X then Y"), a single TikZ figure illustrating the canonical pipeline saves several pages of prose. See the paper-writer skill's references/02-figures-publication-grade.md § Family 1 for the full template.

For a survey, prefer abstracted architectural diagrams that show the pattern shared by a family of methods, not one specific method.

Family 5 — Quantitative trends (matplotlib)

Examples that work in a survey:

• Number of papers per year (publication trend)
• Reported MSE on a single benchmark over time (the SOTA curve)
• Compute requirements over time (size/cost trend)

These earn their place when they reveal something the prose can't. A "papers per year" plot that just goes up linearly is decoration; if it reveals a plateau, an inflection, or a divergence between sub-areas, it's content.

Family 6 — Optional advanced

• Citation co-occurrence network: dot/network plot showing which papers cite each other (use NetworkX → matplotlib). Powerful when the field has clear clusters.
• Geographic / institutional map: world map with author affiliations marked. Niche but illuminating for cross-cultural fields.
• Paradigm comparison: side-by-side architectural sketches (multi-panel TikZ).

No default figures

This skill has no default figure (unlike experiment-suite). All figures are agent-generated.

Quick checklist

• Designed for THIS topic — zero labels/leaves/data carried over from the worked examples (no Informer/PatchTST/time-series content in a non-time-series survey)
• Each figure earns its place by making a specific claim visible (design process step 1)
• Nature/Science look: sans-serif labels, Wong palette / single-hue sequential, despined axes, direct labels, multi-panel with a/b where it tells one story
• matplotlib uses the sans-serif Nature preamble (not font.family:"serif"); figsize at 89 mm / 183 mm
• Caption leads with a one-sentence finding, not a noun phrase
• Taxonomy built with forest (auto-spaced), NOT hand-tuned sibling distance + child{} — no overlapping leaves
• Every TikZ/forest figure wrapped in \resizebox{\linewidth}{!}{…} as a width backstop (after spacing is correct, not instead of it)
• Every \includegraphics has width=…\linewidth; matplotlib figsize width ≤ 6.2 in
• No figure* used to "gain width" (single-column template); many-leaf trees go horizontal / fold, not squashed
• At least 1 taxonomy diagram; cited from §Methods or §Background
• At least 1 timeline; cited from §Introduction
• At least 1 capability / coverage matrix; cited from §Discussion
• 6–10 figures total
• All figure files are vector PDF; fonts embedded (pdf.fonttype=42)
• Each figure referenced in prose with \ref{fig:...}
• Each caption stands alone (a reader who only sees the figure understands it)
• No 3-D bars, no pie charts, no rainbow palettes, no raster screenshots