{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OPVCDKGJZ3V4ADKCV3QCJZ65MD","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":"8b1355355ca1a5e80f6c6b829071277c42073cb3d7cb9fecc9d54bc3badb708e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-20T16:07:55Z","title_canon_sha256":"1c384c1aa9724458cf25197a2f93caa15dd555ed594250c4e5f5830261991ebd"},"schema_version":"1.0","source":{"id":"2605.21341","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.21341","created_at":"2026-05-21T02:05:30Z"},{"alias_kind":"arxiv_version","alias_value":"2605.21341v1","created_at":"2026-05-21T02:05:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21341","created_at":"2026-05-21T02:05:30Z"},{"alias_kind":"pith_short_12","alias_value":"OPVCDKGJZ3V4","created_at":"2026-05-21T02:05:30Z"},{"alias_kind":"pith_short_16","alias_value":"OPVCDKGJZ3V4ADKC","created_at":"2026-05-21T02:05:30Z"},{"alias_kind":"pith_short_8","alias_value":"OPVCDKGJ","created_at":"2026-05-21T02:05:30Z"}],"graph_snapshots":[{"event_id":"sha256:8b18038d8badae704a87f26183c01d10838cc76dc5eb6d75c86467f4c58f5689","target":"graph","created_at":"2026-05-21T02:05:30Z","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/2605.21341/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Functional bilevel methods estimate a lower-level function and plug it into a hypergradient, but this plug-in gradient can retain first-order bias when the lower-level problem is learned nonparametrically. To remove this bias, we develop a semiparametric debiasing theory for population bilevel gradients based on the efficient influence function. This perspective leads to a cross-fitted orthogonal hypergradient estimator for which we establish asymptotic normality together with uniform control over the outer parameter. Under quadratic losses, the estimator reduces to a simple doubly robust scor","authors_text":"Aur\\'elien Bibaut, Fares El Khoury, Houssam Zenati, Michael Arbel, Nathan Kallus","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-20T16:07:55Z","title":"Semiparametric Efficient Bilevel Gradient Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21341","kind":"arxiv","version":1},"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":"sha256:98513608cb26286c18c76ad4dc0d8e0dee7caae6089b6ed8aff54e8e30a32feb","target":"record","created_at":"2026-05-21T02:05:30Z","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":"8b1355355ca1a5e80f6c6b829071277c42073cb3d7cb9fecc9d54bc3badb708e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-20T16:07:55Z","title_canon_sha256":"1c384c1aa9724458cf25197a2f93caa15dd555ed594250c4e5f5830261991ebd"},"schema_version":"1.0","source":{"id":"2605.21341","kind":"arxiv","version":1}},"canonical_sha256":"73ea21a8c9ceebc00d42aee024e7dd60cad4d781f2450ef0ad418e9fdc5990fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"73ea21a8c9ceebc00d42aee024e7dd60cad4d781f2450ef0ad418e9fdc5990fc","first_computed_at":"2026-05-21T02:05:30.067370Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T02:05:30.067370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eSHkkuj74Ve6N3YWUk+olTi6V4k7nS7SeCxFA1qC5Pde3vxP9A6vVOSUi7gj/ozZQK205zo5VgAJdA/K7WXABQ==","signature_status":"signed_v1","signed_at":"2026-05-21T02:05:30.068054Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.21341","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98513608cb26286c18c76ad4dc0d8e0dee7caae6089b6ed8aff54e8e30a32feb","sha256:8b18038d8badae704a87f26183c01d10838cc76dc5eb6d75c86467f4c58f5689"],"state_sha256":"24e7b5c292b38d0ff998fd1714f7c1b0c7310ed93b3ef2e2f2b1abf0e1b7b736"}