Due to the significant computational requirements of processing the QASPER dataset (1,500+ scientific papers) and the ephemeral nature of Google Colab’s local storage, users will notice the following regarding the submitted logs:
Log Persistence: Initial experimentation logs (rapidfire.log and training.log) were generated during active development but were lost when the Colab temporary runtime was recycled.
Resume Logic: To finalize the submission under hardware constraints (GPU timeouts), the notebook was re-run using a "Drive-First" check. The current code detects pre-computed results stored in /results/ on Google Drive and skips redundant heavy processing.
Validation Log: A fresh rapidfire.log has been included in this repository. This log was generated by re-initializing the RapidFire AI environment and executing a validation pass to confirm that the existing experimental configurations and RAG specs are fully functional and properly defined.
Proof of Work: While the full historical logs for every iteration are not present, the MLflow artifacts and the final MRR/Recall data in the notebook represent the authentic outputs of the multi-phase optimization described in the summary document.