{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp04_retrieval_first_paired20_20260802","cell_id":"layer_decay_and_expiration_management__computer_science","hypothesis_id":"H3","search_queries":["MLOps archived model artifacts periodic restore testing disaster recovery","machine learning model registry backup restore drill lineage artifacts","historical ML pipeline reproducibility execute archived environment dataset model","NIST backup restoration testing periodically sample backups","patent machine learning model lineage archive restore reproducibility","MLOps automated disaster recovery test model registry artifacts dataset environment","DVC reproduce historical pipeline data model official documentation","Veeam SureBackup automated restore verification application testing official"],"sources":[{"source_id":"C1","title":"Security and Privacy Controls for Information Systems and Organizations (NIST SP 800-53 Rev. 5.1)","publisher":"National Institute of Standards and Technology","url":"https://csrc.nist.gov/CSRC/media/Projects/risk-management/800-53%20Downloads/800-53r5/SP_800-53_v5_1-derived-OSCAL.pdf","source_class":"STANDARD","claims_supported":["Control CP-9(2) calls for restoring a sample of backup information during contingency testing to verify that selected system functions operate correctly.","CP-10 requires recovery to a known operational state within an organization-defined restoration time."]},{"source_id":"C2","title":"Security Guidelines for Storage Infrastructure (NIST SP 800-209)","publisher":"National Institute of Standards and Technology","url":"https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-209.pdf","source_class":"STANDARD","claims_supported":["NIST recommends periodic restore testing and, for strict restoration-speed requirements, complete end-to-end recovery in a sandbox simulating real restoration.","The guidance also calls for recovery catalogs, audit trails, restoration-speed planning, and refreshing old or unsupported media."]},{"source_id":"C3","title":"Using SureBackup","publisher":"Veeam","url":"https://helpcenter.veeam.com/docs/vbr/userguide/recovery_verification_surebackup_job.html","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["SureBackup recovery-verification jobs can run automatically on a schedule.","Its full-recoverability mode starts machines from backups in an isolated environment and tests live applications, explicitly going beyond integrity-only verification."]},{"source_id":"C4","title":"Model Store Back Up and Retention","publisher":"Oracle Cloud Infrastructure","url":"https://docs.oracle.com/en-us/iaas/Content/data-science/using/model-store-backup-retention.htm","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["OCI implements age-based archival and deletion of model artifacts to reduce storage use, plus cross-region model backup.","An archived model artifact must be restored before it can be downloaded, demonstrating an ML-specific archive-and-restore lifecycle but not a recurring reconstruction drill."]},{"source_id":"C5","title":"Using DVC Commands","publisher":"Data Version Control","url":"https://doc.dvc.org/command-reference","source_class":"OFFICIAL_PRODUCT_DOCUMENTATION","claims_supported":["DVC tracks datasets and model-related data artifacts, codifies processing pipelines, and supports remote artifact storage.","Its documentation states that users can execute or restore any pipeline version with dvc repro, supplying an ML-specific historical reconstruction capability."]},{"source_id":"C6","title":"Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility","publisher":"arXiv / Proceedings of the ACM on Software Engineering","url":"https://arxiv.org/abs/2602.07195","source_class":"PRIMARY_RESEARCH","claims_supported":["The current paper version reports that only 26% of 12,106 historical ML notebooks remained reproducible, providing direct support for environment-erosion failures.","The study detects failures through actual execution and shows that dependency backporting alone can introduce further failure modes."]},{"source_id":"C7","title":"Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles","publisher":"arXiv","url":"https://arxiv.org/abs/2006.12117","source_class":"PRIMARY_RESEARCH","claims_supported":["End-to-end ML reproducibility depends on provenance and versioning beyond model weights, including code, datasets, packages, hyperparameters, data selection, and preprocessing.","The paper documents missing and outdated pipeline components as barriers to reproduction."]},{"source_id":"C8","title":"WO2024173449A1 — Machine learning model lineage tracking","publisher":"Google Patents","url":"https://patents.google.com/patent/WO2024173449A1/en","source_class":"PRIMARY_RESEARCH","claims_supported":["The patent discloses ML lineage graphs containing provenance and versioning edges plus creation functions capable of recreating models from parent checkpoints.","It also discloses automatically applying tests across lineage-connected model nodes and storage optimization of the lineage graph, but not periodic archive restoration stratified by bundle age and format."]}],"proximity":"SUBSTANTIAL_COLLISION","closest_analogues":[{"name":"NIST sampled, periodic end-to-end restoration controls","similarity":"Very high at the causal-mechanism level: periodic sampling, actual restoration rather than checksum inspection, sandbox recovery, operational-function validation, audit evidence, and time-bounded recovery are already standards-backed controls.","remaining_difference":"The standards are system- and storage-general and do not prescribe sampling complete ML release bundles jointly across age and model, data, feature, and runtime-format strata.","source_ids":["C1","C2"]},{"name":"Veeam SureBackup automated live recovery verification","similarity":"High: it schedules restores, starts recovered workloads in isolation, supplies dependencies through application groups, and tests live behavior rather than merely checking backup integrity.","remaining_difference":"It targets machine or application backups rather than lineage-resolved ML bundles and does not disclose age-and-format-stratified sampling or ML behavioral reproduction criteria.","source_ids":["C3"]},{"name":"OCI Model Store lifecycle combined with DVC historical pipeline reproduction","similarity":"High component overlap: age-based model archival, explicit restoration, versioned datasets and pipelines, remote artifact storage, and execution of historical pipeline versions.","remaining_difference":"The located documentation does not combine these functions into recurring sampled drills with reconstruction-time objectives and dataset, feature, environment, and model reconnection checks.","source_ids":["C4","C5"]},{"name":"Execution-based ML reproducibility and lineage systems","similarity":"Moderate to high: research validates historical ML artifacts by executing them and documents environment erosion, while the patent links model versions, provenance, creation functions, automated tests, and storage optimization.","remaining_difference":"These sources address reproducibility repair or lineage-graph operations rather than a retention-governance process that periodically restores archived release bundles across age and format strata.","source_ids":["C6","C7","C8"]}],"overlapping_components":["periodic restore testing","sampled restoration rather than universal restoration","end-to-end recovery in an isolated sandbox","live functional testing beyond checksums","time-bounded recovery objectives","age-based model archival and lower-cost retention","historical pipeline and dataset version restoration","multi-artifact ML provenance and lineage","environment-drift detection through execution","creation functions for reconstructing lineage-connected models","recovery catalogs and audit trails"],"remaining_contrastive_claim":"The bounded search did not locate a recurring operational control that samples complete release-specific ML lineage bundles jointly across age and artifact-format strata and verifies retrieval, parsing, environment creation, dataset and feature reconnection, and behavioral reproduction within a defined restoration time.","claim_falsifier":"A preexisting product manual, deployed-practice report, patent, standard profile, or research system showing scheduled or recurring age-and-format-stratified restoration of complete ML release bundles—with model, data, feature, code, and environment reconnection plus measured reconstruction success and duration—would falsify the remaining distinction.","problem_support":"STRONG","recommendation":"DIFFUSION_LANE","world_novelty_boundary":"Periodic sampled end-to-end restore testing, sandbox recovery, restoration-time objectives, ML artifact lifecycle management, historical pipeline reproduction, and lineage-driven testing are already disclosed separately and partly in combination. The only boundary not found in this eight-query, eight-source review is their ML-specific orchestration around complete release bundles and joint age/format stratification; therefore the treatment is best framed as a targeted transfer and operationalization of established recovery practice, not as a novel recovery mechanism."}