Experiment Tracking and Reproducibility Questions

Focuses on the tools, processes, and engineering practices that ensure experiments can be reproduced, audited, and compared over time. Areas include systematic logging of hyperparameters and results, experiment metadata and registries, code and model version control, dataset versioning and provenance, environment and dependency capture, artifact and checkpoint management, deterministic training practices and random seed handling, automation of experiment pipelines, and integration with continuous integration systems. Candidates should be able to discuss common reproducibility pitfalls, strategies for enabling large scale experiment comparison and analysis, and how experiment artifacts support knowledge reuse and evidence based decision making.