{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E6TI4X5RAYRAMDSCSJZH7SXXIA","short_pith_number":"pith:E6TI4X5R","schema_version":"1.0","canonical_sha256":"27a68e5fb10622060e4292727fcaf740356940f06cbab2f38d3180de32fc7a93","source":{"kind":"arxiv","id":"2408.16087","version":2},"attestation_state":"computed","paper":{"title":"Unlocking Global Optimality in Bilevel Optimization: A Pilot Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Quan Xiao, Tianyi Chen","submitted_at":"2024-08-28T18:34:54Z","abstract_excerpt":"Bilevel optimization has witnessed a resurgence of interest, driven by its critical role in trustworthy and efficient AI applications. While many recent works have established convergence to stationary points or local minima, obtaining the global optimum of bilevel optimization remains an important yet open problem. The difficulty lies in the fact that, unlike many prior non-convex single-level problems, bilevel problems often do not admit a benign landscape, and may indeed have multiple spurious local solutions. Nevertheless, attaining global optimality is indispensable for ensuring reliabili"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2408.16087","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2024-08-28T18:34:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"737153d62c5f8b91db87ce7699af660702406b04fc157b2796835445a309fea3","abstract_canon_sha256":"c5c6670f7f309c17e41948c56aba54487da6b5ba19f4300bfe4809c7d245c726"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:53:43.011679Z","signature_b64":"xteh1/ZLBWHHe1qFcpzW94Hz4S0iNxAhbp7m9bITX95JVY4s9jUnRx0g4j7oFjnaL3B4d4JSwv68VwumZzXMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27a68e5fb10622060e4292727fcaf740356940f06cbab2f38d3180de32fc7a93","last_reissued_at":"2026-07-05T09:53:43.011248Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:53:43.011248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unlocking Global Optimality in Bilevel Optimization: A Pilot Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Quan Xiao, Tianyi Chen","submitted_at":"2024-08-28T18:34:54Z","abstract_excerpt":"Bilevel optimization has witnessed a resurgence of interest, driven by its critical role in trustworthy and efficient AI applications. While many recent works have established convergence to stationary points or local minima, obtaining the global optimum of bilevel optimization remains an important yet open problem. The difficulty lies in the fact that, unlike many prior non-convex single-level problems, bilevel problems often do not admit a benign landscape, and may indeed have multiple spurious local solutions. Nevertheless, attaining global optimality is indispensable for ensuring reliabili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.16087","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2408.16087/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2408.16087","created_at":"2026-07-05T09:53:43.011306+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.16087v2","created_at":"2026-07-05T09:53:43.011306+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.16087","created_at":"2026-07-05T09:53:43.011306+00:00"},{"alias_kind":"pith_short_12","alias_value":"E6TI4X5RAYRA","created_at":"2026-07-05T09:53:43.011306+00:00"},{"alias_kind":"pith_short_16","alias_value":"E6TI4X5RAYRAMDSC","created_at":"2026-07-05T09:53:43.011306+00:00"},{"alias_kind":"pith_short_8","alias_value":"E6TI4X5R","created_at":"2026-07-05T09:53:43.011306+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2601.16399","citing_title":"A Hessian-Free Actor-Critic Algorithm for Bi-Level Reinforcement Learning with Applications to LLM Fine-Tuning","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA","json":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA.json","graph_json":"https://pith.science/api/pith-number/E6TI4X5RAYRAMDSCSJZH7SXXIA/graph.json","events_json":"https://pith.science/api/pith-number/E6TI4X5RAYRAMDSCSJZH7SXXIA/events.json","paper":"https://pith.science/paper/E6TI4X5R"},"agent_actions":{"view_html":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA","download_json":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA.json","view_paper":"https://pith.science/paper/E6TI4X5R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.16087&json=true","fetch_graph":"https://pith.science/api/pith-number/E6TI4X5RAYRAMDSCSJZH7SXXIA/graph.json","fetch_events":"https://pith.science/api/pith-number/E6TI4X5RAYRAMDSCSJZH7SXXIA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA/action/storage_attestation","attest_author":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA/action/author_attestation","sign_citation":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA/action/citation_signature","submit_replication":"https://pith.science/pith/E6TI4X5RAYRAMDSCSJZH7SXXIA/action/replication_record"}},"created_at":"2026-07-05T09:53:43.011306+00:00","updated_at":"2026-07-05T09:53:43.011306+00:00"}