skills/research/research-paper-writing/references/phase5-paper-drafting.md
Goal: Write a complete, publication-ready paper.
A paper project with 50+ experiment files, multiple result directories, and extensive literature notes can easily exceed the agent's context window. Manage this proactively:
What to load into context per drafting task:
| Drafting Task | Load Into Context | Do NOT Load |
|---|---|---|
| Writing Introduction | experiment_log.md, contribution statement, 5-10 most relevant paper abstracts | Raw result JSONs, full experiment scripts, all literature notes |
| Writing Methods | Experiment configs, pseudocode, architecture description | Raw logs, results from other experiments |
| Writing Results | experiment_log.md, result summary tables, figure list | Full analysis scripts, intermediate data |
| Writing Related Work | Organized citation notes (Step 1.4 output), .bib file | Experiment files, raw PDFs |
| Revision pass | Full paper draft, specific reviewer concerns | Everything else |
Principles:
experiment_log.md is the primary context bridge — it summarizes everything needed for writing without loading raw data files (see Step 4.6)context/ directory with pre-compressed summaries:
context/
contribution.md # 1 sentence
experiment_summary.md # Key results table (from experiment_log.md)
literature_map.md # Organized citation notes
figure_inventory.md # List of figures with descriptions
The single most critical insight: Your paper is not a collection of experiments — it's a story with one clear contribution supported by evidence.
Every successful ML paper centers on what Neel Nanda calls "the narrative": a short, rigorous, evidence-based technical story with a takeaway readers care about.
Three Pillars (must be crystal clear by end of introduction):
| Pillar | Description | Test |
|---|---|---|
| The What | 1-3 specific novel claims | Can you state them in one sentence? |
| The Why | Rigorous empirical evidence | Do experiments distinguish your hypothesis from alternatives? |
| The So What | Why readers should care | Does this connect to a recognized community problem? |
If you cannot state your contribution in one sentence, you don't yet have a paper.
This skill synthesizes writing philosophy from researchers who have published extensively at top venues. The writing philosophy layer was originally compiled by Orchestra Research as the ml-paper-writing skill.
| Source | Key Contribution | Link |
|---|---|---|
| Neel Nanda (Google DeepMind) | The Narrative Principle, What/Why/So What framework | How to Write ML Papers |
| Sebastian Farquhar (DeepMind) | 5-sentence abstract formula | How to Write ML Papers |
| Gopen & Swan | 7 principles of reader expectations | Science of Scientific Writing |
| Zachary Lipton | Word choice, eliminating hedging | Heuristics for Scientific Writing |
| Jacob Steinhardt (UC Berkeley) | Precision, consistent terminology | Writing Tips |
| Ethan Perez (Anthropic) | Micro-level clarity tips | Easy Paper Writing Tips |
| Andrej Karpathy | Single contribution focus | Various lectures |
For deeper dives into any of these, see:
Spend approximately equal time on each of:
Why? Most reviewers form judgments before reaching your methods. Readers encounter your paper as: title → abstract → introduction → figures → maybe the rest.
Paper Writing Checklist:
- [ ] Step 1: Define the one-sentence contribution
- [ ] Step 2: Draft Figure 1 (core idea or most compelling result)
- [ ] Step 3: Draft abstract (5-sentence formula)
- [ ] Step 4: Draft introduction (1-1.5 pages max)
- [ ] Step 5: Draft methods
- [ ] Step 6: Draft experiments & results
- [ ] Step 7: Draft related work
- [ ] Step 8: Draft conclusion & discussion
- [ ] Step 9: Draft limitations (REQUIRED by all venues)
- [ ] Step 10: Plan appendix (proofs, extra experiments, details)
- [ ] Step 11: Complete paper checklist
- [ ] Step 12: Final review
When drafting with an AI agent, use a two-pass approach (proven effective in SakanaAI's AI-Scientist pipeline):
Pass 1 — Write + immediate refine per section: For each section, write a complete draft, then immediately refine it in the same context. This catches local issues (clarity, flow, completeness) while the section is fresh.
Pass 2 — Global refinement with full-paper context: After all sections are drafted, revisit each section with awareness of the complete paper. This catches cross-section issues: redundancy, inconsistent terminology, narrative flow, and gaps where one section promises something another doesn't deliver.
Second-pass refinement prompt (per section):
"Review the [SECTION] in the context of the complete paper.
- Does it fit with the rest of the paper? Are there redundancies with other sections?
- Is terminology consistent with Introduction and Methods?
- Can anything be cut without weakening the message?
- Does the narrative flow from the previous section and into the next?
Make minimal, targeted edits. Do not rewrite from scratch."
Append this checklist to every refinement prompt. These are the most common errors when LLMs write LaTeX:
LaTeX Quality Checklist (verify after every edit):
- [ ] No unenclosed math symbols ($ signs balanced)
- [ ] Only reference figures/tables that exist (\ref matches \label)
- [ ] No fabricated citations (\cite matches entries in .bib)
- [ ] Every \begin{env} has matching \end{env} (especially figure, table, algorithm)
- [ ] No HTML contamination (</end{figure}> instead of \end{figure})
- [ ] No unescaped underscores outside math mode (use \_ in text)
- [ ] No duplicate \label definitions
- [ ] No duplicate section headers
- [ ] Numbers in text match actual experimental results
- [ ] All figures have captions and labels
- [ ] No overly long lines that cause overfull hbox warnings
The title is the single most-read element of the paper. It determines whether anyone clicks through to the abstract.
Good titles:
Bad titles:
Rules:
From Sebastian Farquhar (DeepMind):
1. What you achieved: "We introduce...", "We prove...", "We demonstrate..."
2. Why this is hard and important
3. How you do it (with specialist keywords for discoverability)
4. What evidence you have
5. Your most remarkable number/result
Delete generic openings like "Large language models have achieved remarkable success..."
Figure 1 is the second thing most readers look at (after abstract). Draft it before writing the introduction — it forces you to clarify the core idea.
| Figure 1 Type | When to Use | Example |
|---|---|---|
| Method diagram | New architecture or pipeline | TikZ flowchart showing your system |
| Results teaser | One compelling result tells the whole story | Bar chart: "Ours vs baselines" with clear gap |
| Problem illustration | The problem is unintuitive | Before/after showing failure mode you fix |
| Conceptual diagram | Abstract contribution needs visual grounding | 2x2 matrix of method properties |
Rules: Figure 1 must be understandable without reading any text. The caption alone should communicate the core idea. Use color purposefully — don't just decorate.
Must include:
Enable reimplementation:
For each experiment, explicitly state:
Requirements:
Organize methodologically, not paper-by-paper. Cite generously — reviewers likely authored relevant papers.
All major conferences require this. Honesty helps:
Conclusion (required, 0.5-1 page):
Discussion (optional, sometimes combined with conclusion):
Do NOT introduce new results or claims in the conclusion.
Appendices are unlimited at all major venues and are essential for reproducibility. Structure:
| Appendix Section | What Goes Here |
|---|---|
| Proofs & Derivations | Full proofs too long for main text. Main text can state theorems with "proof in Appendix A." |
| Additional Experiments | Ablations, scaling curves, per-dataset breakdowns, hyperparameter sensitivity |
| Implementation Details | Full hyperparameter tables, training details, hardware specs, random seeds |
| Dataset Documentation | Data collection process, annotation guidelines, licensing, preprocessing |
| Prompts & Templates | Exact prompts used (for LLM-based methods), evaluation templates |
| Human Evaluation | Annotation interface screenshots, instructions given to annotators, IRB details |
| Additional Figures | Per-task breakdowns, trajectory visualizations, failure case examples |
Rules:
\appendix command, then \section{A: Proofs} etc.When over the page limit:
| Cut Strategy | Saves | Risk |
|---|---|---|
| Move proofs to appendix | 0.5-2 pages | Low — standard practice |
| Condense related work | 0.5-1 page | Medium — may miss key citations |
| Combine tables with subfigures | 0.25-0.5 page | Low — often improves readability |
Use \vspace{-Xpt} sparingly | 0.1-0.3 page | Low if subtle, high if obvious |
| Remove qualitative examples | 0.5-1 page | Medium — reviewers like examples |
| Reduce figure sizes | 0.25-0.5 page | High — figures must remain readable |
Do NOT: reduce font size, change margins, remove required sections (limitations, broader impact), or use \small/\footnotesize for main text.
Most venues now require or strongly encourage an ethics/broader impact statement. This is not boilerplate — reviewers read it and can flag ethics concerns that trigger desk rejection.
What to include:
| Component | Content | Required By |
|---|---|---|
| Positive societal impact | How your work benefits society | NeurIPS, ICML |
| Potential negative impact | Misuse risks, dual-use concerns, failure modes | NeurIPS, ICML |
| Fairness & bias | Does your method/data have known biases? | All venues (implicitly) |
| Environmental impact | Compute carbon footprint for large-scale training | ICML, increasingly NeurIPS |
| Privacy | Does your work use or enable processing of personal data? | ACL, NeurIPS |
| LLM disclosure | Was AI used in writing or experiments? | ICLR (mandatory), ACL |
Writing the statement:
\section*{Broader Impact Statement}
% NeurIPS/ICML: after conclusion, does not count toward page limit
% 1. Positive applications (1-2 sentences)
This work enables [specific application] which may benefit [specific group].
% 2. Risks and mitigations (1-3 sentences, be specific)
[Method/model] could potentially be misused for [specific risk]. We mitigate
this by [specific mitigation, e.g., releasing only model weights above size X,
including safety filters, documenting failure modes].
% 3. Limitations of impact claims (1 sentence)
Our evaluation is limited to [specific domain]; broader deployment would
require [specific additional work].
Common mistakes:
Compute carbon footprint (for training-heavy papers):
# Estimate using ML CO2 Impact tool methodology
gpu_hours = 1000 # total GPU hours
gpu_tdp_watts = 400 # e.g., A100 = 400W
pue = 1.1 # Power Usage Effectiveness (data center overhead)
carbon_intensity = 0.429 # kg CO2/kWh (US average; varies by region)
energy_kwh = (gpu_hours * gpu_tdp_watts * pue) / 1000
carbon_kg = energy_kwh * carbon_intensity
print(f"Energy: {energy_kwh:.0f} kWh, Carbon: {carbon_kg:.0f} kg CO2eq")
If your paper introduces a new dataset or releases a model, include structured documentation. Reviewers increasingly expect this, and NeurIPS Datasets & Benchmarks track requires it.
Datasheets for Datasets (Gebru et al., 2021) — include in appendix:
Dataset Documentation (Appendix):
- Motivation: Why was this dataset created? What task does it support?
- Composition: What are the instances? How many? What data types?
- Collection: How was data collected? What was the source?
- Preprocessing: What cleaning/filtering was applied?
- Distribution: How is the dataset distributed? Under what license?
- Maintenance: Who maintains it? How to report issues?
- Ethical considerations: Contains personal data? Consent obtained?
Potential for harm? Known biases?
Model Cards (Mitchell et al., 2019) — include in appendix for model releases:
Model Card (Appendix):
- Model details: Architecture, training data, training procedure
- Intended use: Primary use cases, out-of-scope uses
- Metrics: Evaluation metrics and results on benchmarks
- Ethical considerations: Known biases, fairness evaluations
- Limitations: Known failure modes, domains where model underperforms
Sentence-level clarity (Gopen & Swan's 7 Principles):
| Principle | Rule |
|---|---|
| Subject-verb proximity | Keep subject and verb close |
| Stress position | Place emphasis at sentence ends |
| Topic position | Put context first, new info after |
| Old before new | Familiar info → unfamiliar info |
| One unit, one function | Each paragraph makes one point |
| Action in verb | Use verbs, not nominalizations |
| Context before new | Set stage before presenting |
Word choice (Lipton, Steinhardt):
Full writing guide with examples: See references/writing-guide.md
Always copy the entire template directory first, then write within it.
Template Setup Checklist:
- [ ] Step 1: Copy entire template directory to new project
- [ ] Step 2: Verify template compiles as-is (before any changes)
- [ ] Step 3: Read the template's example content to understand structure
- [ ] Step 4: Replace example content section by section
- [ ] Step 5: Use template macros (check preamble for \newcommand definitions)
- [ ] Step 6: Clean up template artifacts only at the end
Step 1: Copy the Full Template
cp -r templates/neurips2025/ ~/papers/my-paper/
cd ~/papers/my-paper/
ls -la # Should see: main.tex, neurips.sty, Makefile, etc.
Copy the ENTIRE directory, not just the .tex file. Templates include style files (.sty), bibliography styles (.bst), example content, and Makefiles.
Step 2: Verify Template Compiles First
Before making ANY changes:
latexmk -pdf main.tex
# Or manual: pdflatex main.tex && bibtex main && pdflatex main.tex && pdflatex main.tex
If the unmodified template doesn't compile, fix that first (usually missing TeX packages — install via tlmgr install <package>).
Step 3: Keep Template Content as Reference
Don't immediately delete example content. Comment it out and use as formatting reference:
% Template example (keep for reference):
% \begin{figure}[t]
% \centering
% \includegraphics[width=0.8\linewidth]{example-image}
% \caption{Template shows caption style}
% \end{figure}
% Your actual figure:
\begin{figure}[t]
\centering
\includegraphics[width=0.8\linewidth]{your-figure.pdf}
\caption{Your caption following the same style.}
\end{figure}
Step 4: Replace Content Section by Section
Work through systematically: title/authors → abstract → introduction → methods → experiments → related work → conclusion → references → appendix. Compile after each section.
Step 5: Use Template Macros
\newcommand{\method}{YourMethodName} % Consistent method naming
\newcommand{\eg}{e.g.,\xspace} % Proper abbreviations
\newcommand{\ie}{i.e.,\xspace}
| Pitfall | Problem | Solution |
|---|---|---|
Copying only .tex file | Missing .sty, won't compile | Copy entire directory |
Modifying .sty files | Breaks conference formatting | Never edit style files |
| Adding random packages | Conflicts, breaks template | Only add if necessary |
| Deleting template content early | Lose formatting reference | Keep as comments until done |
| Not compiling frequently | Errors accumulate | Compile after each section |
| Raster PNGs for figures | Blurry in paper | Always use vector PDF via savefig('fig.pdf') |
| Conference | Main File | Style File | Page Limit |
|---|---|---|---|
| NeurIPS 2025 | main.tex | neurips.sty | 9 pages |
| ICML 2026 | example_paper.tex | icml2026.sty | 8 pages |
| ICLR 2026 | iclr2026_conference.tex | iclr2026_conference.sty | 9 pages |
| ACL 2025 | acl_latex.tex | acl.sty | 8 pages (long) |
| AAAI 2026 | aaai2026-unified-template.tex | aaai2026.sty | 7 pages |
| COLM 2025 | colm2025_conference.tex | colm2025_conference.sty | 9 pages |
Universal: Double-blind, references don't count, appendices unlimited, LaTeX required.
Templates in templates/ directory. See templates/README.md for compilation setup (VS Code, CLI, Overleaf, other IDEs).
Tables — use booktabs for professional formatting:
\usepackage{booktabs}
\begin{tabular}{lcc}
\toprule
Method & Accuracy $\uparrow$ & Latency $\downarrow$ \\
\midrule
Baseline & 85.2 & 45ms \\
\textbf{Ours} & \textbf{92.1} & 38ms \\
\bottomrule
\end{tabular}
Rules:
Figures:
plt.savefig('fig.pdf')For converting between venues, see Phase 7 (Submission Preparation) — it covers the full conversion workflow, page-change table, and post-rejection guidance.
Add these packages to any paper for professional quality. They are compatible with all major conference style files:
% --- Professional Packages (add after conference style file) ---
% Typography
\usepackage{microtype} % Microtypographic improvements (protrusion, expansion)
% Makes text noticeably more polished — always include
% Tables
\usepackage{booktabs} % Professional table rules (\toprule, \midrule, \bottomrule)
\usepackage{siunitx} % Consistent number formatting, decimal alignment
% Usage: \num{12345} → 12,345; \SI{3.5}{GHz} → 3.5 GHz
% Table alignment: S column type for decimal-aligned numbers
% Figures
\usepackage{graphicx} % Include graphics (\includegraphics)
\usepackage{subcaption} % Subfigures with (a), (b), (c) labels
% Usage: \begin{subfigure}{0.48\textwidth} ... \end{subfigure}
% Diagrams and Algorithms
\usepackage{tikz} % Programmable vector diagrams
\usetikzlibrary{arrows.meta, positioning, shapes.geometric, calc, fit, backgrounds}
\usepackage[ruled,vlined]{algorithm2e} % Professional pseudocode
% Alternative: \usepackage{algorithmicx} if template bundles it
% Cross-references
\usepackage{cleveref} % Smart references: \cref{fig:x} → "Figure 1"
% MUST be loaded AFTER hyperref
% Handles: figures, tables, sections, equations, algorithms
% Math (usually included by conference .sty, but verify)
\usepackage{amsmath,amssymb} % AMS math environments and symbols
\usepackage{mathtools} % Extends amsmath (dcases, coloneqq, etc.)
% Colors (for figures and diagrams)
\usepackage{xcolor} % Color management
% Okabe-Ito colorblind-safe palette:
\definecolor{okblue}{HTML}{0072B2}
\definecolor{okorange}{HTML}{E69F00}
\definecolor{okgreen}{HTML}{009E73}
\definecolor{okred}{HTML}{D55E00}
\definecolor{okpurple}{HTML}{CC79A7}
\definecolor{okcyan}{HTML}{56B4E9}
\definecolor{okyellow}{HTML}{F0E442}
Notes:
microtype is the single highest-impact package for visual quality. It adjusts character spacing at a sub-pixel level. Always include it.siunitx handles decimal alignment in tables via the S column type — eliminates manual spacing.cleveref must be loaded after hyperref. Most conference .sty files load hyperref, so put cleveref last.algorithm, amsmath, graphicx). Don't double-load.siunitx makes number-heavy tables significantly more readable:
\begin{tabular}{l S[table-format=2.1] S[table-format=2.1] S[table-format=2.1]}
\toprule
Method & {Accuracy $\uparrow$} & {F1 $\uparrow$} & {Latency (ms) $\downarrow$} \\
\midrule
Baseline & 85.2 & 83.7 & 45.3 \\
Ablation (no X) & 87.1 & 85.4 & 42.1 \\
\textbf{Ours} & \textbf{92.1} & \textbf{90.8} & \textbf{38.7} \\
\bottomrule
\end{tabular}
The S column type auto-aligns on the decimal point. Headers in {} escape the alignment.
Standard pattern for side-by-side figures:
\begin{figure}[t]
\centering
\begin{subfigure}[b]{0.48\textwidth}
\centering
\includegraphics[width=\textwidth]{fig_results_a.pdf}
\caption{Results on Dataset A.}
\label{fig:results-a}
\end{subfigure}
\hfill
\begin{subfigure}[b]{0.48\textwidth}
\centering
\includegraphics[width=\textwidth]{fig_results_b.pdf}
\caption{Results on Dataset B.}
\label{fig:results-b}
\end{subfigure}
\caption{Comparison of our method across two datasets. (a) shows the scaling
behavior and (b) shows the ablation results. Both use 5 random seeds.}
\label{fig:results}
\end{figure}
Use \cref{fig:results} → "Figure 1", \cref{fig:results-a} → "Figure 1a".
\begin{algorithm}[t]
\caption{Iterative Refinement with Judge Panel}
\label{alg:method}
\KwIn{Task $T$, model $M$, judges $J_1 \ldots J_n$, convergence threshold $k$}
\KwOut{Final output $A^*$}
$A \gets M(T)$ \tcp*{Initial generation}
$\text{streak} \gets 0$\;
\While{$\text{streak} < k$}{
$C \gets \text{Critic}(A, T)$ \tcp*{Identify weaknesses}
$B \gets M(T, C)$ \tcp*{Revised version addressing critique}
$AB \gets \text{Synthesize}(A, B)$ \tcp*{Merge best elements}
\ForEach{judge $J_i$}{
$\text{rank}_i \gets J_i(\text{shuffle}(A, B, AB))$ \tcp*{Blind ranking}
}
$\text{winner} \gets \text{BordaCount}(\text{ranks})$\;
\eIf{$\text{winner} = A$}{
$\text{streak} \gets \text{streak} + 1$\;
}{
$A \gets \text{winner}$; $\text{streak} \gets 0$\;
}
}
\Return{$A$}\;
\end{algorithm}
TikZ is the standard for method diagrams in ML papers. Common patterns:
Pipeline/Flow Diagram (most common in ML papers):
\begin{figure}[t]
\centering
\begin{tikzpicture}[
node distance=1.8cm,
box/.style={rectangle, draw, rounded corners, minimum height=1cm,
minimum width=2cm, align=center, font=\small},
arrow/.style={-{Stealth[length=3mm]}, thick},
]
\node[box, fill=okcyan!20] (input) {Input\\$x$};
\node[box, fill=okblue!20, right of=input] (encoder) {Encoder\\$f_\theta$};
\node[box, fill=okgreen!20, right of=encoder] (latent) {Latent\\$z$};
\node[box, fill=okorange!20, right of=latent] (decoder) {Decoder\\$g_\phi$};
\node[box, fill=okred!20, right of=decoder] (output) {Output\\$\hat{x}$};
\draw[arrow] (input) -- (encoder);
\draw[arrow] (encoder) -- (latent);
\draw[arrow] (latent) -- (decoder);
\draw[arrow] (decoder) -- (output);
\end{tikzpicture}
\caption{Architecture overview. The encoder maps input $x$ to latent
representation $z$, which the decoder reconstructs.}
\label{fig:architecture}
\end{figure}
Comparison/Matrix Diagram (for showing method variants):
\begin{tikzpicture}[
cell/.style={rectangle, draw, minimum width=2.5cm, minimum height=1cm,
align=center, font=\small},
header/.style={cell, fill=gray!20, font=\small\bfseries},
]
% Headers
\node[header] at (0, 0) {Method};
\node[header] at (3, 0) {Converges?};
\node[header] at (6, 0) {Quality?};
% Rows
\node[cell] at (0, -1) {Single Pass};
\node[cell, fill=okgreen!15] at (3, -1) {N/A};
\node[cell, fill=okorange!15] at (6, -1) {Baseline};
\node[cell] at (0, -2) {Critique+Revise};
\node[cell, fill=okred!15] at (3, -2) {No};
\node[cell, fill=okred!15] at (6, -2) {Degrades};
\node[cell] at (0, -3) {Ours};
\node[cell, fill=okgreen!15] at (3, -3) {Yes ($k$=2)};
\node[cell, fill=okgreen!15] at (6, -3) {Improves};
\end{tikzpicture}
Iterative Loop Diagram (for methods with feedback):
\begin{tikzpicture}[
node distance=2cm,
box/.style={rectangle, draw, rounded corners, minimum height=0.8cm,
minimum width=1.8cm, align=center, font=\small},
arrow/.style={-{Stealth[length=3mm]}, thick},
label/.style={font=\scriptsize, midway, above},
]
\node[box, fill=okblue!20] (gen) {Generator};
\node[box, fill=okred!20, right=2.5cm of gen] (critic) {Critic};
\node[box, fill=okgreen!20, below=1.5cm of $(gen)!0.5!(critic)$] (judge) {Judge Panel};
\draw[arrow] (gen) -- node[label] {output $A$} (critic);
\draw[arrow] (critic) -- node[label, right] {critique $C$} (judge);
\draw[arrow] (judge) -| node[label, left, pos=0.3] {winner} (gen);
\end{tikzpicture}
Essential for rebuttals — generates a marked-up PDF showing changes between versions:
# Install
# macOS: brew install latexdiff (or comes with TeX Live)
# Linux: sudo apt install latexdiff
# Generate diff
latexdiff paper_v1.tex paper_v2.tex > paper_diff.tex
pdflatex paper_diff.tex
# For multi-file projects (with \input{} or \include{})
latexdiff --flatten paper_v1.tex paper_v2.tex > paper_diff.tex
This produces a PDF with deletions in red strikethrough and additions in blue — standard format for rebuttal supplements.
Install and use for publication-quality plots:
pip install SciencePlots
import matplotlib.pyplot as plt
import scienceplots # registers styles
# Use science style (IEEE-like, clean)
with plt.style.context(['science', 'no-latex']):
fig, ax = plt.subplots(figsize=(3.5, 2.5)) # Single-column width
ax.plot(x, y, label='Ours', color='#0072B2')
ax.plot(x, y2, label='Baseline', color='#D55E00', linestyle='--')
ax.set_xlabel('Training Steps')
ax.set_ylabel('Accuracy')
ax.legend()
fig.savefig('paper/fig_results.pdf', bbox_inches='tight')
# Available styles: 'science', 'ieee', 'nature', 'science+ieee'
# Add 'no-latex' if LaTeX is not installed on the machine generating plots
Standard figure sizes (two-column format):
figsize=(3.5, 2.5) — fits in one columnfigsize=(7.0, 3.0) — spans both columnsfigsize=(3.5, 3.5) — for heatmaps, confusion matrices