Position: Machine Learning Conferences Should Introduce an Autonomous Research Track

Autonomous machine learning research is moving from a speculative possibility to an emerging research practice. Yet, current conference policies are not equipped to handle this change. This position paper argues that machine learning conferences should introduce an Autonomous Research Track where an autonomous AI system controls claim-shaping decisions. However, to ensure that conferences continue to exist for both science and scientists, our proposal anchors this new track in human judgment and participation. First, we propose a hierarchy of AI involvement from incidental AI use to autonomous research, grounded in the CRediT taxonomy of research contributions. In keeping with current conventions, we assert that authorship remain exclusively human while recognizing that the role of author may shift to one of curation of autonomously generated research. We propose a novel review format that separates verification and adjudication: human authors submit a technical review to guarantee accountability, an AI-generated review provides a critical baseline, and a pair of human reviewers evaluate the work's technical correctness and significance. We then propose a discussant-style conference format, where both a human author and a human reviewer present the accepted research. This design assigns visible credit to human participants for thoughtful evaluation and judgment, which is essential for both scientific excellence and maintaining a sense of community.