However, it is imperative to view these findings not as a critique of MIST but rather as a catalyst for broader community engagement in the development and benchmarking of computational tools. The initiative by Goldman et al. [2] to release their training datasets, cross-validation splits, and model configurations sets a commendable precedent for transparency and reproducibility in computational biology and machine learning. This open sharing of resources is crucial for fostering a community-wide effort towards robust benchmarking standards.