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iclr-experiments
Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
ICLR-Skills/skills/iclr-experiments/SKILL.md
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# ICLR Experiments
Use this before submission or during a revision pass to stress-test empirical claims. ICLR
experiments should answer the scientific question, not merely assemble a leaderboard.
## Experiment audit
- Match each experiment to a claim in the introduction.
- Compare against current strong baselines, open-source systems, and the most relevant recent
OpenReview/arXiv papers.
- Add ablations that isolate one mechanism at a time.
- Report variance across seeds or runs when randomness can change conclusions.
- Include robustness checks for dataset shift, prompt changes, architecture variants, hyperparameter
sensitivity, or compute scale when those affect the claim.