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ijcai-experiments
Use when designing or auditing IJCAI or IJCAI-ECAI experiments, baselines, ablations, statistical evidence, hyperparameter reporting, compute descriptions, dataset handling, ethics risks, and reproducibility evidence for AI papers.
IJCAI-Skills/skills/ijcai-experiments/SKILL.md
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# IJCAI Experiments
Use this before submission when the experimental story is not yet locked. IJCAI reviewers
can score novelty, correctness, clarity, significance, impact, presentation, ethics, and
reproducibility.
## Experiment audit
- Map each major claim to a table, figure, theorem, ablation, proof, or qualitative analysis.
- Include strong, current, and properly tuned baselines; explain any missing baseline before
reviewers ask.
- Report dataset splits, preprocessing, metrics, search ranges, final hyperparameters,
selection criteria, random seeds or repeats, and compute infrastructure.
- Add ablations for the core mechanism, not just peripheral architecture choices.