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npx skills add https://github.com/NousResearch/hermes-agent --skill research-paper-writingコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
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SOC 職業分類に基づく
SKILL.md を表示中
| name | research-paper-writing |
| title | Research Paper Writing Pipeline |
| description | Write ML papers for NeurIPS/ICML/ICLR: design→submit. |
| version | 1.1.0 |
| author | Orchestra Research |
| license | MIT |
| dependencies | ["semanticscholar","arxiv","habanero","requests","scipy","numpy","matplotlib","SciencePlots"] |
| platforms | ["linux","macos"] |
| metadata | {"hermes":{"tags":["Research","Paper Writing","Experiments","ML","AI","NeurIPS","ICML","ICLR","ACL","AAAI","COLM","LaTeX","Citations","Statistical Analysis"],"category":"research","related_skills":["arxiv","subagent-driven-development","plan"],"requires_toolsets":["terminal","files"]}} |
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.
┌─────────────────────────────────────────────────────────────┐
│ 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) │
│ │
└─────────────────────────────────────────────────────────────┘
Use this skill when:
[CITATION NEEDED]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.
Goal: Establish the workspace, understand existing work, identify the contribution.
# 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 claimsresults/, outputs/, experiments/ — existing findingsconfigs/ — experimental settings.bib files — existing citationsEstablish 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)
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
Before writing anything, articulate:
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?"
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.
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.
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\citet{} vs \citep{} usageGoal: Find related work, identify baselines, gather citations.
Start from papers already referenced in the codebase:
# Via terminal:
grep -r "arxiv\|doi\|cite" --include="*.md" --include="*.bib" --include="*.py"
find . -name "*.bib"
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"
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.
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.
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."
Goal: Design experiments that directly support paper claims. Every experiment must answer a specific question.
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.
Strong baselines are what separates accepted papers from rejected ones. Reviewers will ask: "Did they compare against X?"
Standard baseline categories:
Before running anything, specify:
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.
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:
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):
See references/human-evaluation.md for complete guide including statistical tests for human eval data, crowdsourcing quality control patterns, and IRB guidance.
Goal: Run experiments reliably, monitor progress, recover from failures.
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.
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.
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.
After each experiment batch completes:
git add -A
git commit -m "Add <experiment name>: <key finding in 1 line>"
git push
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.
Goal: Extract findings, compute statistics, identify the story.
Write analysis scripts that:
# 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}")
Always compute:
See references/experiment-patterns.md for complete implementations of McNemar's test, bootstrapped CIs, and Cohen's h.
After analysis, explicitly answer:
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:
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.
Figures:
plt.savefig('fig.pdf')Tables:
booktabs LaTeX package\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}
| 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 |
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.
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.
| 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. |
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.
Each pass produces three candidates from fresh, isolated agents:
Key parameters:
When refining the paper itself through autoreason:
| 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.
The complete drafting procedure (section-by-section order, LaTeX scaffolding, figure/table
conventions, abstract and intro formulas, related-work positioning) lives in
references/phase5-paper-drafting.md — load it with read_file when you reach this phase.
Pair it with references/writing-guide.md for prose-level style rules.
Goal: Simulate the review process before submission. Catch weaknesses early.
Generate reviews from multiple perspectives. The key insight from automated research pipelines (notably SakanaAI's AI-Scientist): ensemble reviewing with a meta-reviewer produces far more calibrated feedback than a single review pass.
Step 1: Generate N independent reviews (N=3-5)
Use different models or temperature settings. Each reviewer sees only the paper, not other reviews. Default to negative bias — LLMs have well-documented positivity bias in evaluation.
You are an expert reviewer for [VENUE]. You are critical and thorough.
If a paper has weaknesses or you are unsure about a claim, flag it clearly
and reflect that in your scores. Do not give the benefit of the doubt.
Review this paper according to the official reviewer guidelines. Evaluate:
1. Soundness (are claims well-supported? are baselines fair and strong?)
2. Clarity (is the paper well-written? could an expert reproduce it?)
3. Significance (does this matter to the community?)
4. Originality (new insights, not just incremental combination?)
Provide your review as structured JSON:
{
"summary": "2-3 sentence summary",
"strengths": ["strength 1", "strength 2", ...],
"weaknesses": ["weakness 1 (most critical)", "weakness 2", ...],
"questions": ["question for authors 1", ...],
"missing_references": ["paper that should be cited", ...],
"soundness": 1-4,
"presentation": 1-4,
"contribution": 1-4,
"overall": 1-10,
"confidence": 1-5
}
Step 2: Meta-review (Area Chair aggregation)
Feed all N reviews to a meta-reviewer:
You are an Area Chair at [VENUE]. You have received [N] independent reviews
of a paper. Your job is to:
1. Identify consensus strengths and weaknesses across reviewers
2. Resolve disagreements by examining the paper directly
3. Produce a meta-review that represents the aggregate judgment
4. Use AVERAGED numerical scores across all reviews
Be conservative: if reviewers disagree on whether a weakness is serious,
treat it as serious until the authors address it.
Reviews:
[review_1]
[review_2]
...
Step 3: Reflection loop (optional, 2-3 rounds)
Each reviewer can refine their review after seeing the meta-review. Use an early termination sentinel: if the reviewer responds "I am done" (no changes), stop iterating.
Model selection for reviewing: Reviewing is best done with the strongest available model, even if you wrote the paper with a cheaper one. The reviewer model should be chosen independently from the writing model.
Few-shot calibration: If available, include 1-2 real published reviews from the target venue as examples. This dramatically improves score calibration. See references/reviewer-guidelines.md for example reviews.
Text-only review misses an entire class of problems: figure quality, layout issues, visual consistency. If you have access to a vision-capable model, run a separate visual review on the compiled PDF:
You are reviewing the visual presentation of this research paper PDF.
Check for:
1. Figure quality: Are plots readable? Labels legible? Colors distinguishable?
2. Figure-caption alignment: Does each caption accurately describe its figure?
3. Layout issues: Orphaned section headers, awkward page breaks, figures far from their references
4. Table formatting: Aligned columns, consistent decimal precision, bold for best results
5. Visual consistency: Same color scheme across all figures, consistent font sizes
6. Grayscale readability: Would the figures be understandable if printed in B&W?
For each issue, specify the page number and exact location.
This catches problems that text-based review cannot: a plot with illegible axis labels, a figure placed 3 pages from its first reference, inconsistent color palettes between Figure 2 and Figure 5, or a table that's clearly wider than the column width.
After simulated reviews, run a separate verification pass. This catches factual errors that reviewers might miss:
Claim Verification Protocol:
1. Extract every factual claim from the paper (numbers, comparisons, trends)
2. For each claim, trace it to the specific experiment/result that supports it
3. Verify the number in the paper matches the actual result file
4. Flag any claim without a traceable source as [VERIFY]
For agent-based workflows: delegate verification to a fresh sub-agent that receives only the paper text and the raw result files. The fresh context prevents confirmation bias — the verifier doesn't "remember" what the results were supposed to be.
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 |
For each critical/high issue:
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:
latexdiff to generate a marked-up PDF showing changes (see Professional LaTeX Tooling section)What NOT to do: "We respectfully disagree" without evidence. "This is out of scope" without explanation. Ignoring a weakness by only responding to strengths.
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
Goal: Final checks, formatting, and submission.
Every venue has mandatory checklists. Complete them carefully — incomplete checklists can result in desk rejection.
See references/checklists.md for:
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.
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.)
Run these automated checks before attempting pdflatex. Catching errors here is faster than debugging compiler output.
# 1. Lint with chktex (catches common LaTeX mistakes)
# Suppress noisy warnings: -n2 (sentence end), -n24 (parens), -n13 (intersentence), -n1 (command terminated)
chktex main.tex -q -n2 -n24 -n13 -n1
# 2. Verify all citations exist in .bib
# Extract \cite{...} from .tex, check each against .bib
python3 -c "
import re
tex = open('main.tex').read()
bib = open('references.bib').read()
cites = set(re.findall(r'\\\\cite[tp]?{([^}]+)}', tex))
for cite_group in cites:
for cite in cite_group.split(','):
cite = cite.strip()
if cite and cite not in bib:
print(f'WARNING: \\\\cite{{{cite}}} not found in references.bib')
"
# 3. Verify all referenced figures exist on disk
python3 -c "
import re, os
tex = open('main.tex').read()
figs = re.findall(r'\\\\includegraphics(?:\[.*?\])?{([^}]+)}', tex)
for fig in figs:
if not os.path.exists(fig):
print(f'WARNING: Figure file not found: {fig}')
"
# 4. Check for duplicate \label definitions
python3 -c "
import re
from collections import Counter
tex = open('main.tex').read()
labels = re.findall(r'\\\\label{([^}]+)}', tex)
dupes = {k: v for k, v in Counter(labels).items() if v > 1}
for label, count in dupes.items():
print(f'WARNING: Duplicate label: {label} (appears {count} times)')
"
Fix any warnings before proceeding. For agent-based workflows: feed chktex output back to the agent with instructions to make minimal fixes.
# Clean build
rm -f *.aux *.bbl *.blg *.log *.out *.pdf
latexmk -pdf main.tex
# Or manual (triple pdflatex + bibtex for cross-references)
pdflatex -interaction=nonstopmode main.tex
bibtex main
pdflatex -interaction=nonstopmode main.tex
pdflatex -interaction=nonstopmode main.tex
# Verify output exists and has content
ls -la main.pdf
If compilation fails: Parse the .log file for the first error. Common fixes:
| Venue | Special Requirements |
|---|---|
| NeurIPS | Paper checklist in appendix, lay summary if accepted |
| ICML | Broader Impact Statement (after conclusion, doesn't count toward limit) |
| ICLR | LLM disclosure required, reciprocal reviewing agreement |
| ACL | Mandatory Limitations section, Responsible NLP checklist |
| AAAI | Strict style file — no modifications whatsoever |
| COLM | Frame contribution for language model community |
When converting between venues, never copy LaTeX preambles between templates:
# 1. Start fresh with target template
cp -r templates/icml2026/ new_submission/
# 2. Copy ONLY content sections (not preamble)
# - Abstract text, section content, figures, tables, bib entries
# 3. Adjust for page limits
# 4. Add venue-specific required sections
# 5. Update references
| From → To | Page Change | Key Adjustments |
|---|---|---|
| NeurIPS → ICML | 9 → 8 | Cut 1 page, add Broader Impact |
| ICML → ICLR | 8 → 9 | Expand experiments, add LLM disclosure |
| NeurIPS → ACL | 9 → 8 | Restructure for NLP conventions, add Limitations |
| ICLR → AAAI | 9 → 7 | Significant cuts, strict style adherence |
| Any → COLM | varies → 9 | Reframe for language model focus |
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
Posting to arXiv is standard practice in ML but has important timing and anonymity considerations.
Timing decision tree:
| Situation | Recommendation |
|---|---|
| Submitting to double-blind venue (NeurIPS, ICML, ACL) | Post to arXiv after submission deadline, not before. Posting before can technically violate anonymity policies, though enforcement varies. |
| Submitting to ICLR | ICLR explicitly allows arXiv posting before submission. But don't put author names in the submission itself. |
| Paper already on arXiv, submitting to new venue | Acceptable at most venues. Do NOT update arXiv version during review with changes that reference reviews. |
| Workshop paper | arXiv is fine at any time — workshops are typically not double-blind. |
| Want to establish priority | Post immediately if scooping is a concern — but accept the anonymity tradeoff. |
arXiv category selection (ML/AI papers):
| Category | Code | Best For |
|---|---|---|
| Machine Learning | cs.LG | General ML methods |
| Computation and Language | cs.CL | NLP, language models |
| Artificial Intelligence | cs.AI | Reasoning, planning, agents |
| Computer Vision | cs.CV | Vision models |
| Information Retrieval | cs.IR | Search, recommendation |
List primary + 1-2 cross-listed categories. More categories = more visibility, but only cross-list where genuinely relevant.
Versioning strategy:
# Check if your paper's title is already taken on arXiv
# (before choosing a title)
pip install arxiv
python -c "
import arxiv
results = list(arxiv.Search(query='ti:\"Your Exact Title\"', max_results=5).results())
print(f'Found {len(results)} matches')
for r in results: print(f' {r.title} ({r.published.year})')
"
Releasing clean, runnable code significantly increases citations and reviewer trust. Package code alongside the camera-ready submission.
Repository structure:
your-method/
README.md # Setup, usage, reproduction instructions
requirements.txt # Or environment.yml for conda
setup.py # For pip-installable packages
LICENSE # MIT or Apache 2.0 recommended for research
configs/ # Experiment configurations
src/ # Core method implementation
scripts/ # Training, evaluation, analysis scripts
train.py
evaluate.py
reproduce_table1.sh # One script per main result
data/ # Small data or download scripts
download_data.sh
results/ # Expected outputs for verification
README template for research code:
# [Paper Title]
Official implementation of "[Paper Title]" (Venue Year).
## Setup
[Exact commands to set up environment]
## Reproduction
To reproduce Table 1: `bash scripts/reproduce_table1.sh`
To reproduce Figure 2: `python scripts/make_figure2.py`
## Citation
[BibTeX entry]
Pre-release checklist:
- [ ] Code runs from a clean clone (test on fresh machine or Docker)
- [ ] All dependencies pinned to specific versions
- [ ] No hardcoded absolute paths
- [ ] No API keys, credentials, or personal data in repo
- [ ] README covers setup, reproduction, and citation
- [ ] LICENSE file present (MIT or Apache 2.0 for max reuse)
- [ ] Results are reproducible within expected variance
- [ ] .gitignore excludes data files, checkpoints, logs
Anonymous code for submission (before acceptance):
# Use Anonymous GitHub for double-blind review
# https://anonymous.4open.science/
# Upload your repo → get an anonymous URL → put in paper
Goal: Maximize the impact of your accepted paper through presentation materials and community engagement.
Most conferences require a poster session. Poster design principles:
| Element | Guideline |
|---|---|
| Size | Check venue requirements (typically 24"x36" or A0 portrait/landscape) |
| Content | Title, authors, 1-sentence contribution, method figure, 2-3 key results, conclusion |
| Flow | Top-left to bottom-right (Z-pattern) or columnar |
| Text | Title readable at 3m, body at 1m. No full paragraphs — bullet points only. |
| Figures | Reuse paper figures at higher resolution. Enlarge key result. |
Tools: LaTeX (beamerposter package), PowerPoint/Keynote, Figma, Canva.
Production: Order 2+ weeks before the conference. Fabric posters are lighter for travel. Many conferences now support virtual/digital posters too.
If awarded an oral or spotlight presentation:
| Talk Type | Duration | Content |
|---|---|---|
| Spotlight | 5 min | Problem, approach, one key result. Rehearse to exactly 5 minutes. |
| Oral | 15-20 min | Full story: problem, approach, key results, ablations, limitations. |
| Workshop talk | 10-15 min | Adapt based on workshop audience — may need more background. |
Slide design rules:
An accessible summary significantly increases impact:
Timing: Post within 1-2 days of paper appearing on proceedings or arXiv camera-ready.
Workshop papers and short papers (e.g., ACL short papers, Findings papers) follow the same pipeline but with different constraints and expectations.
| Property | Workshop | Main Conference |
|---|---|---|
| Page limit | 4-6 pages (typically) | 7-9 pages |
| Review standard | Lower bar for completeness | Must be complete, thorough |
| Review process | Usually single-blind or light review | Double-blind, rigorous |
| What's valued | Interesting ideas, preliminary results, position pieces | Complete empirical story with strong baselines |
| arXiv | Post anytime | Timing matters (see arXiv strategy) |
| Contribution bar | Novel direction, interesting negative result, work-in-progress | Significant advance with strong evidence |
When to target a workshop:
ACL venues have distinct submission types:
| Type | Pages | What's Expected |
|---|---|---|
| Long paper | 8 | Complete study, strong baselines, ablations |
| Short paper | 4 | Focused contribution: one clear point with evidence |
| Findings | 8 | Solid work that narrowly missed main conference |
Short paper strategy: Pick ONE claim and support it thoroughly. Don't try to compress a long paper into 4 pages — write a different, more focused paper.
The main pipeline above targets empirical ML papers. Other paper types require different structures and evidence standards. See references/paper-types.md for detailed guidance on each type.
Structure: Introduction → Preliminaries (definitions, notation) → Main Results (theorems) → Proof Sketches → Discussion → Full Proofs (appendix)
Key differences from empirical papers:
Proof writing principles:
\begin{proof}...\end{proof} environmentsStructure: Introduction → Taxonomy / Organization → Detailed Coverage → Open Problems → Conclusion
Key differences:
Structure: Introduction → Task Definition → Dataset Construction → Baseline Evaluation → Analysis → Intended Use & Limitations
Key differences:
Structure: Introduction → Background → Thesis / Argument → Supporting Evidence → Counterarguments → Implications
Key differences:
This skill is designed for the Hermes agent. It uses Hermes tools, delegation, scheduling, and memory for the full research lifecycle.
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.
| Tool | Usage in This Pipeline |
|---|---|
terminal | LaTeX compilation (latexmk -pdf), git operations, launching experiments (nohup python run.py &), process checks |
process | Background experiment management: process("start", ...), process("poll", pid), process("log", pid), process("kill", pid) |
execute_code | Run Python for citation verification, statistical analysis, data aggregation. Has tool access via RPC. |
read_file / write_file / patch | Paper editing, experiment scripts, result files. Use patch for targeted edits to large .tex files. |
web_search | Literature discovery: web_search("transformer attention mechanism 2024") |
web_extract | Fetch paper content, verify citations: web_extract("https://arxiv.org/abs/2303.17651") |
delegate_task | Parallel section drafting — spawn isolated subagents for each section. Also for concurrent citation verification. |
todo | Primary state tracker across sessions. Update after every phase transition. |
memory | Persist key decisions across sessions: contribution framing, venue choice, reviewer feedback. |
cronjob | Schedule experiment monitoring, deadline countdowns, automated arXiv checks. |
clarify | Ask the user targeted questions when blocked (venue choice, contribution framing). |
cron deliver: | Notify the user when experiments complete or drafts are ready even if they're not in chat — schedule the check as a cron job with a messaging deliver: target (the agent no longer has a send_message tool; outbound delivery is handled by cron/hermes send). |
Experiment monitoring (most common):
terminal("ps aux | grep <pattern>")
→ terminal("tail -30 <logfile>")
→ terminal("ls results/")
→ execute_code("analyze results JSON, compute metrics")
→ terminal("git add -A && git commit -m '<descriptive message>' && git push")
→ (final response auto-delivers "Experiment complete: <summary>"; for unattended runs, schedule via cron with a deliver: target)
Parallel section drafting (using delegation):
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)
memory and todomemory tool — persist key decisions (bounded: ~2200 chars for MEMORY.md):
memory("add", "Paper: autoreason. Venue: NeurIPS 2025 (9 pages).
Contribution: structured refinement works when generation-evaluation gap is wide.
Key results: Haiku 42/42, Sonnet 3/5, S4.6 constrained 2/3.
Status: Phase 5 — drafting Methods section.")
Update memory after major decisions or phase transitions. This persists across sessions.
todo tool — track granular progress:
todo("add", "Design constrained task experiments for Sonnet 4.6")
todo("add", "Run Haiku baseline comparison")
todo("add", "Draft Methods section")
todo("update", id=3, status="in_progress")
todo("update", id=1, status="completed")
Session startup protocol:
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
cronjobUse 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."
})
When to notify the user (via your direct/final response, or a cron deliver: target for unattended runs):
When NOT to notify:
[SILENT][SILENT]Report format — always include structured data:
## Experiment: <name>
Status: Complete / Running / Failed
| Task | Method A | Method B | Method C |
|------|---------|---------|---------|
| Task 1 | 85.2 | 82.1 | **89.4** |
Key finding: <one sentence>
Next step: <what happens next>
Use clarify for targeted questions when genuinely blocked:
| Decision | When to Ask |
|---|---|
| Target venue | Before starting paper (affects page limits, framing) |
| Contribution framing | When multiple valid framings exist |
| Experiment priority | When TODO list has more experiments than time allows |
| Submission readiness | Before final submission |
Do NOT ask about (be proactive, make a choice, flag it):
Understanding what reviewers look for helps focus effort:
| Criterion | What They Check |
|---|---|
| Quality | Technical soundness, well-supported claims, fair baselines |
| Clarity | Clear writing, reproducible by experts, consistent notation |
| Significance | Community impact, advances understanding |
| Originality | New insights (doesn't require new method) |
Scoring (NeurIPS 6-point scale):
See references/reviewer-guidelines.md for detailed guidelines, common concerns, and rebuttal strategies.
| Issue | Solution |
|---|---|
| Abstract too generic | Delete first sentence if it could prepend any ML paper. Start with your specific contribution. |
| Introduction exceeds 1.5 pages | Split background into Related Work. Front-load contribution bullets. |
| Experiments lack explicit claims | Add: "This experiment tests whether [specific claim]..." before each one. |
| Reviewers find paper hard to follow | Add signposting, use consistent terminology, make figure captions self-contained. |
| Missing statistical significance | Add error bars, number of runs, statistical tests, confidence intervals. |
| Scope creep in experiments | Every experiment must map to a specific claim. Cut experiments that don't. |
| Paper rejected, need to resubmit | See Conference Resubmission in Phase 7. Address reviewer concerns without referencing reviews. |
| Missing broader impact statement | See Step 5.10. Most venues require it. "No negative impacts" is almost never credible. |
| Human eval criticized as weak | See Step 2.5 and references/human-evaluation.md. Report agreement metrics, annotator details, compensation. |
| Reviewers question reproducibility | Release code (Step 7.9), document all hyperparameters, include seeds and compute details. |
| Theory paper lacks intuition | Add proof sketches with plain-language explanations before formal proofs. See references/paper-types.md. |
| Results are negative/null | See Phase 4.3 on handling negative results. Consider workshops, TMLR, or reframing as analysis. |
| Document | Contents |
|---|---|
| references/writing-guide.md | Gopen & Swan 7 principles, Perez micro-tips, Lipton word choice, Steinhardt precision, figure design |
| references/citation-workflow.md | Citation APIs, Python code, CitationManager class, BibTeX management |
| references/checklists.md | NeurIPS 16-item, ICML, ICLR, ACL requirements, universal pre-submission checklist |
| references/reviewer-guidelines.md | Evaluation criteria, scoring, common concerns, rebuttal template |
| references/sources.md | Complete bibliography of all writing guides, conference guidelines, APIs |
| references/experiment-patterns.md | Experiment design patterns, evaluation protocols, monitoring, error recovery |
| references/autoreason-methodology.md | Autoreason loop, strategy selection, model guide, prompts, scope constraints, Borda scoring |
| references/human-evaluation.md | Human evaluation design, annotation guidelines, agreement metrics, crowdsourcing QC, IRB guidance |
| references/paper-types.md | Theory papers (proof writing, theorem structure), survey papers, benchmark papers, position papers |
Templates in templates/ for: NeurIPS 2025, ICML 2026, ICLR 2026, ACL, AAAI 2026, COLM 2025.
See templates/README.md for compilation instructions.
Writing Philosophy:
APIs: Semantic Scholar | CrossRef | arXiv