How much changes can you make to a paper between acceptance and camera ready?

Editorial analysis

The research findings in how much changes can you make to a paper between acceptance and camera ready? contribute to the growing body of empirical evidence on AI system behavior at scale. We have a paper accepted to NeurIPS, but at the same time we were working on a resubmission to ICLR just in case NeurIPS rejected us. There has been substantial rewriting, and we feel it would be... The methodological rigor—controlled experiments with statistical significance testing—sets a standard for production AI evaluation. Key takeaways for practitioners: the reported metrics should be validated against your specific data distribution before architectural decisions, and the failure modes identified often generalize across model families. The open-sourcing of evaluation benchmarks and datasets enables reproducible comparison, which is essential for the field's progress beyond leaderboard chasing. Teams should incorporate these findings into their evaluation pipelines and consider contributing their own experimental results back to the community.