Research Paper Writing Pipeline

End-to-end pipeline for producing publication-ready ML/AI research papers targeting NeurIPS, ICML, ICLR, ACL, AAAI, and COLM. This skill covers the full research lifecycle: experiment design, execution, monitoring, analysis, paper writing, review, revision, and submission.

This is not a linear pipeline — it is an iterative loop. Results trigger new experiments. Reviews trigger new analysis. The agent must handle these feedback loops.

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┌─────────────────────────────────────────────────────────────┐
│                    RESEARCH PAPER PIPELINE                  │
│                                                             │
│  Phase 0: Project Setup ──► Phase 1: Literature Review      │
│       │                          │                          │
│       ▼                          ▼                          │
│  Phase 2: Experiment     Phase 5: Paper Drafting ◄──┐      │
│       Design                     │                   │      │
│       │                          ▼                   │      │
│       ▼                    Phase 6: Self-Review      │      │
│  Phase 3: Execution &           & Revision ──────────┘      │
│       Monitoring                 │                          │
│       │                          ▼                          │
│       ▼                    Phase 7: Submission               │
│  Phase 4: Analysis ─────► (feeds back to Phase 2 or 5)     │
│                                                             │
└─────────────────────────────────────────────────────────────┘

<!-- ascii-guard-ignore-end -->

---

When To Use This Skill

Use this skill when:

• Starting a new research paper from an existing codebase or idea
• Designing and running experiments to support paper claims
• Writing or revising any section of a research paper
• Preparing for submission to a specific conference or workshop
• Responding to reviews with additional experiments or revisions
• Converting a paper between conference formats
• Writing non-empirical papers — theory, survey, benchmark, or position papers (see Paper Types Beyond Empirical ML)
• Designing human evaluations for NLP, HCI, or alignment research
• Preparing post-acceptance deliverables — posters, talks, code releases

Core Philosophy

1. Be proactive. Deliver complete drafts, not questions. Scientists are busy — produce something concrete they can react to, then iterate.
2. Never hallucinate citations. AI-generated citations have ~40% error rate. Always fetch programmatically. Mark unverifiable citations as [CITATION NEEDED].
3. Paper is a story, not a collection of experiments. Every paper needs one clear contribution stated in a single sentence. If you can't do that, the paper isn't ready.
4. Experiments serve claims. Every experiment must explicitly state which claim it supports. Never run experiments that don't connect to the paper's narrative.
5. Commit early, commit often. Every completed experiment batch, every paper draft update — commit with descriptive messages. Git log is the experiment history.

Proactivity and Collaboration

Default: Be proactive. Draft first, ask with the draft.

Confidence Level	Action
High (clear repo, obvious contribution)	Write full draft, deliver, iterate on feedback
Medium (some ambiguity)	Write draft with flagged uncertainties, continue
Low (major unknowns)	Ask 1-2 targeted questions via clarify, then draft

Section	Draft Autonomously?	Flag With Draft
Abstract	Yes	"Framed contribution as X — adjust if needed"
Introduction	Yes	"Emphasized problem Y — correct if wrong"
Methods	Yes	"Included details A, B, C — add missing pieces"
Experiments	Yes	"Highlighted results 1, 2, 3 — reorder if needed"
Related Work	Yes	"Cited papers X, Y, Z — add any I missed"

Block for input only when: target venue unclear, multiple contradictory framings, results seem incomplete, explicit request to review first.

---

Phase 0: Project Setup

Goal: Establish the workspace, understand existing work, identify the contribution.

Step 0.1: Explore the Repository

# Understand project structure
ls -la
find . -name "*.py" | head -30
find . -name "*.md" -o -name "*.txt" | xargs grep -l -i "result\|conclusion\|finding"

Look for:

• README.md — project overview and claims
• results/, outputs/, experiments/ — existing findings
• configs/ — experimental settings
• .bib files — existing citations
• Draft documents or notes

Step 0.2: Organize the Workspace

Establish a consistent workspace structure:

workspace/
  paper/               # LaTeX source, figures, compiled PDFs
  experiments/         # Experiment runner scripts
  code/                # Core method implementation
  results/             # Raw experiment results (auto-generated)
  tasks/               # Task/benchmark definitions
  human_eval/          # Human evaluation materials (if needed)

Step 0.3: Set Up Version Control

git init  # if not already
git remote add origin <repo-url>
git checkout -b paper-draft  # or main

Git discipline: Every completed experiment batch gets committed with a descriptive message. Example:

Add Monte Carlo constrained results (5 runs, Sonnet 4.6, policy memo task)
Add Haiku baseline comparison: autoreason vs refinement baselines at cheap model tier

Step 0.4: Identify the Contribution

Before writing anything, articulate:

• The What: What is the single thing this paper contributes?
• The Why: What evidence supports it?
• The So What: Why should readers care?

Propose to the scientist: "Based on my understanding, the main contribution is: [one sentence]. The key results show [Y]. Is this the framing you want?"

Step 0.5: Create a TODO List

Use the todo tool to create a structured project plan:

Research Paper TODO:
- [ ] Define one-sentence contribution
- [ ] Literature review (related work + baselines)
- [ ] Design core experiments
- [ ] Run experiments
- [ ] Analyze results
- [ ] Write first draft
- [ ] Self-review (simulate reviewers)
- [ ] Revise based on review
- [ ] Submission prep

Update this throughout the project. It serves as the persistent state across sessions.

Step 0.6: Estimate Compute Budget

Before running experiments, estimate total cost and time:

Compute Budget Checklist:
- [ ] API costs: (model price per token) × (estimated tokens per run) × (number of runs)
- [ ] GPU hours: (time per experiment) × (number of experiments) × (number of seeds)
- [ ] Human evaluation costs: (annotators) × (hours) × (hourly rate)
- [ ] Total budget ceiling and contingency (add 30-50% for reruns)

Track actual spend as experiments run:

# Simple cost tracker pattern
import json, os
from datetime import datetime

COST_LOG = "results/cost_log.jsonl"

def log_cost(experiment: str, model: str, input_tokens: int, output_tokens: int, cost_usd: float):
    entry = {
        "timestamp": datetime.now().isoformat(),
        "experiment": experiment,
        "model": model,
        "input_tokens": input_tokens,
        "output_tokens": output_tokens,
        "cost_usd": cost_usd,
    }
    with open(COST_LOG, "a") as f:
        f.write(json.dumps(entry) + "\n")

When budget is tight: Run pilot experiments (1-2 seeds, subset of tasks) before committing to full sweeps. Use cheaper models for debugging pipelines, then switch to target models for final runs.

Step 0.7: Multi-Author Coordination

Most papers have 3-10 authors. Establish workflows early:

Workflow	Tool	When to Use
Overleaf	Browser-based	Multiple authors editing simultaneously, no git experience
Git + LaTeX	git with .gitignore for aux files	Technical teams, need branch-based review
Overleaf + Git sync	Overleaf premium	Best of both — live collab with version history

Section ownership: Assign each section to one primary author. Others comment but don't edit directly. Prevents merge conflicts and style inconsistency.

Author Coordination Checklist:
- [ ] Agree on section ownership (who writes what)
- [ ] Set up shared workspace (Overleaf or git repo)
- [ ] Establish notation conventions (before anyone writes)
- [ ] Schedule internal review rounds (not just at the end)
- [ ] Designate one person for final formatting pass
- [ ] Agree on figure style (colors, fonts, sizes) before creating figures

LaTeX conventions to agree on early:

• \method{} macro for consistent method naming
• Citation style: \citet{} vs \citep{} usage
• Math notation: lowercase bold for vectors, uppercase bold for matrices, etc.
• British vs American spelling

---

Phase 1: Literature Review

Goal: Find related work, identify baselines, gather citations.

Step 1.1: Identify Seed Papers

Start from papers already referenced in the codebase:

# Via terminal:
grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
find . -name "*.bib"

Step 1.2: Search for Related Work

Load the arxiv skill for structured paper discovery: skill_view("arxiv"). It provides arXiv REST API search, Semantic Scholar citation graphs, author profiles, and BibTeX generation.

Use web_search for broad discovery, web_extract for fetching specific papers:

# Via web_search:
web_search("[main technique] + [application domain] site:arxiv.org")
web_search("[baseline method] comparison ICML NeurIPS 2024")

# Via web_extract (for specific papers):
web_extract("https://arxiv.org/abs/2303.17651")

Additional search queries to try:

Search queries:
- "[main technique] + [application domain]"
- "[baseline method] comparison"
- "[problem name] state-of-the-art"
- Author names from existing citations

Recommended: Install Exa MCP for real-time academic search:

claude mcp add exa -- npx -y mcp-remote "https://mcp.exa.ai/mcp"

Step 1.2b: Deepen the Search (Breadth-First, Then Depth)

A flat search (one round of queries) typically misses important related work. Use an iterative breadth-then-depth pattern inspired by deep research pipelines:

Iterative Literature Search:

Round 1 (Breadth): 4-6 parallel queries covering different angles
  - "[method] + [domain]"
  - "[problem name] state-of-the-art 2024 2025"
  - "[baseline method] comparison"
  - "[alternative approach] vs [your approach]"
  → Collect papers, extract key concepts and terminology

Round 2 (Depth): Generate follow-up queries from Round 1 learnings
  - New terminology discovered in Round 1 papers
  - Papers cited by the most relevant Round 1 results
  - Contradictory findings that need investigation
  → Collect papers, identify remaining gaps

Round 3 (Targeted): Fill specific gaps
  - Missing baselines identified in Rounds 1-2
  - Concurrent work (last 6 months, same problem)
  - Key negative results or failed approaches
  → Stop when new queries return mostly papers you've already seen

When to stop: If a round returns >80% papers already in your collection, the search is saturated. Typically 2-3 rounds suffice. For survey papers, expect 4-5 rounds.

For agent-based workflows: Delegate each round's queries in parallel via delegate_task. Collect results, deduplicate, then generate the next round's queries from the combined learnings.

Step 1.3: Verify Every Citation

NEVER generate BibTeX from memory. ALWAYS fetch programmatically.

For each citation, follow the mandatory 5-step process:

Citation Verification (MANDATORY per citation):
1. SEARCH → Query Semantic Scholar or Exa MCP with specific keywords
2. VERIFY → Confirm paper exists in 2+ sources (Semantic Scholar + arXiv/CrossRef)
3. RETRIEVE → Get BibTeX via DOI content negotiation (programmatically, not from memory)
4. VALIDATE → Confirm the claim you're citing actually appears in the paper
5. ADD → Add verified BibTeX to bibliography
If ANY step fails → mark as [CITATION NEEDED], inform scientist

# Fetch BibTeX via DOI
import requests

def doi_to_bibtex(doi: str) -> str:
    response = requests.get(
        f"https://doi.org/{doi}",
        headers={"Accept": "application/x-bibtex"}
    )
    response.raise_for_status()
    return response.text

If you cannot verify a citation:

\cite{PLACEHOLDER_author2024_verify_this}  % TODO: Verify this citation exists

Always tell the scientist: "I've marked [X] citations as placeholders that need verification."

See references/citation-workflow.md for complete API documentation and the full CitationManager class.

Step 1.4: Organize Related Work

Group papers by methodology, not paper-by-paper:

Good: "One line of work uses X's assumption [refs] whereas we use Y's assumption because..." Bad: "Smith et al. introduced X. Jones et al. introduced Y. We combine both."

---

Phase 2: Experiment Design

Goal: Design experiments that directly support paper claims. Every experiment must answer a specific question.

Step 2.1: Map Claims to Experiments

Create an explicit mapping:

Claim	Experiment	Expected Evidence
"Our method outperforms baselines"	Main comparison (Table 1)	Win rate, statistical significance
"Effect is larger for weaker models"	Model scaling study	Monotonic improvement curve
"Convergence requires scope constraints"	Constrained vs unconstrained	Convergence rate comparison

Rule: If an experiment doesn't map to a claim, don't run it.

Step 2.2: Design Baselines

Strong baselines are what separates accepted papers from rejected ones. Reviewers will ask: "Did they compare against X?"

Standard baseline categories:

• Naive baseline: Simplest possible approach
• Strong baseline: Best known existing method
• Ablation baselines: Your method minus one component
• Compute-matched baselines: Same compute budget, different allocation

Step 2.3: Define Evaluation Protocol

Before running anything, specify:

• Metrics: What you're measuring, direction symbols (higher/lower better)
• Aggregation: How results are combined across runs/tasks
• Statistical tests: What tests will establish significance
• Sample sizes: How many runs/problems/tasks

Step 2.4: Write Experiment Scripts

Follow these patterns from successful research pipelines:

Incremental saving — save results after each step for crash recovery:

# Save after each problem/task
result_path = f"results/{task}/{strategy}/result.json"
if os.path.exists(result_path):
    continue  # Skip already-completed work
# ... run experiment ...
with open(result_path, 'w') as f:
    json.dump(result, f, indent=2)

Artifact preservation — save all intermediate outputs:

results/<experiment>/
  <task>/
    <strategy>/
      final_output.md          # Final result
      history.json             # Full trajectory
      pass_01/                 # Per-iteration artifacts
        version_a.md
        version_b.md
        critic.md

Separation of concerns — keep generation, evaluation, and visualization separate:

run_experiment.py              # Core experiment runner
run_baselines.py               # Baseline comparison
run_comparison_judge.py        # Blind evaluation
analyze_results.py             # Statistical analysis
make_charts.py                 # Visualization

See references/experiment-patterns.md for complete design patterns, cron monitoring, and error recovery.

Step 2.5: Design Human Evaluation (If Applicable)

Many NLP, HCI, and alignment papers require human evaluation as primary or complementary evidence. Design this before running automated experiments — human eval often has longer lead times (IRB approval, annotator recruitment).

When human evaluation is needed:

• Automated metrics don't capture what you care about (fluency, helpfulness, safety)
• Your contribution is about human-facing qualities (readability, preference, trust)
• Reviewers at NLP venues (ACL, EMNLP) expect it for generation tasks

Key design decisions:

Decision	Options	Guidance
Annotator type	Expert, crowdworker, end-user	Match to what your claims require
Scale	Likert (1-5), pairwise comparison, ranking	Pairwise is more reliable than Likert for LLM outputs
Sample size	Per annotator and total items	Power analysis or minimum 100 items, 3+ annotators
Agreement metric	Cohen's kappa, Krippendorff's alpha, ICC	Krippendorff's alpha for >2 annotators; report raw agreement too
Platform	Prolific, MTurk, internal team	Prolific for quality; MTurk for scale; internal for domain expertise

Annotation guideline checklist:

- [ ] Clear task description with examples (good AND bad)
- [ ] Decision criteria for ambiguous cases
- [ ] At least 2 worked examples per category
- [ ] Attention checks / gold standard items (10-15% of total)
- [ ] Qualification task or screening round
- [ ] Estimated time per item and fair compensation (>= local minimum wage)
- [ ] IRB/ethics review if required by your institution

Reporting requirements (reviewers check all of these):

• Number of annotators and their qualifications
• Inter-annotator agreement with specific metric and value
• Compensation details (amount, estimated hourly rate)
• Annotation interface description or screenshot (appendix)
• Total annotation time

See references/human-evaluation.md for complete guide including statistical tests for human eval data, crowdsourcing quality control patterns, and IRB guidance.

---

Phase 3: Experiment Execution & Monitoring

Goal: Run experiments reliably, monitor progress, recover from failures.

Step 3.1: Launch Experiments

Use nohup for long-running experiments:

nohup python run_experiment.py --config config.yaml > logs/experiment_01.log 2>&1 &
echo $!  # Record the PID

Parallel execution: Run independent experiments simultaneously, but be aware of API rate limits. 4+ concurrent experiments on the same API will slow each down.

Step 3.2: Set Up Monitoring (Cron Pattern)

For long-running experiments, set up periodic status checks. The cron prompt should follow this template:

Monitor Prompt Template:
1. Check if process is still running: ps aux | grep <pattern>
2. Read last 30 lines of log: tail -30 <logfile>
3. Check for completed results: ls <result_dir>
4. If results exist, read and report: cat <result_file>
5. If all done, commit: git add -A && git commit -m "<descriptive message>" && git push
6. Report in structured format (tables with key metrics)
7. Answer the key analytical question for this experiment

Silent mode: If nothing has changed since the last check, respond with [SILENT] to suppress notification to the user. Only report when there's news.

Step 3.3: Handle Failures

Common failure modes and recovery:

Failure	Detection	Recovery
API rate limit / credit exhaustion	402/429 errors in logs	Wait, then re-run (scripts skip completed work)
Process crash	PID gone, incomplete results	Re-run from last checkpoint
Timeout on hard problems	Process stuck, no log progress	Kill and skip, note in results
Wrong model ID	Errors referencing model name	Fix ID and re-run

Key: Scripts should always check for existing results and skip completed work. This makes re-runs safe and efficient.

Step 3.4: Commit Completed Results

After each experiment batch completes:

git add -A
git commit -m "Add <experiment name>: <key finding in 1 line>"
git push

Step 3.5: Maintain an Experiment Journal

Git commits track what happened, but not the exploration tree — the decisions about what to try next based on what you learned. Maintain a structured experiment journal that captures this tree:

// experiment_journal.jsonl — append one entry per experiment attempt
{
  "id": "exp_003",
  "parent": "exp_001",
  "timestamp": "2025-05-10T14:30:00Z",
  "hypothesis": "Adding scope constraints will fix convergence failure from exp_001",
  "plan": "Re-run autoreason with max_tokens=2000 and fixed structure template",
  "config": {"model": "haiku", "strategy": "autoreason", "max_tokens": 2000},
  "status": "completed",
  "result_path": "results/exp_003/",
  "key_metrics": {"win_rate": 0.85, "convergence_rounds": 3},
  "analysis": "Scope constraints fixed convergence. Win rate jumped from 0.42 to 0.85.",
  "next_steps": ["Try same constraints on Sonnet", "Test without structure template"],
  "figures": ["figures/exp003_convergence.pdf"]
}

Why a journal, not just git? Git tracks file changes. The journal tracks the reasoning: why you tried X, what you learned, and what that implies for the next experiment. When writing the paper, this tree is invaluable for the Methods section ("we observed X, which motivated Y") and for honest failure reporting.

Selecting the best path: When the journal shows a branching tree (exp_001 → exp_002a, exp_002b, exp_003), identify the path that best supports the paper's claims. Document dead-end branches in the appendix as ablations or negative results.

Snapshot code per experiment: Copy the experiment script after each run:

cp experiment.py results/exp_003/experiment_snapshot.py

This enables exact reproduction even after subsequent code changes.

---

Phase 4: Result Analysis

Goal: Extract findings, compute statistics, identify the story.

Step 4.1: Aggregate Results

Write analysis scripts that:

1. Load all result files from a batch
2. Compute per-task and aggregate metrics
3. Generate summary tables

# Standard analysis pattern
import json, os
from pathlib import Path

results = {}
for result_file in Path("results/").rglob("result.json"):
    data = json.loads(result_file.read_text())
    strategy = result_file.parent.name
    task = result_file.parent.parent.name
    results.setdefault(strategy, {})[task] = data

# Compute aggregate metrics
for strategy, tasks in results.items():
    scores = [t["score"] for t in tasks.values()]
    print(f"{strategy}: mean={np.mean(scores):.1f}, std={np.std(scores):.1f}")

Step 4.2: Statistical Significance

Always compute:

• Error bars: Standard deviation or standard error, specify which
• Confidence intervals: 95% CI for key results
• Pairwise tests: McNemar's test for comparing two methods
• Effect sizes: Cohen's d or h for practical significance

See references/experiment-patterns.md for complete implementations of McNemar's test, bootstrapped CIs, and Cohen's h.

Step 4.3: Identify the Story

After analysis, explicitly answer:

1. What is the main finding? State it in one sentence.
2. What surprised you? Unexpected results often make the best papers.
3. What failed? Failed experiments can be the most informative. Honest reporting of failures strengthens the paper.
4. What follow-up experiments are needed? Results often raise new questions.

Handling Negative or Null Results

When your hypothesis was wrong or results are inconclusive, you have three options:

Situation	Action	Venue Fit
Hypothesis wrong but why is informative	Frame paper around the analysis of why	NeurIPS, ICML (if analysis is rigorous)
Method doesn't beat baselines but reveals something new	Reframe contribution as understanding/analysis	ICLR (values understanding), workshop papers
Clean negative result on popular claim	Write it up — the field needs to know	NeurIPS Datasets & Benchmarks, TMLR, workshops
Results inconclusive, no clear story	Pivot — run different experiments or reframe	Don't force a paper that isn't there

How to write a negative results paper:

• Lead with what the community believes and why it matters to test it
• Describe your rigorous methodology (must be airtight — reviewers will scrutinize harder)
• Present the null result clearly with statistical evidence
• Analyze why the expected result didn't materialize
• Discuss implications for the field

Venues that explicitly welcome negative results: NeurIPS (Datasets & Benchmarks track), TMLR, ML Reproducibility Challenge, workshops at major conferences. Some workshops specifically call for negative results.

Step 4.4: Create Figures and Tables

Figures:

• Use vector graphics (PDF) for all plots: plt.savefig('fig.pdf')
• Colorblind-safe palettes (Okabe-Ito or Paul Tol)
• Self-contained captions — reader should understand without main text
• No title inside figure — the caption serves this function

Tables:

• Use booktabs LaTeX package
• Bold best value per metric
• Include direction symbols (higher/lower better)
• Consistent decimal precision

\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}

Step 4.5: Decide: More Experiments or Write?

Situation	Action
Core claims supported, results significant	Move to Phase 5 (writing)
Results inconclusive, need more data	Back to Phase 2 (design)
Unexpected finding suggests new direction	Back to Phase 2 (design)
Missing one ablation reviewers will ask for	Run it, then Phase 5
All experiments done but some failed	Note failures, move to Phase 5

Step 4.6: Write the Experiment Log (Bridge to Writeup)

Before moving to paper writing, create a structured experiment log that bridges results to prose. This is the single most important connective tissue between experiments and the writeup — without it, the writing agent has to re-derive the story from raw result files.

Create experiment_log.md with the following structure:

# Experiment Log

## Contribution (one sentence)
[The paper's main claim]

## Experiments Run

### Experiment 1: [Name]
- **Claim tested**: [Which paper claim this supports]
- **Setup**: [Model, dataset, config, number of runs]
- **Key result**: [One sentence with the number]
- **Result files**: results/exp1/final_info.json
- **Figures generated**: figures/exp1_comparison.pdf
- **Surprising findings**: [Anything unexpected]

### Experiment 2: [Name]
...

## Figures
| Filename | Description | Which section it belongs in |
|----------|-------------|---------------------------|
| figures/main_comparison.pdf | Bar chart comparing all methods on benchmark X | Results, Figure 2 |
| figures/ablation.pdf | Ablation removing components A, B, C | Results, Figure 3 |
...

## Failed Experiments (document for honesty)
- [What was tried, why it failed, what it tells us]

## Open Questions
- [Anything the results raised that the paper should address]

Why this matters: When drafting, the agent (or a delegated sub-agent) can load experiment_log.md alongside the LaTeX template and produce a first draft grounded in actual results. Without this bridge, the writing agent must parse raw JSON/CSV files and infer the story — a common source of hallucinated or misreported numbers.

Git discipline: Commit this log alongside the results it describes.

---

Iterative Refinement: Strategy Selection

Any output in this pipeline — paper drafts, experiment scripts, analysis — can be iteratively refined. The autoreason research provides empirical evidence for when each refinement strategy works and when it fails. Use this section to choose the right approach.

Quick Decision Table

Your Situation	Strategy	Why
Mid-tier model + constrained task	Autoreason	Sweet spot. Generation-evaluation gap is widest. Baselines actively destroy weak model outputs.
Mid-tier model + open task	Autoreason with scope constraints added	Add fixed facts, structure, or deliverable to bound the improvement space.
Frontier model + constrained task	Autoreason	Wins 2/3 constrained tasks even at frontier.
Frontier model + unconstrained task	Critique-and-revise or single pass	Autoreason comes last. Model self-evaluates well enough.
Concrete technical task (system design)	Critique-and-revise	Direct find-and-fix loop is more efficient.
Template-filling task (one correct structure)	Single pass or conservative	Minimal decision space. Iteration adds no value.
Code with test cases	Autoreason (code variant)	Structured analysis of why it failed before fixing. Recovery rate 62% vs 43%.
Very weak model (Llama 8B class)	Single pass	Model too weak for diverse candidates. Invest in generation quality.

The Generation-Evaluation Gap

Core insight: Autoreason's value depends on the gap between a model's generation capability and its self-evaluation capability.

Model Tier        │ Generation │ Self-Eval │ Gap    │ Autoreason Value
──────────────────┼────────────┼───────────┼────────┼─────────────────
Weak (Llama 8B)   │ Poor       │ Poor      │ Small  │ None — can't generate diverse candidates
Mid (Haiku 3.5)   │ Decent     │ Poor      │ LARGE  │ MAXIMUM — 42/42 perfect Borda
Mid (Gemini Flash)│ Decent     │ Moderate  │ Large  │ High — wins 2/3
Strong (Sonnet 4) │ Good       │ Decent    │ Medium │ Moderate — wins 3/5
Frontier (S4.6)   │ Excellent  │ Good      │ Small  │ Only with constraints

This gap is structural, not temporary. As costs drop, today's frontier becomes tomorrow's mid-tier. The sweet spot moves but never disappears.

Autoreason Loop (Summary)

Each pass produces three candidates from fresh, isolated agents:

1. Critic → finds problems in incumbent A (no fixes)
2. Author B → revises A based on critique
3. Synthesizer → merges A and B (randomized labels)
4. Judge Panel → 3 blind CoT judges rank A, B, AB via Borda count
5. Convergence → A wins k=2 consecutive passes → done

Key parameters:

• k=2 convergence (k=1 premature, k=3 too expensive, no quality gain)
• CoT judges always (3x faster convergence)
• Temperature 0.8 authors, 0.3 judges
• Conservative tiebreak: incumbent wins ties
• Every role is a fresh agent with no shared context

Applying to Paper Drafts

When refining the paper itself through autoreason:

• Provide ground truth to the critic: actual experimental data, result JSONs, statistical outputs. Without this, models hallucinate fabricated ablation studies and fake confidence intervals.
• Use 3 working judges minimum: A broken judge parser doesn't add noise — it prevents equilibrium entirely.
• Scope constrain the revision: "Address these specific weaknesses" not "improve the paper."

Failure Modes

Failure	Detection	Fix
No convergence (A never wins)	A wins <15% over 20+ passes	Add scope constraints to the task
Synthesis drift	Word counts grow unboundedly	Constrain structure and deliverable
Degradation below single pass	Baselines score higher than iterated output	Switch to single pass; model may be too weak
Overfitting (code)	High public-test pass, low private-test pass	Use structured analysis, not just test feedback
Broken judges	Parsing failures reduce panel below 3	Fix parser before continuing

See references/autoreason-methodology.md for complete prompts, Borda scoring details, model selection guide, scope constraint design patterns, and compute budget reference.

---

Phase 5: Paper Drafting

Goal: Write a complete, publication-ready paper.

Context Management for Large Projects

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)
• Load one section's context at a time when delegating. A sub-agent drafting Methods doesn't need the literature review notes.
• Summarize, don't include raw files. For a 200-line result JSON, load a 10-line summary table. For a 50-page related paper, load the 5-sentence abstract + your 2-line note about its relevance.
• For very large projects: Create a 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 Narrative Principle

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.

The Sources Behind This Guidance

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:

• references/writing-guide.md — Full explanations with examples
• references/sources.md — Complete bibliography

Time Allocation

Spend approximately equal time on each of:

1. The abstract
2. The introduction
3. The figures
4. Everything else combined

Why? Most reviewers form judgments before reaching your methods. Readers encounter your paper as: title → abstract → introduction → figures → maybe the rest.

Writing Workflow

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

Two-Pass Refinement Pattern

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."

LaTeX Error Checklist

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

Step 5.0: Title

The title is the single most-read element of the paper. It determines whether anyone clicks through to the abstract.

Good titles:

• State the contribution or finding: "Autoreason: When Iterative LLM Refinement Works and Why It Fails"
• Highlight a surprising result: "Scaling Data-Constrained Language Models" (implies you can)
• Name the method + what it does: "DPO: Direct Preference Optimization of Language Models"

Bad titles:

• Too generic: "An Approach to Improving Language Model Outputs"
• Too long: anything over ~15 words
• Jargon-only: "Asymptotic Convergence of Iterative Stochastic Policy Refinement" (who is this for?)

Rules:

• Include your method name if you have one (for citability)
• Include 1-2 keywords reviewers will search for
• Avoid colons unless both halves carry meaning
• Test: would a reviewer know the domain and contribution from the title alone?

Step 5.1: Abstract (5-Sentence Formula)

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..."

Step 5.2: Figure 1

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.

Step 5.3: Introduction (1-1.5 pages max)

Must include:

• Clear problem statement
• Brief approach overview
• 2-4 bullet contribution list (max 1-2 lines each in two-column format)
• Methods should start by page 2-3

Step 5.4: Methods

Enable reimplementation:

• Conceptual outline or pseudocode
• All hyperparameters listed
• Architectural details sufficient for reproduction
• Present final design decisions; ablations go in experiments

Step 5.5: Experiments & Results

For each experiment, explicitly state:

• What claim it supports
• How it connects to main contribution
• What to observe: "the blue line shows X, which demonstrates Y"

Requirements:

• Error bars with methodology (std dev vs std error)
• Hyperparameter search ranges
• Compute infrastructure (GPU type, total hours)
• Seed-setting methods

Step 5.6: Related Work

Organize methodologically, not paper-by-paper. Cite generously — reviewers likely authored relevant papers.

Step 5.7: Limitations (REQUIRED)

All major conferences require this. Honesty helps:

• Reviewers are instructed not to penalize honest limitation acknowledgment
• Pre-empt criticisms by identifying weaknesses first
• Explain why limitations don't undermine core claims

Step 5.8: Conclusion & Discussion

Conclusion (required, 0.5-1 page):

• Restate the contribution in one sentence (different wording from abstract)
• Summarize key findings (2-3 sentences, not a list)
• Implications: what does this mean for the field?
• Future work: 2-3 concrete next steps (not vague "we leave X for future work")

Discussion (optional, sometimes combined with conclusion):

• Broader implications beyond immediate results
• Connections to other subfields
• Honest assessment of when the method does and doesn't work
• Practical deployment considerations

Do NOT introduce new results or claims in the conclusion.

Step 5.9: Appendix Strategy

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:

• The main paper must be self-contained — reviewers are not required to read appendices
• Never put critical evidence only in the appendix
• Cross-reference: "Full results in Table 5 (Appendix B)" not just "see appendix"
• Use \appendix command, then \section{A: Proofs} etc.

Page Budget Management

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.

Step 5.10: Ethics & Broader Impact Statement

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:

• Writing "we foresee no negative impacts" (almost never true — reviewers distrust this)
• Being vague: "this could be misused" without specifying how
• Ignoring compute costs for large-scale work
• Forgetting to disclose LLM use at venues that require it

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")

Step 5.11: Datasheets & Model Cards (If Applicable)

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

Writing Style

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):

• Be specific: "accuracy" not "performance"
• Eliminate hedging: drop "may" unless genuinely uncertain
• Consistent terminology throughout
• Avoid incremental vocabulary: "develop", not "combine"

Full writing guide with examples: See references/writing-guide.md

Using LaTeX Templates

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}

Template Pitfalls

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')

Quick Template Reference

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 and Figures

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:

• Bold best value per metric
• Include direction symbols ($\uparrow$ higher better, $\downarrow$ lower better)
• Right-align numerical columns
• Consistent decimal precision

Figures:

• Vector graphics (PDF, EPS) for all plots and diagrams — plt.savefig('fig.pdf')
• Raster (PNG 600 DPI) only for photographs
• Colorblind-safe palettes (Okabe-Ito or Paul Tol)
• Verify grayscale readability (8% of men have color vision deficiency)
• No title inside figure — the caption serves this function
• Self-contained captions — reader should understand without main text

Conference Resubmission

For converting between venues, see Phase 7 (Submission Preparation) — it covers the full conversion workflow, page-change table, and post-rejection guidance.

Professional LaTeX Preamble

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.
• Check if the conference template already loads any of these (especially algorithm, amsmath, graphicx). Don't double-load.

siunitx Table Alignment

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.

Subfigures

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".

Pseudocode with algorithm2e

\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 Diagram Patterns

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}

latexdiff for Revision Tracking

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.

SciencePlots for matplotlib

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):

• Single column: figsize=(3.5, 2.5) — fits in one column
• Double column: figsize=(7.0, 3.0) — spans both columns
• Square: figsize=(3.5, 3.5) — for heatmaps, confusion matrices

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Moved to references/ (same content, loaded on demand)

• references/phase-6-self-review-revision.md — Phase 6: Self-Review & Revision; Phase 7: Submission Preparation; Setup; Reproduction; Citation; Phase 8: Post-Acceptance Deliverables; …