Author-in-the-loop Agentic Reproducibility Auditing for a Machine Learning Conference
arXiv:2610.22465v1 Announce Type: new Abstract: Reproducibility checks during peer review require a shared understanding of what authors' code is expected to reproduce. We present an author-in-the-loop collaborative audit that makes author declarations explicit inputs to an AI agent's investigation and assessment. A structured checklist records reproduction instructions and limitations; a two-stage workflow on GitHub connects agent checks to author clarification, revision, and agent reassessment. The agent separately assesses whether results have been reproduced and whether authors need to pro
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本站仅作技术动态聚合，不构成任何形式的建议。