Science progresses by iteratively advancing and correcting humanity's understanding of the world. In machine learning (ML) research, rapid advancements have led to an explosion of publications, but have also led to misleading, incorrect, flawed or perhaps even fraudulent studies being accepted and sometimes highlighted at ML conferences due to the fallibility of peer review. While such mistakes are understandable, ML conferences do not offer robust processes to help the field systematically correct when such errors are made. This position paper argues that ML conferences should establish a dedicated "Refutations and Critiques" (R&C) Track. This R&CTrack would provide a high-profile, reputable platform to support vital research that critically challenges prior research, thereby fostering a dynamic self-correcting research ecosystem. We discuss key considerations including track design, review principles, potential pitfalls, and provide an illustrative example submission concerning a recent ICLR 2025 Oral. We conclude that ML conferences should create official, reputable mechanisms to help ML research self-correct.

We thank all the reviewers for their constructive comments. We will provide details in the final draft. MCUNet shows consistent improvement across different devices (F746, H743) and tasks (classification, detection). R1: Whether the overall network topology brings major improvement. R2: Why the auto-tuning in TVM fails to work on MCUs.

Forthenotations of directions, we use the convention that both the incident and outgoing rays point away from a scattering location. Spherical Harmonics (SH) are orthonormal basis defined on complex numbersovertheunitsphere. Since they were designed for scenes with solid objects, we adapt them to cope with participating media. Our implementation of the Neural Reflectance Field [2] baseline uses the same neural network architecture and positional encoding asinthe original paper. In addition, we employ a visibility MLP [3]tocompute a1-Dvisibility anda1-Dexpected termination depth.