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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.
Passed 19 of 22 test cases with the skill, and 10 of 22 without it.
and 8 more in the full report
11 fixed8 passed either way1 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are submitting a paper on neural-guided constraint satisfaction to AAAI. Last year we presented an early 4-page version at a workshop, and a related paper was published at IJCAI 2024. Could you evaluate our paper's positioning and produce a summary that identifies the nearest benchmark or prior paper, the core dimension of contrast, the required references (including non-archival preprints), any potential multiple-submission risk classification, and a finalized related-work novelty paragraph ready for our submission?
| Checked | Without skill | With skill |
|---|---|---|
| Formats output using the structured tags [Closest work], [Difference axis], [Must-cite items], [Multiple-submission risk], and [Revision text]. | Missed | Passed |
| Clarifies the archival delta over the previous 4-page workshop paper to satisfy dual-submission policy. | Passed | Passed |
| Follows the four-sentence novelty paragraph structure contrasting assumptions, missing setting, contribution, and limited scope. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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>.
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 |
test cases passed, vs 10 / 22 without
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