Skills Artificial Intelligence Positioning Novelty in AAAI Related Work

Positioning Novelty in AAAI Related Work

v20260724
aaai-related-work
This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.
Get Skill
417 downloads
Overview

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>
Info
Name aaai-related-work
Version v20260724
Size 3.54KB
Updated At 2026-07-28
Language