{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:3GE7IH2CFFGVUG5D4VGPFUI3EI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"53ae63a60ffb33fcec136b3918f2de6932d943e19ded39ec159b0e8774481b26","cross_cats_sorted":["cs.CV","math.DS","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-16T06:58:12Z","title_canon_sha256":"cc75b71799afbe609e68f9367bdf5568d48e6d53358916949b815c92a7117e08"},"schema_version":"1.0","source":{"id":"2102.07976","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2102.07976","created_at":"2026-07-05T03:44:51Z"},{"alias_kind":"arxiv_version","alias_value":"2102.07976v3","created_at":"2026-07-05T03:44:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.07976","created_at":"2026-07-05T03:44:51Z"},{"alias_kind":"pith_short_12","alias_value":"3GE7IH2CFFGV","created_at":"2026-07-05T03:44:51Z"},{"alias_kind":"pith_short_16","alias_value":"3GE7IH2CFFGVUG5D","created_at":"2026-07-05T03:44:51Z"},{"alias_kind":"pith_short_8","alias_value":"3GE7IH2C","created_at":"2026-07-05T03:44:51Z"}],"graph_snapshots":[{"event_id":"sha256:8e7c3f0de4525208119c2dd4c38eccc2c1e12123ee5d8bfee70034ee58974642","target":"graph","created_at":"2026-07-05T03:44:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2102.07976/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In recent years, a variety of gradient-based methods have been developed to solve Bi-Level Optimization (BLO) problems in machine learning and computer vision areas. However, the theoretical correctness and practical effectiveness of these existing approaches always rely on some restrictive conditions (e.g., Lower-Level Singleton, LLS), which could hardly be satisfied in real-world applications. Moreover, previous literature only proves theoretical results based on their specific iteration strategies, thus lack a general recipe to uniformly analyze the convergence behaviors of different gradie","authors_text":"Jin Zhang, Pan Mu, Risheng Liu, Shangzhi Zeng, Xiaoming Yuan","cross_cats":["cs.CV","math.DS","math.OC"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-16T06:58:12Z","title":"A General Descent Aggregation Framework for Gradient-based Bi-level Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.07976","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1f7ce1ec148a73c399573255abecda560726bbaeac152a1c6c41e90a12daf884","target":"record","created_at":"2026-07-05T03:44:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"53ae63a60ffb33fcec136b3918f2de6932d943e19ded39ec159b0e8774481b26","cross_cats_sorted":["cs.CV","math.DS","math.OC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-02-16T06:58:12Z","title_canon_sha256":"cc75b71799afbe609e68f9367bdf5568d48e6d53358916949b815c92a7117e08"},"schema_version":"1.0","source":{"id":"2102.07976","kind":"arxiv","version":3}},"canonical_sha256":"d989f41f42294d5a1ba3e54cf2d11b2239a4810866f571bb88ed1768fa2d940a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d989f41f42294d5a1ba3e54cf2d11b2239a4810866f571bb88ed1768fa2d940a","first_computed_at":"2026-07-05T03:44:51.877925Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:44:51.877925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3QLo2/acsJcLx9bECgD8gjNy+lTDm81i9Fm6rVfc2vOyKdbYkzCTucAuZG93FhNtdDlgr7aYIGpgwskK/4TSBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:44:51.878348Z","signed_message":"canonical_sha256_bytes"},"source_id":"2102.07976","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1f7ce1ec148a73c399573255abecda560726bbaeac152a1c6c41e90a12daf884","sha256:8e7c3f0de4525208119c2dd4c38eccc2c1e12123ee5d8bfee70034ee58974642"],"state_sha256":"dec654eeec8829744563ce8f4a20cfdd98d33e1b11f27286b305e90598f7aaac"}