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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.
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
Responding to reviews with additional experiments or revisions
Converting a paper between conference formats
Core Philosophy
Be proactive. Deliver complete drafts, not questions. Scientists are busy — produce something concrete they can react to, then iterate.
Never hallucinate citations. AI-generated citations have ~40% error rate. Always fetch programmatically. Mark unverifiable citations as [CITATION NEEDED].
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.
Experiments serve claims. Every experiment must explicitly state which claim it supports. Never run experiments that don't connect to the paper's narrative.
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.
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.
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:
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")
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
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 ...withopen(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:
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
Phase 4: Result Analysis
Goal: Extract findings, compute statistics, identify the story.
Step 4.1: Aggregate Results
Write analysis scripts that:
Load all result files from a batch
Compute per-task and aggregate metrics
Generate summary tables
# Standard analysis patternimport 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 metricsfor 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
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:
Critic → finds problems in incumbent A (no fixes)
Author B → revises A based on critique
Synthesizer → merges A and B (randomized labels)
Judge Panel → 3 blind CoT judges rank A, B, AB via Borda count
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.
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.
Time Allocation
Spend approximately equal time on each of:
The abstract
The introduction
The figures
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
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 5.3: Methods
Enable reimplementation:
Conceptual outline or pseudocode
All hyperparameters listed
Architectural details sufficient for reproduction
Present final design decisions; ablations go in experiments
Step 5.4: 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 5.5: Related Work
Organize methodologically, not paper-by-paper. Cite generously — reviewers likely authored relevant papers.
Step 5.6: 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
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.
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 afterhyperref. 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:
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".
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
Phase 6: Self-Review & Revision
Goal: Simulate the review process before submission. Catch weaknesses early.
Step 6.1: Simulate Reviews
Generate reviews from multiple perspectives using strong models (Opus 4, Sonnet 4.6, Gemini 2.5 Pro). Use the reviewer guidelines from the target venue.
Review prompt template:
You are an expert reviewer for [VENUE]. Review this paper according to the
official reviewer guidelines. Evaluate:
1. Quality (technical soundness, baselines, claims supported by evidence)
2. Clarity (writing, notation consistency, reproducibility)
3. Significance (impact, importance of the problem)
4. Originality (novelty, new insights)
Provide:
- Summary (2-3 sentences)
- Strengths (bullet list)
- Weaknesses (bullet list, most critical first)
- Questions for authors
- Missing references
- Score (1-6 on NeurIPS scale)
- Confidence (1-5)
Step 6.2: Prioritize Feedback
After collecting reviews, categorize:
Priority
Action
Critical (technical flaw, missing baseline)
Must fix. May require new experiments → back to Phase 2
High (clarity issue, missing ablation)
Should fix in this revision
Medium (minor writing issues, extra experiments)
Fix if time allows
Low (style preferences, tangential suggestions)
Note for future work
Step 6.3: Revision Cycle
For each critical/high issue:
Identify the specific section(s) affected
Draft the fix
Verify the fix doesn't break other claims
Update the paper
Re-check against the reviewer's concern
Step 6.4: Rebuttal Writing
When responding to actual reviews (post-submission), rebuttals are a distinct skill from revision:
Format: Point-by-point. For each reviewer concern:
> R1-W1: "The paper lacks comparison with Method X."
We thank the reviewer for this suggestion. We have added a comparison with
Method X in Table 3 (revised). Our method outperforms X by 3.2pp on [metric]
(p<0.05). We note that X requires 2x our compute budget.
Rules:
Address every concern — reviewers notice if you skip one
Lead with the strongest responses
Be concise and direct — reviewers read dozens of rebuttals
Include new results if you ran experiments during the rebuttal period
Never be defensive or dismissive, even of weak criticisms
Use latexdiff to generate a marked-up PDF showing changes (see Professional LaTeX Tooling section)
Thank reviewers for specific, actionable feedback (not generic praise)
What NOT to do: "We respectfully disagree" without evidence. "This is out of scope" without explanation. Ignoring a weakness by only responding to strengths.
Step 6.5: Paper Evolution Tracking
Save snapshots at key milestones:
paper/
paper.tex # Current working version
paper_v1_first_draft.tex # First complete draft
paper_v2_post_review.tex # After simulated review
paper_v3_pre_submission.tex # Final before submission
paper_v4_camera_ready.tex # Post-acceptance final
Phase 7: Submission Preparation
Goal: Final checks, formatting, and submission.
Step 7.1: Conference Checklist
Every venue has mandatory checklists. Complete them carefully — incomplete checklists can result in desk rejection.
Double-blind review means reviewers cannot know who wrote the paper. Check ALL of these:
Anonymization Checklist:
- [ ] No author names or affiliations anywhere in the PDF
- [ ] No acknowledgments section (add after acceptance)
- [ ] Self-citations written in third person: "Smith et al. [1] showed..." not "We previously showed [1]..."
- [ ] No GitHub/GitLab URLs pointing to your personal repos
- [ ] Use Anonymous GitHub (https://anonymous.4open.science/) for code links
- [ ] No institutional logos or identifiers in figures
- [ ] No file metadata containing author names (check PDF properties)
- [ ] No "our previous work" or "in our earlier paper" phrasing
- [ ] Dataset names don't reveal institution (rename if needed)
- [ ] Supplementary materials don't contain identifying information
Common mistakes: Git commit messages visible in supplementary code, watermarked figures from institutional tools, acknowledgments left in from a previous draft, arXiv preprint posted before anonymity period.
Step 7.3: Formatting Verification
Pre-Submission Format Check:
- [ ] Page limit respected (excluding references and appendix)
- [ ] All figures are vector (PDF) or high-res raster (600 DPI PNG)
- [ ] All figures readable in grayscale
- [ ] All tables use booktabs
- [ ] References compile correctly (no "?" in citations)
- [ ] No overfull hboxes in critical areas
- [ ] Appendix clearly labeled and separated
- [ ] Required sections present (limitations, broader impact, etc.)
When cutting pages: move proofs to appendix, condense related work, combine tables, use subfigures.
When expanding: add ablations, expand limitations, include additional baselines, add qualitative examples.
After rejection: Address reviewer concerns in the new version, but don't include a "changes" section or reference the previous submission (blind review).
After acceptance, prepare the camera-ready version:
Camera-Ready Checklist:
- [ ] De-anonymize: add author names, affiliations, email addresses
- [ ] Add Acknowledgments section (funding, compute grants, helpful reviewers)
- [ ] Add public code/data URL (real GitHub, not anonymous)
- [ ] Address any mandatory revisions from meta-reviewer
- [ ] Switch template to camera-ready mode (if applicable — e.g., AAAI \anon → \camera)
- [ ] Add copyright notice if required by venue
- [ ] Update any "anonymous" placeholders in text
- [ ] Verify final PDF compiles cleanly
- [ ] Check page limit for camera-ready (sometimes differs from submission)
- [ ] Upload supplementary materials (code, data, appendix) to venue portal
Hermes Agent Integration
This skill is designed for the Hermes agent. It uses Hermes tools, delegation, scheduling, and memory for the full research lifecycle.
Related Skills
Compose this skill with other Hermes skills for specific phases:
Skill
When to Use
How to Load
arxiv
Phase 1 (Literature Review): searching arXiv, generating BibTeX, finding related papers via Semantic Scholar
skill_view("arxiv")
subagent-driven-development
Phase 5 (Drafting): parallel section writing with 2-stage review (spec compliance then quality)
skill_view("subagent-driven-development")
plan
Phase 0 (Setup): creating structured plans before execution. Writes to .hermes/plans/
skill_view("plan")
qmd
Phase 1 (Literature): searching local knowledge bases (notes, transcripts, docs) via hybrid BM25+vector search
Install: skill_manage("install", "qmd")
diagramming
Phase 4-5: creating Excalidraw-based figures and architecture diagrams
skill_view("diagramming")
data-science
Phase 4 (Analysis): Jupyter live kernel for interactive analysis and visualization
skill_view("data-science")
This skill supersedes ml-paper-writing — it contains all of ml-paper-writing's content plus the full experiment/analysis pipeline and autoreason methodology.
delegate_task("Draft the Methods section based on these experiment scripts and configs.
Include: pseudocode, all hyperparameters, architectural details sufficient for
reproduction. Write in LaTeX using the neurips2025 template conventions.")
delegate_task("Draft the Related Work section. Use web_search and web_extract to
find papers. Verify every citation via Semantic Scholar. Group by methodology.")
delegate_task("Draft the Experiments section. Read all result files in results/.
State which claim each experiment supports. Include error bars and significance.")
Each delegate runs as a fresh subagent with no shared context — provide all necessary information in the prompt. Collect outputs and integrate.
Citation verification (using execute_code):
# In execute_code:from semanticscholar import SemanticScholar
import requests
sch = SemanticScholar()
results = sch.search_paper("attention mechanism transformers", limit=5)
for paper in results:
doi = paper.externalIds.get('DOI', 'N/A')
if doi != 'N/A':
bibtex = requests.get(f"https://doi.org/{doi}",
headers={"Accept": "application/x-bibtex"}).text
print(bibtex)
1. todo("list") # Check current task list
2. memory("read") # Recall key decisions
3. terminal("git log --oneline -10") # Check recent commits
4. terminal("ps aux | grep python") # Check running experiments
5. terminal("ls results/ | tail -20") # Check for new results
6. Report status to user, ask for direction
Cron Monitoring with cronjob
Use the cronjob tool to schedule periodic experiment checks:
cronjob("create", {
"schedule": "*/30 * * * *", # Every 30 minutes
"prompt": "Check experiment status:
1. ps aux | grep run_experiment
2. tail -30 logs/experiment_haiku.log
3. ls results/haiku_baselines/
4. If complete: read results, compute Borda scores,
git add -A && git commit -m 'Add Haiku results' && git push
5. Report: table of results, key finding, next step
6. If nothing changed: respond with [SILENT]"
})
[SILENT] protocol: When nothing has changed since the last check, respond with exactly [SILENT]. This suppresses notification delivery to the user. Only report when there are genuine changes worth knowing about.
Deadline tracking:
cronjob("create", {
"schedule": "0 9 * * *", # Daily at 9am
"prompt": "NeurIPS 2025 deadline: May 22. Today is {date}.
Days remaining: {compute}.
Check todo list — are we on track?
If <7 days: warn user about remaining tasks."
})
Communication Patterns
When to notify the user (via send_message or direct response):
Experiment batch completed (with results table)
Unexpected finding or failure requiring decision
Draft section ready for review
Deadline approaching with incomplete tasks
When NOT to notify:
Experiment still running, no new results → [SILENT]
