{"schema_version":"papers/paper-detail-v1","title":"Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models","surface":"papers","opportunity_kernel":{"paper_id":"e1e62d40-023c-4504-966e-d374764f6667","title":"Rethinking LoRA for Privacy-Preserving Federated Learning in Large Models","authors":[],"arxiv_id":"2602.19926v1","doi":null,"published_at":"2026-02-23T15:05:28.000Z","score_object":{"overall":{"value":6,"scale":"0-10","confidence":0.85,"confidence_reason":"Backfilled from persisted papers.viability_score.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":1,"missingness":[]},"technical":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted technical score source was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["reproducibility_results.reproducibility_score","deployability_scores.score","paper_extraction_scorecards.reconstruction_score"]},"commercial":{"value":0,"scale":"0-10","confidence":0.75,"confidence_reason":"Backfilled from persisted commercial_flags and repo availability.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":1,"missingness":[]},"market":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted distribution_readiness_scores row was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["distribution_readiness_scores.score"]},"team":{"value":10,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted team-quality evidence was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-04-16T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["engineer_profiles.builder_score","author_startups"]},"methodology":{"value":0,"scale":"0-10","confidence":0.15,"confidence_reason":"No persisted methodology score source was available; marked low confidence.","model_version":"phase0-backfill-v1","pipeline_version":"phase0-kernel-v1","computed_at":"2026-04-02T02:30:40.136Z","fresh_until":"2026-05-02T02:30:40.136Z","is_stale":true,"source_count":0,"missingness":["paper_extraction_scorecards.total_score","paper_extraction_scorecards.standard_extraction_score"]}},"evidence_receipt":{"freshness":"stale","proof_status":"unverified","repo_status":"missing","references_count":0,"source_count":0,"coverage":0.1667,"missingness":["repo_url","references","proof_status","distribution_readiness_scores","paper_extraction_scorecards"],"unresolved_unknowns":["distribution readiness has not been computed yet","proof verification has not been recorded yet"],"last_verification_at":"2026-04-02T02:30:40.136Z"},"lineage_hash":"e15b46de26780c33e2cdbd790c3495f951bb57d7ce704263678463690db91764"},"distribution":null,"replication_evidence":[],"author_dna":[]}