This reference documents the mandatory checklist requirements for major ML/AI conferences. All major venues now require paper checklists—missing them results in desk rejection.
All NeurIPS submissions must include a completed paper checklist. Papers lacking this element face automatic desk rejection. The checklist appears after references and supplemental material, outside the page limit.
Authors must verify that abstract and introduction claims match theoretical and experimental results, with clearly stated contributions, assumptions, and limitations.
What to check: - [ ] Abstract claims match actual results - [ ] Introduction doesn't overclaim - [ ] Contributions are specific and falsifiable
Papers should include a dedicated "Limitations" section addressing strong assumptions, robustness to violations, scope constraints, and performance-influencing factors.
What to include: - [ ] Dedicated Limitations section - [ ] Honest assessment of scope - [ ] Conditions where method may fail
Theoretical contributions require full assumption statements and complete proofs (main paper or appendix with proof sketches for intuition).
What to check: - [ ] All assumptions stated formally - [ ] Complete proofs provided (main text or appendix) - [ ] Proof sketches for intuition in main text
Authors must describe steps ensuring results verification through code release, detailed instructions, model access, or checkpoints appropriate to their contribution type.
What to provide: - [ ] Clear reproducibility statement - [ ] Code availability information - [ ] Model checkpoints if applicable
Instructions for reproducing main experimental results should be provided (supplemental material or URLs), including exact commands and environment specifications.
What to include: - [ ] Exact commands to run experiments - [ ] Environment specifications (requirements.txt, conda env) - [ ] Data access instructions
Papers must specify training details: data splits, hyperparameters, and selection methods in the main paper or supplementary materials.
What to document: - [ ] Train/val/test split details - [ ] All hyperparameters used - [ ] Hyperparameter selection method
Results require error bars, confidence intervals, or statistical tests with clearly stated calculation methods and underlying assumptions.
What to include: - [ ] Error bars or confidence intervals - [ ] Number of runs/seeds - [ ] Calculation method (std dev vs std error)
Specifications needed: compute worker types (CPU/GPU), memory, storage, execution time per run, and total project compute requirements.
What to document: - [ ] GPU type and count - [ ] Training time per run - [ ] Total compute used
Authors confirm adherence to the NeurIPS Code of Ethics, noting any necessary deviations.
What to verify: - [ ] Read NeurIPS Code of Ethics - [ ] Confirm compliance - [ ] Note any deviations with justification
Discussion of potential negative societal applications, fairness concerns, privacy risks, and possible mitigation strategies when applicable.
What to address: - [ ] Potential negative applications - [ ] Fairness considerations - [ ] Privacy implications - [ ] Mitigation strategies
High-risk models (language models, internet-scraped datasets) require controlled release mechanisms and usage guidelines.
What to consider: - [ ] Release strategy for sensitive models - [ ] Usage guidelines if needed - [ ] Access controls if appropriate
All existing assets require creator citations, license names, URLs, version numbers, and terms-of-service acknowledgment.
What to document: - [ ] Dataset licenses cited - [ ] Code licenses respected - [ ] Version numbers included
New releases need structured templates documenting training details, limitations, consent procedures, and licensing information.
For new datasets/models: - [ ] Datasheet or model card - [ ] Training data documentation - [ ] Known limitations
Crowdsourcing studies must include participant instructions, screenshots, compensation details, and comply with minimum wage requirements.
What to include: - [ ] Task instructions - [ ] Compensation details - [ ] Time estimates
Human subjects research requires documented institutional review board approval or equivalent, with risk descriptions disclosed (maintaining anonymity at submission).
What to verify: - [ ] IRB approval obtained - [ ] Risk assessment completed - [ ] Anonymized at submission
Usage of large language models as core methodology components requires disclosure; writing/editing use doesn't require declaration.
What to disclose: - [ ] LLM used as core methodology component - [ ] How LLM was used - [ ] (Writing assistance doesn't require disclosure)
Authors select "yes," "no," or "N/A" per question, with optional 1-2 sentence justifications.
Important: Reviewers are explicitly instructed not to penalize honest limitation acknowledgment.
ICML requires a Broader Impact Statement at the end of the paper, before references. This does NOT count toward the page limit.
Required elements: - Potential positive impacts - Potential negative impacts - Mitigation strategies - Who may be affected
ICLR has a specific LLM disclosure requirement:
"If LLMs played a significant role in research ideation and/or writing to the extent that they could be regarded as a contributor, authors must describe their precise role in a separate appendix section."
When disclosure is required: - LLM used for significant research ideation - LLM used for substantial writing - LLM could be considered a contributor
When disclosure is NOT required: - Grammar checking - Minor editing assistance - Code completion tools
Consequences of non-disclosure: - Desk rejection - Potential post-publication issues
Add a statement referencing: - Supporting materials - Code availability - Data availability - Model checkpoints
Address potential concerns in ≤1 page. Does not count toward page limit.
ACL specifically requires a Limitations section:
What to include: - Strong assumptions made - Scope limitations - When method may fail - Generalization concerns
Important: The Limitations section does NOT count toward the page limit.
If applicable: - [ ] Language diversity addressed - [ ] Non-English languages included - [ ] Translation quality verified
If applicable: - [ ] Annotator details provided - [ ] Agreement metrics reported - [ ] Compensation documented
AAAI enforces formatting rules more strictly than any other major venue. Papers that deviate from the template are desk-rejected.
\setlength, no \vspace hacks, no font overridesaaai2026.bst)COLM (Conference on Language Modeling) focuses specifically on language model research. Framing must target this community.
| Conference | Main Content | References | Appendix |
|---|---|---|---|
| NeurIPS 2025 | 9 pages | Unlimited | Unlimited (checklist separate) |
| ICML 2026 | 8 pages (+1 camera) | Unlimited | Unlimited |
| ICLR 2026 | 9 pages (+1 camera) | Unlimited | Unlimited |
| ACL 2025 | 8 pages (long) | Unlimited | Unlimited |
| AAAI 2026 | 7 pages (+1 camera) | Unlimited | Unlimited |
| COLM 2025 | 9 pages (+1 camera) | Unlimited | Unlimited |
All conference templates are in the templates/ directory:
templates/
├── icml2026/ # ICML 2026 official
├── iclr2026/ # ICLR 2026 official
├── neurips2025/ # NeurIPS 2025
├── acl/ # ACL style files
├── aaai2026/ # AAAI 2026
└── colm2025/ # COLM 2025