Install in seconds
Install this skill
Copy the command and run it in your terminal. You can review the source before installing.
terminal
git clone https://github.com/brycewang-stanford/Awesome-Journal-Skills

Works with Git. The repository opens in your current directory.

📝
WritingStata

AAAI Related Work

by brycewang-stanford

Helps craft AAAI papers' related-work sections that clearly distinguish novelty from archival and contemporaneous work, addressing reviewer expectations across AAAI's broad AI scope and adhering to dual-submission and citation policies.

880 stars106 forksAdded 2026/07/20
academic-researchacademic-writingagent-skillsai-agentsanthropicawesome-listcausal-inferenceclaudeclaude-codeeconometricseconomicsempirical-researchfinancejournalllmmcppeer-reviewreplicationresearch-toolsscholarly-publishing

Documentation

README

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>