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Get Started Free →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.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-14 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-01 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-02 | ✗→✓ | ▲ Improved | -25% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -22% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 1% | 0% |
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.
expectations.
evidence, scope, and contribution.
Use this structure:
textClosest 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>.
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 |
and name the specific setting or evidence you add; do not bury or ignore it.
substantially similar work is under review elsewhere, satisfying the dual-submission rule.
AI systems as citable scientific sources and hallucinated citations are a credibility risk.
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.
text[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>
Other measured skills in the registry, with their headline benchmark lift.