Automated Artifact Evaluation remains a labor-intensive bottleneck in computer science conferences. This Master’s thesis addresses this issue by developing an LLM-driven pipeline to automate reproducibility testing, reduce reviewer workload, and enable high-impact interdisciplinary research.