{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KA2V4BWX4MMZGHKCMI6QFC3XFI","short_pith_number":"pith:KA2V4BWX","canonical_record":{"source":{"id":"2606.29582","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-28T19:54:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ffeffd861530680f31c0a632e72a8672da42682627dc211a3f9515d34438fc4c","abstract_canon_sha256":"470eca9ea47dc5ef03f5b95ae81a0012af756a0872393074626aca260ce2785f"},"schema_version":"1.0"},"canonical_sha256":"50355e06d7e319931d42623d028b772a2031eadb9048b54bc378ebb318605709","source":{"kind":"arxiv","id":"2606.29582","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29582","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29582v1","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29582","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_12","alias_value":"KA2V4BWX4MMZ","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_16","alias_value":"KA2V4BWX4MMZGHKC","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_8","alias_value":"KA2V4BWX","created_at":"2026-06-30T01:18:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KA2V4BWX4MMZGHKCMI6QFC3XFI","target":"record","payload":{"canonical_record":{"source":{"id":"2606.29582","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-28T19:54:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ffeffd861530680f31c0a632e72a8672da42682627dc211a3f9515d34438fc4c","abstract_canon_sha256":"470eca9ea47dc5ef03f5b95ae81a0012af756a0872393074626aca260ce2785f"},"schema_version":"1.0"},"canonical_sha256":"50355e06d7e319931d42623d028b772a2031eadb9048b54bc378ebb318605709","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:18:13.000825Z","signature_b64":"BhJGYPo1Fpk98ztfGq491UKrWcMziOVNjRE/ZEiO4C77byolYCUnob2hDcJ6/arszSefcQBYGvf/Nq8bz6p7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"50355e06d7e319931d42623d028b772a2031eadb9048b54bc378ebb318605709","last_reissued_at":"2026-06-30T01:18:13.000002Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:18:13.000002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.29582","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:18:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4cQCwqQrXRYHp6g/235XwW+Kwg9EMyIk7gxiQXaLIFx8o/c4YmEIIfKbq6unXPHKYeYQDVROY0ic8uVrt3IiBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T08:15:06.580799Z"},"content_sha256":"60f92a24819743040633db13df333d6c3395b44527803519cbd82325e1861b61","schema_version":"1.0","event_id":"sha256:60f92a24819743040633db13df333d6c3395b44527803519cbd82325e1861b61"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KA2V4BWX4MMZGHKCMI6QFC3XFI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bilevel Optimization for Neural Architecture Search","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Abhishek Shukla, Ankur Sinha, Faiz Hamid","submitted_at":"2026-06-28T19:54:20Z","abstract_excerpt":"Bilevel optimization has become an influential and widely adopted framework for addressing hierarchical optimization problems in machine learning, providing an effective approach to modeling the interaction between two levels of optimization, with applications such as hyperparameter tuning, meta-learning, adversarial training, and data poisoning. Neural Architecture Search (NAS), a subfield of hyperparameter optimization, is a prime example of a bilevel optimization problem, with architecture parameters optimized at the outer-level and network weights optimized at the inner level. This paper p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29582","kind":"arxiv","version":1},"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/2606.29582/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T01:18:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oyqjyQVfD2t30K9L9C5pjAWT7ZdsQ5XJrmJtTfa/+sY2WO30x8ox/uWvwknlDTKdgwN2KEqKlWUT2fUZ6P2PDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T08:15:06.581180Z"},"content_sha256":"3accbe12ecfad12c4ea83d40cf29e732ca26d4e0ac89bd454fa1a50597b80427","schema_version":"1.0","event_id":"sha256:3accbe12ecfad12c4ea83d40cf29e732ca26d4e0ac89bd454fa1a50597b80427"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/bundle.json","state_url":"https://pith.science/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-02T08:15:06Z","links":{"resolver":"https://pith.science/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI","bundle":"https://pith.science/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/bundle.json","state":"https://pith.science/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KA2V4BWX4MMZGHKCMI6QFC3XFI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KA2V4BWX4MMZGHKCMI6QFC3XFI","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":"470eca9ea47dc5ef03f5b95ae81a0012af756a0872393074626aca260ce2785f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-28T19:54:20Z","title_canon_sha256":"ffeffd861530680f31c0a632e72a8672da42682627dc211a3f9515d34438fc4c"},"schema_version":"1.0","source":{"id":"2606.29582","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29582","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29582v1","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29582","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_12","alias_value":"KA2V4BWX4MMZ","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_16","alias_value":"KA2V4BWX4MMZGHKC","created_at":"2026-06-30T01:18:13Z"},{"alias_kind":"pith_short_8","alias_value":"KA2V4BWX","created_at":"2026-06-30T01:18:13Z"}],"graph_snapshots":[{"event_id":"sha256:3accbe12ecfad12c4ea83d40cf29e732ca26d4e0ac89bd454fa1a50597b80427","target":"graph","created_at":"2026-06-30T01:18:13Z","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/2606.29582/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bilevel optimization has become an influential and widely adopted framework for addressing hierarchical optimization problems in machine learning, providing an effective approach to modeling the interaction between two levels of optimization, with applications such as hyperparameter tuning, meta-learning, adversarial training, and data poisoning. Neural Architecture Search (NAS), a subfield of hyperparameter optimization, is a prime example of a bilevel optimization problem, with architecture parameters optimized at the outer-level and network weights optimized at the inner level. This paper p","authors_text":"Abhishek Shukla, Ankur Sinha, Faiz Hamid","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-28T19:54:20Z","title":"Bilevel Optimization for Neural Architecture Search"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29582","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":"sha256:60f92a24819743040633db13df333d6c3395b44527803519cbd82325e1861b61","target":"record","created_at":"2026-06-30T01:18:13Z","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":"470eca9ea47dc5ef03f5b95ae81a0012af756a0872393074626aca260ce2785f","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-28T19:54:20Z","title_canon_sha256":"ffeffd861530680f31c0a632e72a8672da42682627dc211a3f9515d34438fc4c"},"schema_version":"1.0","source":{"id":"2606.29582","kind":"arxiv","version":1}},"canonical_sha256":"50355e06d7e319931d42623d028b772a2031eadb9048b54bc378ebb318605709","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"50355e06d7e319931d42623d028b772a2031eadb9048b54bc378ebb318605709","first_computed_at":"2026-06-30T01:18:13.000002Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:18:13.000002Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BhJGYPo1Fpk98ztfGq491UKrWcMziOVNjRE/ZEiO4C77byolYCUnob2hDcJ6/arszSefcQBYGvf/Nq8bz6p7DA==","signature_status":"signed_v1","signed_at":"2026-06-30T01:18:13.000825Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29582","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60f92a24819743040633db13df333d6c3395b44527803519cbd82325e1861b61","sha256:3accbe12ecfad12c4ea83d40cf29e732ca26d4e0ac89bd454fa1a50597b80427"],"state_sha256":"ee19366367de13cdb01b93259db563ce48a56dff377d1ac9fb33318a8c379b0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+2Npknyn/adzsygVVYd/rWez7DHF43jkC+544T2EDFlysACW53o0d/WufjeTwowG3B3lCpktxpoUvlOn9aE5AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T08:15:06.583311Z","bundle_sha256":"b9e43fe5133883fecc1911b900280541ccb5efedc1fefa7b7e77ff807b8c3803"}}