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name: aaai-related-work description: Use when positioning an AAAI paper's novelty against archival work, contemporaneous arXiv or workshop papers, and AAAI/IJCAI/NeurIPS/ICML/ICLR neighbors across the broad AI scope, while staying inside AAAI's dual-submission and AI-as-source policy constraints and writing a related-work section legible to non-specialist reviewers.

AAAI Related Work

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>