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neurips-topic-selection

neurips-topic-selection

Use when deciding whether a paper belongs at NeurIPS, choosing main-track versus another NeurIPS track, selecting contribution type, or rerouting to a better AI/ML venue.

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name: neurips-topic-selection description: Use when deciding whether a paper belongs at NeurIPS, choosing main-track versus another NeurIPS track, selecting contribution type, or rerouting to a better AI/ML venue.

NeurIPS Topic Selection

Use this skill before committing to NeurIPS. The target is not "any good AI paper"; it is a paper whose contribution will matter to the NeurIPS reviewer community and survive the current official track rules.

Fit signals

Strong NeurIPS candidates usually have one of these cores:

  • a general ML method, model, objective, optimization, inference, or learning principle;
  • a theory result that changes understanding of ML behavior or limits;
  • a high-quality empirical finding about models, data, evaluation, robustness, or scaling;
  • a use-inspired result with a real scientific, social, health, robotics, sustainability, or creative-AI problem and a clear ML contribution;
  • a dataset, benchmark, or evaluation contribution that belongs in the correct current NeurIPS track rather than being forced into main track;
  • a rigorous negative result that changes community understanding.

Poor fit signals

  • Engineering integration without a research insight.
  • Domain application where the ML contribution is ordinary.
  • Benchmark improvement without mechanism, error analysis, or generality.
  • Safety, fairness, or societal claim with thin evidence.
  • Reproduction or replication study better suited to MLRC/TMLR.
  • Dataset or evaluation paper that should use the E&D track.
  • Position argument that should use the Position Papers track.

Contribution-type choice

Choose the contribution type that changes reviewer expectations. A theory paper should make proofs central. A use-inspired paper needs a real task and ML novelty. A concept-and-feasibility paper needs high-risk/high-reward framing and credible preliminary evidence. A negative-results paper needs a lesson that matters beyond one failed run.

Output format

[Fit] High / Medium / Low
[Recommended track] Main / E&D / Position / MLRC / workshop / other venue
[Contribution type] General / Theory / Use-Inspired / Concept & Feasibility / Negative Results
[Why NeurIPS] <one sentence>
[Main upgrade needed] <evidence, framing, related work, artifact, ethics, or reroute>
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