AAAI Experiments

Use this before submission to ensure empirical evidence supports the AI contribution. AAAI reviewers may come from adjacent AI subfields, so experiments must be interpretable beyond one benchmark community.

Experiment audit

• Map every experimental block to a claim in the introduction.
• Compare against strong, recent, and fairly tuned baselines.
• Include ablations that isolate mechanisms rather than removing multiple components at once.
• Report uncertainty, variance, and statistical tests when small differences matter.
• Test robustness to data split, prompt, seed, environment, user population, or distribution shift when relevant.
• For human evaluation, document task, instructions, annotator pool, quality control, aggregation, and ethics/IRB status.
• Report compute, hardware, data access, model size, and training/inference cost.

Claim-to-evidence ledger