AAAI Related Work
SkillDev toolsYour AI can position an AAAI paper's novelty against prior work and write a clear, readable related-work section. It compares your contribution to archival publications, contemporaneous arXiv and workshop papers, and neighboring papers from AAAI, IJCAI, NeurIPS, ICML, and ICLR across the broad AI field. It also keeps the section within AAAI's dual-submission and AI-as-source policy constraints.
Available today. Use it from your connected AI after setup.
No other account needed.
After adding it, tell your AI what your paper contributes and which papers or research areas it should be compared against. Then ask it to draft or revise the related-work section.
Then ask your AI: use the AAAI Related Work skill
What your AI can do with it
- Position your paper's novelty against archival and contemporaneous work
- Compare your contribution to neighboring papers from AAAI, IJCAI, NeurIPS, ICML, and ICLR
- Cover related work across the broad AI field
- Keep the section compliant with AAAI's dual-submission rules
- Follow AAAI's policy on AI as a source
- Write a clear, readable related-work section
What this skill tells your AI
The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in AAAI-Skills/skills/aaai-related-work/SKILL.md and read by ahel’s review.
Use this to make the novelty claim robust under AAAI's broad AI review. The related-work section must help reviewers distinguish the paper from both archival work and contemporaneous non-archival work.
Positioning checks
- Identify the closest archival AI papers and current arXiv/workshop work.
- Separate method novelty, task novelty, evaluation novelty, and system integration novelty.
- Cite contemporaneous non-archival work carefully when it affects priority or reviewer expectations.
- Do not submit substantially similar work to multiple archival venues at the same time.
- Explain how the paper differs from AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors in assumptions, evidence, scope, and contribution.
- Avoid using AI systems as citable scientific sources under AAAI policy.
Novelty paragraph
Use this structure:
Closest prior work solves <problem> under <assumptions>.
It does not address <specific missing setting/mechanism/evidence>.
This paper contributes <new item> and verifies it through <evidence>.
The claim is limited to <scope>.
Positioning across AAAI's breadth
AAAI spans search, planning, knowledge representation, constraint satisfaction, multi-agent systems, learning, NLP, vision, and robotics, so the closest prior work may live in a subfield your reviewer does not. Make the contrast explicit for a non-specialist instead of assuming shared background.
| Neighbor venue | Reviewer expectation | Differentiation to spell out |
|---|---|---|
| IJCAI | broad-AI overlap | what your result adds beyond their framing |
| NeurIPS/ICML | ML method or theory depth | why AAAI breadth, not just a benchmark gain |
| ICLR | representation-learning lens | non-learning mechanism or guarantee you contribute |
| AAAI prior years | incremental-track suspicion | the new assumption, evidence, or scope |
Reviewer-pushback patterns
- "This looks concurrent with arXiv paper Y." Fix: cite Y, state it is non-archival and contemporaneous, and name the specific setting or evidence you add; do not bury or ignore it.
- "Isn't this the same as your workshop paper?" Fix: clarify the archival delta and confirm no substantially similar work is under review elsewhere, satisfying the dual-submission rule.
- "Citation looks AI-generated." Fix: verify every reference against a real source; AAAI policy bars AI systems as citable scientific sources and hallucinated citations are a credibility risk.
Worked vignette
A reasoning-over-knowledge-graphs paper sits near both a KR archival line and a recent NeurIPS embedding paper. Using the axes: against KR work the difference is evidence (learned vs. hand-built rules); against the NeurIPS neighbor it is scope (logical soundness, not just link prediction). One contemporaneous arXiv preprint is cited as non-archival with a one-line delta, and the dual-submission box is checked clean.
Output format
[Closest work] <paper/system/benchmark>
[Difference axis] problem / method / theory / data / evaluation / system / impact
[Must-cite items] <archival and contemporaneous work>
[Multiple-submission risk] none / clarify / withdraw / reroute
[Revision text] <AAAI-ready related-work paragraph>
Signals
- GitHub stars
- 1k
- Forks
- 146
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
aaai-related-work- Source
- github.com/brycewang-stanford/awesome-journal-skills